Journal articles on the topic 'Generalised AutoRegressive Conditional Heteroscedastic (GARCH)'
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Odah, Meshal Harbi. "Comparison of GARCH & ARMA Models to Forecasting Exchange Rate." Mathematical Modelling of Engineering Problems 8, no. 6 (December 22, 2021): 979–83. http://dx.doi.org/10.18280/mmep.080619.
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Financial time series are defined by their fluctuations, which are characterized by instability or uncertainty, implying that there are periods of volatility followed by periods of relative calm. Therefore, time series analysis requires homogeneity of variance. In this paper, some models used in time series analysis have been studied and applied. Comparison between Autoregressive Moving Average (ARMA) and Generalized Autoregressive Conditionally Heteroscedastic (GARCH) models to identify the efficient model through (MAE, MASE) measures to determine the best forecasting model is studied. The findings show that the models of Generalised Autoregressive Conditional Heteroscedastic are more efficient in forecasting time series of financial. In addition, the GARCH model (1,1) is the best to forecasting exchange rate.
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Novianti Dwi PujiAstuti and Suwanda. "Evaluasi Model Exponential Generelized Autoregressive Conditional Heteroscedastic (EGARCH)." Bandung Conference Series: Statistics 2, no. 2 (July 29, 2022): 358–64. http://dx.doi.org/10.29313/bcss.v2i2.4365.
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Abstract. In time series data that has a fairly high volatility, it is possible to have an error variance that is not constant (Heteroscedasticity). This is reflected in the square of error that also follows the time series model, for example the autoregressive (AR) model and the expectation of the conditional error square is not constant, the AR model of the square of error is called the Autoregressive Conditional Heteroscedastic (ARCH). The AR model that combines time series data and squared error is called Generalized Autoregressive Conditional Heteroscedastic (GARCH). However, the GARCH model ignores the asymmetric effect on the data. So Nelson (1991) developed the GARCH model to overcome the asymmetric problem with the Exponential GARCH model. The purpose of this study was to determine the symptoms of the EGARCH model and apply the EGARCH model in stock price index data at PT. Bank X in Indonesia. The data used is closing price data for the period January 2019 – December 2021. The results show that the Residual from GARCH(2.0) is used to test the effect of asymmetry. The best model used for forecasting based on the comparison results of MAPE, AIC and SIC values from several other models is the EGARCH(2,1) model.
Abstrak. Pada data deret waktu yang memiliki volatilitas cukup tinggi dimungkinkan memiliki varian error menjadi tidak konstan (Heteroskedastisitas). Hal ini tercermin dari kuadrat error yang juga mengikuti model deret waktu, misal model autoregressive (AR) dan ekpektasi kuadrat error bersyarat tidak konstan, model AR dari kuadrat error disebut Autoregressive Conditional Heteroscedastic (ARCH). Model AR yang menggabungkan data deret waktu dan kuadrat error disebut Generalized Autoregressive Conditional Heteroscedastic (GARCH). Namun model GARCH mengabaikan efek asimetris pada data. Sehingga Nelson (1991) mengembangkan model GARCH untuk mengatasi permasalahan asimetris dengan model Exponential GARCH. Tujuan dari penelitian ini adalah untuk mengetahui gejala model EGARCH dan menerapkan model EGARCH pada data indeks harga saham di PT. Bank X di Indonesia. Data yang digunakan merupakan data harga penutupan selama periode Januari 2019 – Desember 2021. Hasilnya menunjukkan bahwa Residual dari GARCH(2,0) dipakai untuk menguji pengaruh asimetri. Model terbaik yang digunakan untuk peramalan berdasarkan hasil perbandingan nilai MAPE, AIC maupun SIC dari beberapa model lainnya ialah model EGARCH(2,1).
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Jhohura, Fatema Tuz, and Md Israt Rayhan. "An Assessment of Renewable Energy in Bangladesh through ARIMA, Holts, ARCH-GARCH Models." Dhaka University Journal of Science 60, no. 2 (July 31, 2012): 159–62. http://dx.doi.org/10.3329/dujs.v60i2.11486.
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Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holts linear exponential smoothing, and the autoregressive conditional heteroscedastic (with their extensions to generalized autoregressive conditional heteroscedastic) models were fitted to the data. The goodness of fit is performed via the Akaike information criteria, Schwartz Bayesian criteria. It was established that the generalized autoregressive conditional heteroscedastic model was superior to the autoregressive integrated moving average model and Holts linear exponential smoothing because the data was characterized by changing mean and variance.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11486 Dhaka Univ. J. Sci. 60(2): 159-162, 2012 (July)
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Iqbal, Teuku Achmad, Kusman Sadik, and I. Made Sumertajaya. "Pemodelan Pengukuran Luas Panen Padi Nasional Menggunakan Generalized Autoregressive Conditional Heteroscedastic Model (GARCH)." Jurnal Penelitian Pertanian Tanaman Pangan 33, no. 1 (April 30, 2014): 17. http://dx.doi.org/10.21082/jpptp.v33n1.2014.p17-26.
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This study was aimed to build a model for the estimation of national harvested area of rice by incorporating element of variant heterogeneity and the influence of asymmetry factors on time series data using five types of GARCH models, namely: symmetric GARCH, exponential asymmetric GARCH, quadratic asymmetric GARCH, Threshold GARCH, and non-linear asymmetric GARCH. Those models were compared and evaluated, and then the best model was used to predict the accuracy of the national rice harvested area. The results showed that two types of GARCH had significant coefficient, indicating the validity of the model. Those models were symmetric GARCH and quadratic GARCH models. Based on the value of mean absolute percentage error (MAPE) for the twelve month periods ahead, quadratic GARCH model was better than the symmetric GARCH model. Furthermore, based on the value of mean absolute deviation (MAD) and mean square error (MSE), quadratic GARCH model also seemed to be a better model than symmetric GARCH model. The best model can be used to predict the harvested area in the subsequent year.
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Robinson Sihombing, Pardomuan, Oki Prasetia Hendarsin, Sarah Sholikhatun Risma, and Bekti Endar Susilowati. "The Application Of Autoregressive Integrated Moving Average Generalized Autoregressive Conditional Heteroscedastic (Arima - Garch)." Udayana Journal of Social Sciences and Humanities (UJoSSH) 4, no. 2 (September 29, 2020): 63. http://dx.doi.org/10.24843/ujossh.2020.v04.i02.p04.
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Rice farming for Indonesia is vital. Rice farming is inseparable from the fact that rice farming is the livelihood of most of the population, while rice is the staple food of almost all Indonesians. The nature of rice that is easy to process and, following the public consumption culture, causes a very high dependence on rice. On the other hand, the price of rice is quite volatile. If the price of rice is soaring high, it can cause changes in the pattern of rice consumption. Some people want a stable supply and rice price, available at all times and evenly distributed and at affordable prices. Because the cost of rice is quite fluctuating, it is necessary to have a model that can be used to predict future rice prices so that the right policies can be implemented. Autoregressive Integrated Moving Average Model Generalized Autoregressive Conditional Heteroscedastic (ARIMA-GARCH) is a useful model for evaluating and predicting price fluctuations. This model's application is implemented in the national average retail rice price data between January 2007 and December 2017. In this study, rice data in the study period was not stationary at the level so that differentiating was carried out in the data. The best model is ARIMA (1,1,2) and Garch model (2,0). In this model, the data has complied with the white noise assumption, and the resulting GARCH model is free from the heteroscedasticity assumption.
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Liko, Rozana. "Modeling the Behavior of Inflation Rate in Albania Using Time Series." JOURNAL OF ADVANCES IN MATHEMATICS 13, no. 3 (July 30, 2017): 7257–63. http://dx.doi.org/10.24297/jam.v13i3.6196.
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In this paper, time series theory is used to modelling monthly inflation data in Albania during the period from January 2000 to December 2016. The autoregressive conditional heteroscedastic (ARCH) and their extensions, generalized autoregressive conditional heteroscedasticity (GARCH)) models are used to better fit the data. The study reveals that the inflation series is stationary, non-normality and has serial correlation. Based on minimum AIC and SIC values the best model turn to be GARCH (1, 1) model with mean equation ARMA (2, 1)x(2, 0)12. Based on the selected model one year of inflation is forecasted (from January 2016 to December 2016).
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Petrică, Andreea-Cristina, and Stelian Stancu. "The determinants of exchange rates and the movements of EUR/RON exchange rate via non-linear stochastic processes." Proceedings of the International Conference on Business Excellence 11, no. 1 (July 1, 2017): 937–48. http://dx.doi.org/10.1515/picbe-2017-0099.
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Abstract Modeling exchange rate volatility became an important topic for research debate starting with 1973, when many countries switched to floating exchange rate system. In this paper, we focus on the EUR/RON exchange rate both as an economic measure and present the implied economic links, and also as a financial investment and analyze its movements and fluctuations through two volatility stochastic processes: the Standard Generalized Autoregressive Conditionally Heteroscedastic Model (GARCH) and the Exponential Generalized Autoregressive Conditionally Heteroscedastic Model (EGARCH). The objective of the conditional variance processes is to capture dependency in the return series of the EUR/RON exchange rate. On this account, analyzing exchange rates could be seen as the input for economic decisions regarding Romanian macroeconomics - the exchange rates being influenced by many factors such as: interest rates, inflation, trading relationships with other countries (imports and exports), or investments - portfolio optimization, risk management, asset pricing. Therefore, we talk about political stability and economic performance of a country that represents a link between the two types of inputs mentioned above and influences both the macroeconomics and the investments. Based on time-varying volatility, we examine implied volatility of daily returns of EUR/RON exchange rate using the standard GARCH model and the asymmetric EGARCH model, whose parameters are estimated through the maximum likelihood method and the error terms follow two distributions (Normal and Student’s t). The empirical results show EGARCH(2,1) with Asymmetric order 2 and Student’s t error terms distribution performs better than all the estimated standard GARCH models (GARCH(1,1), GARCH(1,2), GARCH(2,1) and GARCH(2,2)). This conclusion is supported by the major advantage of the EGARCH model compared to the GARCH model which consists in allowing good and bad news having different impact on the volatility. The EGARCH model is able to model volatility clustering, persistence, as well as the leverage effect.
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Xuan, Haiyan, Lixin Song, Muhammad Amin, and Yongxia Shi. "Quasi-maximum likelihood estimator of Laplace (1, 1) for GARCH models." Open Mathematics 15, no. 1 (December 29, 2017): 1539–48. http://dx.doi.org/10.1515/math-2017-0131.
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Abstract This paper studies the quasi-maximum likelihood estimator (QMLE) for the generalized autoregressive conditional heteroscedastic (GARCH) model based on the Laplace (1,1) residuals. The QMLE is proposed to the parameter vector of the GARCH model with the Laplace (1,1) firstly. Under some certain conditions, the strong consistency and asymptotic normality of QMLE are then established. In what follows, a real example with Laplace and normal distribution is analyzed to evaluate the performance of the QMLE and some comparison results on the performance are given. In the end the proofs of some theorem are presented.
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Lee, Sangyeol, Chang Kyeom Kim, and Sangjo Lee. "Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression." Entropy 22, no. 5 (May 20, 2020): 578. http://dx.doi.org/10.3390/e22050578.
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This study considers the problem of detecting a change in the conditional variance of time series with time-varying volatilities based on the cumulative sum (CUSUM) of squares test using the residuals from support vector regression (SVR)-generalized autoregressive conditional heteroscedastic (GARCH) models. To compute the residuals, we first fit SVR-GARCH models with different tuning parameters utilizing a time series of training set. We then obtain the best SVR-GARCH model with the optimal tuning parameters via a time series of the validation set. Subsequently, based on the selected model, we obtain the residuals, as well as the estimates of the conditional volatility and employ these to construct the residual CUSUM of squares test. We conduct Monte Carlo simulation experiments to illustrate its validity with various linear and nonlinear GARCH models. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and Korean won/U.S. dollar (KRW/USD) exchange rate datasets is provided to exhibit its scope of application.
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Kipriyanov, Aleksei. "Comparison of Models for Growth-at-Risk Forecasting." Russian Journal of Money and Finance 81, no. 1 (March 2022): 23–45. http://dx.doi.org/10.31477/rjmf.202201.23.
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During the past several decades, the importance of assessing the risk of GDP growth downturns has increased tremendously. The financial crisis of 2008–2009 and the global lockdown caused by the COVID-19 pandemic demonstrated the vulnerability of the modern economy. As a result, a new framework (Growth-at-Risk) has been developed which allows the estimation of the size of the potential downfall of future GDP growth. However, most of the research focuses on the performance of quantile regression. I apply different approaches to forecasting growth-at-risk, including quantile regression, quantile random forests, and generalised autoregressive conditional heteroscedastic (GARCH) models, using the US economy for the analysis. I find that GARCH-type models perform worse at 5% and 10% coverage levels, but that quantile random forests and quantile regressions seem to have equal predictive ability.
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Magaji, Bashir, and Jamilu Garba. "Forecasting the exchange rate of Nigerian Naira to United State’ Dollar using ARIMA-GARCH Model." Dutse Journal of Pure and Applied Sciences 8, no. 3b (October 14, 2022): 87–96. http://dx.doi.org/10.4314/dujopas.v8i3b.9.
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In order to model and forecast exchange rates in both developed and emerging countries, majority of time series analysts have employed various technical and fundamental approaches, the forecast outcome differs depending on the approach chosen or implemented. In this view, this study is about hybridization of Autoregressive Integrated Moving Average (ARIMA) with Generalized Autoregressive Conditional Heteroscedastic (GARCH) model in forecasting exchange rate using monthly data of the Nigerian Naira against the U.S. Dollar for the period of January 2002 to February 2020. The stationarity of the exchange rate series is examined using unit root test of Augmented Dickey Fuller (ADF) test and Kwaitkowski-Philips-Schmidt-Shin (KPSS) which showed that the series is non stationary. To make the exchange rate series stationary, the data was transformed by first differencing and appropriate ARIMA models were obtained using Box-Jenkins method. ARIMA (0,1,1) and ARIMA(0,1,2) models were selected using AIC criteria and the residuals of these models were found to be serially correlated and heteroscedastic; hence the need for the hybridization of ARIMA with GARCH model. Therefore ARIMA models were hybridized with GARCH(1,1) to form ARIMA(0,1,1)-GARCH(1,1) and ARIMA(0,1,2)-GARCH(1,1). The results of forecast performance indicates that the best model is ARIMA(0,1,1)–GARCH(1,1) which has the lowest Root Means Square Error (RMSE) and Mean Absolute Error( MAE).
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Firmansyah, Firmansyah, Afriani H, and Wahyu Aji Paiso. "Analisis Volatilitas Harga Daging Sapi Sebelum Sampai Dengan Sesudah Hari Besar Agama di Kota Jambi." Jurnal Ilmiah Universitas Batanghari Jambi 21, no. 1 (February 8, 2021): 365. http://dx.doi.org/10.33087/jiubj.v21i1.1332.
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This study aims to analyze the level of beef price volatility before fasting (D-7) to after Eid (H + 7) in Jambi City, and compile a forecast model. This study used a survey method for beef traders in the Angso Duo market, Jambi City. The analysis used to calculate the volatility of beef prices is the ARCH (Autoregressive Conditional Heteroscedastic) model analysis and the GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model analysis. The average price of beef during the period before fasting (D-7) to after Eid (H + 7) in Jambi City was IDR 124,147 per kg with the highest price of IDR 150,000 and the lowest was 110,000 per kg. The volatility of beef prices during the period before fasting (D-7) to after Eid (H + 7) in Jambi City is the highest before Eid al-Fitr (Eid). ARCH and GARCH models can predict the future value of beef prices.
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Abdul Halim, Nurfadhlina, Endang Soeryana, and Alit Kartiwa. "A GARCH APPROACH TO VaR CALCULATION IN FINANCIAL MARKET." International Journal of Quantitative Research and Modeling 1, no. 1 (February 2, 2020): 35–46. http://dx.doi.org/10.46336/ijqrm.v1i1.5.
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Value at Risk (VaR) has already becomes a standard measurement that must be carried out by financial institution for both internal interest and regulatory. VaR is defined as the value that portfolio will loss with a certain probability value and over a certain time horizon (usually one or ten days). In this paper we examine of VaR calculation when the volatility is not constant using generalized autoregressive conditional heteroscedastic (GARCH) model. We illustrate the method to real data from Indonesian financial market that is the stock of PT. Indosat Tbk.
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Pincak, Richard, and Kabin Kanjamapornkul. "GARCH in spinor field." International Journal of Geometric Methods in Modern Physics 16, no. 07 (July 2019): 1950099. http://dx.doi.org/10.1142/s0219887819500993.
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We extend generalized autoregressive conditional heteroscedastic (GARCH) errors in the Euclidean plane of the scalar field to the tensor field and to the spinor field [Formula: see text], the so-called spinor garch, S-GARCH. We use the model of S-GARCH to explain the stylized fact in financial time series, the so-called volatility cluster, by using hyperbolic coordinate with induced complex lag of delay time scale in mirror symmetry concept. As the result of this theory, we obtain an equivalent form of Yang–Mills equation for financial time series as the interaction between the behavior of traders, the so-called, fundamentalist, chatlist and noise trader, by using volatility in spinor field with invariant of the gauge group [Formula: see text], the so-called modeling of the financial market in icosahedral supersymmetry gauge group.
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Bose, Shekar, and Hafizur Rahman. "Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market." SAGE Open 12, no. 4 (October 2022): 215824402211271. http://dx.doi.org/10.1177/21582440221127157.
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The primary objective of this paper is to empirically examine the nature and statistical significance of the news effect on conditional volatility of unpredictable components of stock returns. Daily stock return data of 12 local and multinational companies on Dhaka Stock Exchange Ltd., Bangladesh, for the period 1990 to 2011 were used in this study. The likelihood of asymmetric effects of news on conditional volatility was tested using a set of diagnostics under the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework. The results fail to reject the null hypothesis of symmetric effects, thereby suggesting that the conditional volatility of unpredictable components of stock returns is affected equally by positive and negative news. The robustness of the results was further checked by using three widely used asymmetric models, namely exponential GARCH (EGARCH), Glosten, Jagannathan & Runkle (GJR)-GARCH, and a partially non-parametric Autoregressive Conditional Heteroscedastic (PNP-ARCH) models. Yet again, the results do not provide any evidence of significant asymmetric effects in the volatility process. In addition, the descriptive results confirm the stylized facts of unpredictable return series such as non-normal distribution, time variant conditional volatility, and persistence in return volatility. Collectively these findings, perhaps, indicate the adequacy of the GARCH (1,1) model in representing the data generating process. A number of regulatory and behavioral factors that are anticipated to be accountable for the absence of asymmetric news effects are underlined. Finally, some policy implications of the results and possible extensions of the present paper are also conveyed. JEL codes: G10, G12, G14
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Niedzielski, Tomasz, and Wieslaw Kosek. "An Application of Low-Order Arma and Garch Models for Sea Level Fluctuations." Artificial Satellites 45, no. 1 (January 1, 2010): 27–39. http://dx.doi.org/10.2478/v10018-010-0003-x.
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An Application of Low-Order Arma and Garch Models for Sea Level FluctuationsThe paper presents the analysis of geographically-dependent irregular sea level fluctuations, often referred to as residual terms around deterministic signals, carried out by means of stochastic low-order autoregressive moving average (ARMA) and generalised autoregressive conditional heteroscedastic (GARCH) models. The gridded sea level anomaly (SLA) time series from TOPEX/Poseidon (T/P) and Jason-1 (J-1) satellite altimetry, commencing on 10th January 1993 and finishing on 14th July 2003, has been examined. The aforementioned models, limited to low-orders being combinations of 0,1 and 2, have been fitted to the SLA data. The root mean square and the Shapiro-Wilk test for the normal distribution have been used to calculate statistics of the residuals from these models. It has been found that autoregressive (AR) models as well as ARMA ones serve well the purpose of adequate modelling irregular sea level fluctuations, with a successful fit in some patchy bits of the equatorial Pacific. In contrast, GARCH models have been shown to be rather inaccurate, specifically in the vicinity of the tropical Pacific, in the North Pacific and in the equatorial Indian Ocean. The pattern of the Tropical Instability Waves (TIWs) has been noticed in the statistics of AR and ARMA model residuals indicating that the dynamics of these waves cannot be captured by the aforementioned linear stochastic processes.
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YANG, LU, and SHIGEYUKI HAMORI. "MODELING THE DYNAMICS OF INTERNATIONAL AGRICULTURAL COMMODITY PRICES: A COMPARISON OF GARCH AND STOCHASTIC VOLATILITY MODELS." Annals of Financial Economics 13, no. 03 (September 2018): 1850010. http://dx.doi.org/10.1142/s2010495218500100.
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In this study, we employ generalized autoregressive conditional heteroscedastic (GARCH) and stochastic volatility models to investigate the dynamics of wheat, corn, and soybean prices. We find that the stochastic volatility model provides the highest persistence of the volatility estimation in all cases. In addition, based on the monthly data, we find that the jump process and asymmetric effect do not exist in agricultural commodity prices. Finally, by estimating Value at risk (VaR) for these agricultural commodities, we find that the upsurge in agricultural prices in 2008 may have been caused by financialization.
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Zahid, Mamoona, Farhat Iqbal, Abdul Raziq Abdul Raziq, and Naveed Sheikh. "Modeling and Forecasting the Realized Volatility of Bitcoin using Realized HAR-GARCH-type Models with Jumps and Inverse Leverage Effect." Sains Malaysiana 51, no. 3 (March 31, 2022): 929–42. http://dx.doi.org/10.17576/jsm-2022-5103-25.
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Using the high-frequency data of Bitcoin, this study aims to model the time-varying volatility identified in the residuals of the heterogeneous autoregressive (HAR) model of realized volatility using the symmetric, asymmetric and long-memory generalized autoregressive conditional heteroscedastic models (GARCH) models. We further extended these models by incorporating jumps and continuous components in the realized volatility estimators and investigating the impact of the inverse leverage effect. The Diebold Mariano and model confidence set test confirm that the forecasting performance of HAR-type models can be effectively improved by these innovations. The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized volatility models. The findings of this study may benefit individual investors and risk managers who wish to minimize risks and diversify their portfolios to maximize profits in Bitcoin’s investment.
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Kurnia, Ranti Pramushinta, and Abdullah Ahmad Dzikrullah. "VOLATILITAS HARGA BAWANG DI JAWA BARAT DENGAN METODE ARCH/GARCH." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 3, no. 3 (December 31, 2022): 468–77. http://dx.doi.org/10.46306/lb.v3i3.153.
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Food commodities such as shallots and garlic are still a concern for the Indonesian people, this is because prices are not fixed and fluctuate for each period. It is these price fluctuations that cause high price volatility. An illustration of the magnitude of the risks that will be faced by economic actors in the future can be illustrated by the magnitude of price changes that indicate market fluctuations in a period of time. The model that can be used to analyze the nature of price volatility is Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The purpose of this study was to determine the price volatility of shallots and garlic in West Java Province for the period January 2013 to December 2021. The results showed that retail prices for garlic were heteroscedastic, so the model that was more suitable was ARCH/GARCH. As for the commodity of shallots that are homoscedastic, the model that is carried out is only up to the ARIMA model analysis. The volatility model for garlic is the ARCH(1) model. The results of the model estimation show that garlic price volatility is quite high
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Oredegbe, Abayomi, and Oye Abioye. "Stock Market Volatility and Persistence: Evidence from High-Income and Middle-Income Economies." International Journal of Economics and Finance 14, no. 8 (July 25, 2022): 56. http://dx.doi.org/10.5539/ijef.v14n8p56.
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This study examines the volatility of stock market indices in high-income and middle-income economies. Relying on daily closing prices from January 4, 2005 to May 4, 2021 and using the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model with one ARCH term and one GARCH term, the study finds evidence of long memory and mean reversion, suggesting that volatility persists but that it returns to its mean. In addition, the study finds that the latest news and prior information about volatility influence the volatility of indices, but prior information exerts greater influence. By providing a deeper understanding of stock market volatility in high-income and middle-income economies, this study contributes to the literature and provides investors, policymakers, and regulators additional insight.
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Dungore, Parizad Phiroze, and Sarosh Hosi Patel. "Analysis of Volatility Volume and Open Interest for Nifty Index Futures Using GARCH Analysis and VAR Model." International Journal of Financial Studies 9, no. 1 (January 14, 2021): 7. http://dx.doi.org/10.3390/ijfs9010007.
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The generalized autoregressive conditional heteroscedastic model (GARCH) is used to estimate volatility for Nifty Index futures on day trades. The purpose is to find out if a contemporaneous or causal relation exists between volatility volume and open interest for Nifty Index futures traded on the National Stock Exchange of India, and the extent and direction of these relationships. A complete absence of bidirectional causality in any particular instance depicts noise trading and empirical analysis according to this study establishes that volume has a stronger impact on volatility compared to open interest. Furthermore, the impulse originating from volatility of volume and open interest is low.
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Ojirobe, Yunusa Adavi, Abdulsalam Hussein Ahmad, and Ikwuoche John David. "Modelling and Forecasting Volatility of Crude Oil Returns in Nigeria based on Six Error Innovations." Journal of Statistical Modelling and Analytics 3, no. 1 (July 1, 2021): 78–93. http://dx.doi.org/10.22452/josma.vol3no1.6.
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Modeling price volatility of crude oil (PVCO) is pertinent because of the overbearing impact on any oil-producing economy. This study aimed at evaluating the performance of some volatility models in modeling and forecasting crude oil returns. Utilizing daily returns data from October 23, 2009, to March 23, 2020, this study attempted to capture the dynamics of crude oil price volatility in Nigeria using a symmetric and asymmetric GARCH models. In our research, we considered the generalized autoregressive conditional heteroscedastic model (GARCH), Exponential (E-GARCH), Glosten, Jagannathan and Runkle (GJR-GARCH) and Asymmetric Power (AP-ARCH) under six error innovations that include the skewed variant of the student-t, generalized error and normal distribution. From the results obtained, it was discovered that the AP-ARCH (1, 1) model performed better in the fitting and performance evaluation phase. The skew Student’s t-distribution (SStD) was also reported to be the best performing error innovation in most of the models. Based upon these results, we conclude that the AP-ARCH (1, 1)-SStD model is the best model for capturing the dynamics of crude oil returns in Nigeria.
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Chávez, Diego, Javier E. Contreras-Reyes, and Byron J. Idrovo-Aguirre. "A Threshold GARCH Model for Chilean Economic Uncertainty." Journal of Risk and Financial Management 16, no. 1 (December 28, 2022): 20. http://dx.doi.org/10.3390/jrfm16010020.
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In this paper, an autoregressive moving average (ARMA) model with threshold generalized autoregressive conditional heteroscedasticity (TGARCH) innovations is considered to model Chilean economic uncertainty time series. Uncertainty is measured through the Business Confidence Index (BCI) and Consumer Perception Index (CPI). The BCI time series provide useful information about industry; commerce; the finance, mining, construction, and agricultural sectors; and the global economic situation and the general business situation. As a counterpart, the CPI time series measure the perception of consumers regarding the state of the Chilean economy, evaluating their economic situation and expectations. The ARMA-TGARCH model is compared with the classical seasonal ARIMA and threshold AR ones. The results show that the ARMA-TGARCH model explains the regime changes in economic uncertainty better than the others, given that negative shocks are associated with statistically significant and quantitatively larger levels of volatility produced by the COVID-19 pandemic. In addition, a diagnostic analysis and prediction performance illustrates the suitability of the proposed model. Using a cross-validation analysis for the forecasting performance, a proposed heteroscedastic model may effectively help improve the forecasting accuracy for observations related to pessimism periods like the social uprising and the COVID-19 crisis which produced volatility in the Chilean uncertainty indexes.
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Alexis, Esdra, Thomas Plocoste, and Silvere Paul Nuiro. "Analysis of Particulate Matter (PM10) Behavior in the Caribbean Area Using a Coupled SARIMA-GARCH Model." Atmosphere 13, no. 6 (May 25, 2022): 862. http://dx.doi.org/10.3390/atmos13060862.
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The aim of this study was to model the behavior of particles with aerodynamic diameter lower or equal to 10μm (PM10) in the Caribbean area according to African dust seasonality. To carry out this study, PM10 measurement from Guadeloupe (GPE) and Puerto Rico (PR) between 2006 and 2010 were used. Firstly, the missing data issues were addressed using algorithms that we elaborated. Thereafter, the coupled SARIMA-GARCH (Seasonal Autoregressive Integrated Moving Average and Generalized Autoregressive Conditional Heteroscedastic) model was developed and compared to PM10 empirical data. The SARIMA process is representative of the main PM10 sources, while the heteroskedasticity is also taken into account by the GARCH process. In this framework, PM10 data from GPE and PR are decomposed into the sum of the background atmosphere (Bt = anthropogenic activities + marine aerosol), African dust seasonality (St = mineral dust), and extreme events processes (Ct). Akaike’s information criterion (AIC) helped us to choose the best model. Forecast evaluation indexes such as the Mean Absolute Percentage Error (MAPE), the Mean Absolute Scale Error (MASE), and Theil’s U statistic provided significant results. Specifically, the MASE and U values were found to be almost zero. Thus, these indexes validated the forecasts of the coupled SARIMA-GARCH model. To sum up, the SARIMA-GARCH combination is an efficient tool to forecast PM10 behavior in the Caribbean area.
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Elek, P., and L. Márkus. "A long range dependent model with nonlinear innovations for simulating daily river flows." Natural Hazards and Earth System Sciences 4, no. 2 (April 16, 2004): 277–83. http://dx.doi.org/10.5194/nhess-4-277-2004.
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Abstract. We present the analysis aimed at the estimation of flood risks of Tisza River in Hungary on the basis of daily river discharge data registered in the last 100 years. The deseasonalised series has skewed and leptokurtic distribution and various methods suggest that it possesses substantial long memory. This motivates the attempt to fit a fractional ARIMA model with non-Gaussian innovations as a first step. Synthetic streamflow series can then be generated from the bootstrapped innovations. However, there remains a significant difference between the empirical and the synthetic density functions as well as the quantiles. This brings attention to the fact that the innovations are not independent, both their squares and absolute values are autocorrelated. Furthermore, the innovations display non-seasonal periods of high and low variances. This behaviour is characteristic to generalised autoregressive conditional heteroscedastic (GARCH) models. However, when innovations are simulated as GARCH processes, the quantiles and extremes of the discharge series are heavily overestimated. Therefore we suggest to fit a smooth transition GARCH-process to the innovations. In a standard GARCH model the dependence of the variance on the lagged innovation is quadratic whereas in our proposed model it is a bounded function. While preserving long memory and eliminating the correlation from both the generating noise and from its square, the new model is superior to the previously mentioned ones in approximating the probability density, the high quantiles and the extremal behaviour of the empirical river flows.
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Napitupulu, Herlina, Rizki Apriva Hidayana, and Jumadil Saputra. "Determination of VaR on BBRI Stocks and BMRI Stocks Using the ARIMA-GARCH Model." Operations Research: International Conference Series 2, no. 3 (September 5, 2021): 71–74. http://dx.doi.org/10.47194/orics.v2i3.178.
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Stocks are investment instruments that are much in demand by investors as a basis in financial storage. Return and risk are the most important things in investing. Return is a complete summary of investment and the return series is easier to handle than the price series. The movement of risk of loss is obtained from stock investments with profits. One way to calculate risk is value-at-risk. The movement of stocks is used to form a time series so that the calculation of risk can use time series. The purpose of this study was to find out the Value-at-Risk value of BBRI and BMRI stock using the ARIMA-GARCH model. The data used in this study was the daily closing price for 3 years. The time series method used is the Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedastic (GARCH) model. The stage of analysis is to determine the prediction of stock price movements using the ARIMA model used for the mean model and the GARCH model is used for volatility models. The average value and variants obtained from the model are used to calculate value-at-risk in BBRI and BMRI stock. The results obtained are the ARIMA(3,0,3)-GARCH(1,1) and ARIMA(2,0,2)-GARCH(1,1) model so with a significance level of 5% obtained Value-at-Risk of 0.04058 to BBRI stock and 0.10167 to BMRI stock.
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Olaniyan, Sunday, and Hamadu Dallah. "MODELING THE VOLATILITY FOR LONG TERM INTEREST RATE RETURNS IN THE NIGERIA BOND MARKET USING CONDITIONALY HETEROSCEDASTIC MODELS." Jurnal Wahana Akuntansi 15, no. 1 (August 5, 2020): 46–56. http://dx.doi.org/10.21009/wahana.15.014.
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Investigating the volatility of financial assets is fundamental to risk management. This study used generalized Autoregressive Conditional Heteroscedastic Volatility models to evaluate the volatility of the long term interest rate of Nigeria's financial market. We also incorporated three innovations distributions viz: the Gaussian, the student-t, and the Generalized Error Distribution (GED) in the modeling process under the maximum likelihood estimation method. The results show that GARCH (GED) is the most performing model for describing the volatility of three and twenty-year interest rate returns while TARCH (GED) is the most suitable model for describing the volatility of five and ten-year interest rate returns in Nigeria. The preferred models will help in the development of tools for effective risk management by monitoring the behavior of long term interest rates.
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LAMA, ACHAL, K. N. SINGH, RAVINDRA SINGH SHEKHAWAT, KRISHNA PADA SARKAR, and BISHAL GURUNG. "Forecasting price index of finger millet (Eleusine coracana) in India under policy interventions." Indian Journal of Agricultural Sciences 90, no. 5 (September 4, 2020): 885–89. http://dx.doi.org/10.56093/ijas.v90i5.104334.
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Millets are the major substitute for cereals such as rice and wheat. For developing country like India, millets hold immense importance as the cost of production is low and has high nutritional values. Various policy interventions are made by government of India from time to time to popularise its consumption and production. Few major policy interventions were made in last decade and inclusion of coarse cereals under Food Security Bill is one among them. Keeping this in mind, the present study was carried out at ICAR- Indian Agricultural Statistics Research Institute, New Delhi during 2018 to know the impact of policy interventions on the price index of Ragi. Further, we have introduced these interventions in the model using structural break analysis. The volatile Ragi price index series were modelled and forecasted using popular class of Generalised Autoregressive Conditional Heteroscedastic (GARCH) models and its asymmetric extensions. The results indicated improvement in modelling and forecasting performance of the models after incorporation of the policy interventions. Study has empirically highlighted the positive impact of policies introduced.
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Bonga-Bonga, Lumengo, and Tebogo Maake. "The Relationship between Carry Trade and Asset Markets in South Africa." Journal of Risk and Financial Management 14, no. 7 (July 1, 2021): 300. http://dx.doi.org/10.3390/jrfm14070300.
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This paper investigates the extent of volatility or risk spillovers between the currency carry trade and asset markets, namely the equity and bond markets, in South Africa to infer the extent of the connectivity between the two markets. The carry trade operation examined in this paper involves two strategies, both of which use the South African rand as the investment currency, with the U.S. dollar and the Japanese yen as the funding currencies. The vector autoregressive BEKK-Generalised Autoregressive Conditional Heteroscedastic (multivariate VAR-BEKK-GARCH) model is used to this end. Moreover, the paper assesses the dynamic correlation between each currency carry trade and asset markets to infer the time-varying dependence between the two markets. The results of the empirical analysis show evidence of volatility spillover between the carry trade returns and the two asset market returns. The extent of the spillover depends on the choice of the funding currency, with the U.S. dollar-funded strategy transmitting more shocks to the South African equity market compared to the bond market. Moreover, the synchronisation of the dynamic correlation between each asset market and the currency carry trade returns shows that any possibility of arbitrage is precluded in the currency carry trade market.
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Ndwiga, David, and Peter W. Muriu. "Stock Returns And Volatility İn An Emerging Equity Market. Evidence From Kenya." European Scientific Journal, ESJ 12, no. 4 (February 28, 2016): 79. http://dx.doi.org/10.19044/esj.2016.v12n4p79.
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This study investigates volatility pattern of Kenyan stock market based on time series data which consists of daily closing prices of NSE Index for the period 2ndJanuary 2001 to 31st December 2014. The analysis has been done using both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. The study provides evidence for the existence of a positive and significant risk premium. Moreover, volatility shocks on daily returns at the stock market are transitory. We do not find any significant leverage effect. Introduction of the new regulations on foreign investors with a 25% minimum reserve of the issued share capital going to local investors (in 2002), introduction of live trading, cross listing in Uganda and Tanzania stock exchange (in 2006) and change in equity settlement cycle from T+4 to T+3 (in 2011) significantly reduce volatility clustering. The onset of US tapering increase the daily mean returns significantly while reducing conditional volatility.
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Hidayana, Rizki Apriva, Herlina Napitupulu, and Jumadil Saputra. "Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks." Operations Research: International Conference Series 2, no. 3 (September 4, 2021): 62–66. http://dx.doi.org/10.47194/orics.v2i3.176.
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Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock prices, the model that will be used is the Glosten Jagannathan Runkle-generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The purpose of this study was to determine the value of the risk obtained by using the time series model. GJR-GARCH is a development of GARCH by including the leverage effect. The effect of leverage is related to the concept of asymmetry. Asymmetry generally arises because of the difference between price changes and value volatility. The method used in this study is a literature and experimental study through secondary data simulations in the form of daily data from BCA shares and BNI shares. Data processing by looking at the heteroscedasticity of the data, then continued by using the GARCH model and seeing whether there is an asymmetry in the data. If there is an asymmetric effect on the processed data, then it is continued by using the GJR-GARCH model. The results obtained on the two stocks can be explained that the analyzed stock has a stock return volatility value for the leverage effect because the GJR-GARCH coefficient value is > 0. So, the risk value obtained by using VaR measurements on BCA stocks is 0.047247 and on BNI stocks. is 0.037355. Therefore, the ARMA-GJR-GARCH model is good for determining the value of stock risk using VaR.
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Hidayana, Rizki Apriva, Subiyanto Subiyanto, and Sudradjat Supian. "The Study of Value-At-Risk Calculation and Back-testing Using the ARMA-GARCH Model Based on Stock Returns: An Overview." International Journal of Research in Community Services 3, no. 4 (November 4, 2022): 142–46. http://dx.doi.org/10.46336/ijrcs.v3i4.368.
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Stocks are investment instruments that provide returns but tend to be risky. The most important component of investing is volatility, where volatility is identical to the standard conditional deviation of stock price return. The important thing in investing in addition to return is a risk. Value-at-Risk (VaR) is a statistical method of estimating maximum losses. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. The goal of the study was to estimate the Autoregressive Moving Average-Generalized Conditional Heteroscedastic (ARMA-GARCH) model to determine Value-at-Risk and back-testing. ARMA is a combination of AR and MA models, while GARCH is a time series model with symmetrical properties. The method in this study is systematic browsing of libraries. Systematic library tracing is an attempt to identify, evaluate, and interpret all research relevant to a particular phenomenon.
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Chandra Pati, Pratap, and Prabina Rajib. "Volatility persistence and trading volume in an emerging futures market." Journal of Risk Finance 11, no. 3 (May 25, 2010): 296–309. http://dx.doi.org/10.1108/15265941011043666.
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PurposeThe purpose of this paper is to estimate time‐varying conditional volatility, and examine the extent to which trading volume, as a proxy for information arrival, explain the persistence of futures market volatility using National Stock Exchange S&P CRISIL NSE Index Nifty index futures.Design/methodology/approachTo estimate the volatility and capture the stylized facts of fat‐tail distribution, volatility clustering, leverage effect, and mean‐reversion in futures returns, appropriate ARMA‐generalized autoregressive conditional heteroscedastic (GARCH) and ARMA‐EGARCH models with generalized error distribution have been used. The ARMA‐EGARCH model is augmented by including contemporaneous and lagged trading volume to determine their contribution to time‐varying conditional volatility.FindingsThe paper finds evidence of leverage effect, which indicates that negative shocks increase the futures market volatility more than positive shocks of the same magnitude. In addition, the results indicate that inclusion of both contemporaneous and lagged trading volume in the GARCH model reduces the persistence in volatility, but contemporaneous volume provides a greater reduction than lagged volume. Nevertheless, the GARCH effect does not completely vanish.Practical implicationsResearch findings have important implications for the traders, regulatory bodies, and practitioners. A positive volume‐price volatility relationship implies that a new futures contract will be successful only to the extent that there is enough price uncertainty associated with the underlying asset. Higher trading volume causes higher volatility; so, it suggests the need for greater regulatory restrictions.Originality/valueEquity derivatives are relatively new phenomena in Indian capital market. This paper extends and updates the existing empirical research on the relationship between futures price volatility and volume in the emerging Indian capital market using improved methodology and recent data set.
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Paul, Ranjit Kumar, and Md Yeasin. "COVID-19 and prices of pulses in Major markets of India: Impact of nationwide lockdown." PLOS ONE 17, no. 8 (August 25, 2022): e0272999. http://dx.doi.org/10.1371/journal.pone.0272999.
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The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected for five major markets in India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad during the period January, 2019 to September, 2020 from Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The Government of India declared nationwide lockdown since March, 24, to May, 31, 2020 in different phases in order to restrict the spread of the infection due to COVID-19. To see the impact of lockdown on price and price volatility, time series model namely Autoregressive integrated moving average (ARIMA) model with error following Generalized autoregressive conditional heteroscedastic (GARCH) model incorporating exogenous variable as lockdown dummy in both mean as well variance equations. It is observed that in almost all the markets, lockdown has significant impact on price of the pulses whereas in few cases, it has significant impact on price volatility.
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Soeryana Hasbullah, Endang, Nurfadhlina Bt Abdul Halim, Sukono ., Adam Sukma Putra, and Abdul Talib Bon. "Mean-Variance Portfolio Optimization on Islamic Stocks by Using Non Constant Mean and Volatility Models and Genetic Algorithm." International Journal of Engineering & Technology 7, no. 3.20 (September 1, 2018): 366. http://dx.doi.org/10.14419/ijet.v7i3.20.19274.
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The risk in stock market has taken an sinificant issue in investment of stock market, including Investment in some Islamic stocks. In order to minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several Islamic stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to Islamic stocks by using mean and volatility is not constant approaches. Non constant mean analyzed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analyzed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique followed by the Genetic Algorithm (GA). The expected result is to get the proportion of investment in each Islamic stock analyzed. Based on the result, we got that GA give a proportion of portfolio optimum selection with the best expected return. However, The GA has more potential candidate of solution that give the investor an alternative of their optimum portfolio selection. in this paper, we only present the best solution which has the highest fitness to the model.
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Mohammed, Geleta T., Jane A. Aduda, and Ananda O. Kube. "Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model." Applied Computational Intelligence and Soft Computing 2021 (December 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/6637091.
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This work shown as the fuzzy-EGARCH-ANN (fuzzy-exponential generalized autoregressive conditional heteroscedastic-artificial neural network) model does not require continuous model calibration if the corresponding DE algorithm is used appropriately, but other models such as GARCH, EGARCH, and EGARCH-ANN need continuous model calibration and validation so they fit the data and reality very well up to the desired accuracy. Also, a robust analysis of volatility forecasting of the daily S&P 500 data collected from Yahoo Finance for the daily spanning period 1/3/2006 to 20/2/2020. To our knowledge, this is the first study that focuses on the daily S&P 500 data using high-frequency data and the fuzzy-EGARCH-ANN econometric model. Finally, the research finds that the best performing model in terms of one-step-ahead forecasts based on realized volatility computed from the underlying daily data series is the fuzzy-EGARCH-ANN (1,1,2,1) model with Student’s t-distribution.
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Banik, Shipra, Mohammed Anwer, and A. F. M. Khodadad Khan. "Modeling Chaotic Behavior of Chittagong Stock Indices." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/410832.
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Stock market prediction is an important area of financial forecasting, which attracts great interest to stock buyers and sellers, stock investors, policy makers, applied researchers, and many others who are involved in the capital market. In this paper, a comparative study has been conducted to predict stock index values using soft computing models and time series model. Paying attention to the applied econometric noises because our considered series are time series, we predict Chittagong stock indices for the period from January 1, 2005 to May 5, 2011. We have used well-known models such as, the genetic algorithm (GA) model and the adaptive network fuzzy integrated system (ANFIS) model as soft computing forecasting models. Very widely used forecasting models in applied time series econometrics, namely, the generalized autoregressive conditional heteroscedastic (GARCH) model is considered as time series model. Our findings have revealed that the use of soft computing models is more successful than the considered time series model.
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Chalissery, Neenu, Mosab I. Tabash, Mohamed Nishad T., and Maha Rahrouh. "Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective." Investment Management and Financial Innovations 19, no. 4 (December 6, 2022): 244–59. http://dx.doi.org/10.21511/imfi.19(4).2022.20.
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In recent years, numerous models with various amounts of variance have been developed to estimate and forecast important characteristics of time series data. While there are many studies on asymmetric volatility and accuracy testing of univariate Generalized Autoregressive Conditional Heteroscedasticity models, there are no parallel studies involving multiple financial assets and different heteroscedastic models and density functions. The objective of this study is to contrast the forecasting accuracy of univariate volatility models with Normal and Student-t distributions in forecasting the volatility of stock, gold futures, crude futures, exchange rate, and bond yield over a 10-year time span from January 2010 through December 2021 in Indian market. The results of exponential, threshold and asymmetric power models show that the volatility stock (–0.12047, 0.17433, 0.74020 for Nifty, and –0.1153, 0.1676, 0.7372 for Sensex), exchange rate (–0.0567, 0.0961,0.9004), crude oil futures (-0.0411, 0.0658, 0.2130), and bond yield (–0.0193, 0.0514 and –0.0663) react asymmetrically to good and bad news. In case of gold futures, an inverse asymmetric effect (0.0537, –0.01217, –0.1898) is discovered; positive news creates higher variance in gold futures than bad news. The Exponential model captures the asymmetric volatility effect in all asset classes better than any other asymmetric models. This opens the door for many studies in Indian financial market.
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Inglada-Pérez, Lucía, and Pablo Coto-Millán. "A Chaos Analysis of the Dry Bulk Shipping Market." Mathematics 9, no. 17 (August 26, 2021): 2065. http://dx.doi.org/10.3390/math9172065.
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Finding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this paper aims to unveil the nonlinear dynamics of the Baltic Dry Index that has been proposed as a measure of the shipping rates for certain raw materials. We tested for the existence of nonlinearity and low-dimensional chaos. We have also examined the chaotic dynamics throughout three sub-sampling periods, which have been determined by the volatility pattern of the series. For this purpose, from a comprehensive view we apply several metric and topological techniques, including the most suitable methods for noisy time series analysis. The proposed methodology considers the characteristics of chaotic time series, such as nonlinearity, determinism, sensitivity to initial conditions, fractal dimension and recurrence. Although there is strong evidence of a nonlinear structure, a chaotic and, therefore, deterministic behavior cannot be assumed during the whole or the three periods considered. Our findings indicate that the generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model explain a significant part of the nonlinear structure that is found in the dry bulk shipping freight market.
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Ghysels, Eric, Alberto Plazzi, Rossen Valkanov, Antonio Rubia, and Asad Dossani. "Direct Versus Iterated Multiperiod Volatility Forecasts." Annual Review of Financial Economics 11, no. 1 (December 26, 2019): 173–95. http://dx.doi.org/10.1146/annurev-financial-110217-022808.
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Multiperiod-ahead forecasts of returns’ variance are used in most areas of applied finance where long-horizon measures of risk are necessary. Yet, the major focus in the variance forecasting literature has been on one-period-ahead forecasts. In this review, we compare several approaches of producing multiperiod-ahead forecasts within the generalized autoregressive conditional heteroscedastic (GARCH) and realized volatility (RV) families—iterated, direct, and scaled short-horizon forecasts. We also consider the newer class of mixed data sampling (MIDAS) methods. We carry the comparison on 30 assets, comprising equity, Treasury, currency, and commodity indices. While the underlying data are available at high frequency (5 minutes), we are interested in forecasting variances 5, 10, 22, 44, and 66 days ahead. The empirical analysis, which is performed in sample and out of sample with data from 2005 to 2018, yields the following results: Iterated GARCH dominates the direct GARCH approach, and the direct RV is preferred to the iterated RV. This dichotomy of results emphasizes the need foran approach that uses the richness of high-frequency data and, at the same time, produces a direct forecast of the variance at the desired horizon, without iterating. The MIDAS is such an approach, and unsurprisingly, it yields the most precise forecasts of variance both in and out of sample. More broadly, our study dispels the notion that volatility is not forecastable at long horizons and offers an approach that delivers accurate out-of-sample predictions.
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Guo, Jin, and Tetsuji Tanaka. "Dynamic Transmissions and Volatility Spillovers between Global Price and U.S. Producer Price in Agricultural Markets." Journal of Risk and Financial Management 13, no. 4 (April 23, 2020): 83. http://dx.doi.org/10.3390/jrfm13040083.
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A considerable number of studies have examined the relationship between global prices and local prices in food-importing nations, but the linkages between international prices and the producer prices of large agricultural exporters have been largely ignored. This paper analyzes the connections between world prices and U.S. producer prices in the wheat, soybeans, and corn markets using a vector error correction generalized autoregressive conditional heteroscedastic model with a multivariate Baba-Engle-Kraft Kroner specification (VECM-GARCH-BEKK) and cross-correlation function (CCF). Our findings indicate firstly that a long-run equilibrium relationship exists between international and U.S. producer prices for the three agricultural crops. It also finds a significant bidirectional causality-in-mean and causality-in-variance between international and U.S. producer prices for these crops. Finally, the empirical results suggest that international wheat and corn prices play a leading role in U.S. local markets in return transmissions and that U.S. wheat price can be considered to be a leading indicator of the global wheat price in volatility transmissions.
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Gu, Wentao, Linghong Zhang, Houjiao Xi, and Suhao Zheng. "Stock Prediction Based on News Text Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 5 (September 20, 2021): 581–91. http://dx.doi.org/10.20965/jaciii.2021.p0581.
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With the vigorous development of information technology, the textual data of financial news have grown massively, and this ever-rich online news information can influence investors’ decision-making behavior, which affects the stock market. Thus, online news is an important factor affecting market volatility. Quantifying the sentiment of news media and applying it to stock-market prediction has become a popular research topic. In this study, a financial news sentiment lexicon and an auxiliary lexicon applicable to the financial field are constructed, and a sentiment index (SI) is constructed by defining the weight of semantic rules. Then, a comprehensive sentiment index (CSI) is constructed via principal component analysis of the sentiment index and structured stock-market trading data. Finally, these two sentiment indices are added to the generalized autoregressive conditional heteroscedastic (GARCH) and the Long short-term memory (LSTM) models to predict stock returns. The results indicate that the prediction results of LSTM models are better than those of GARCH models. Compared with general-purpose lexicons, the financial lexicons constructed in this study are more stable, and the inclusion of a comprehensive investor sentiment index improves the accuracy of measuring sentiment information. Thus, the proposed lexicons allow more comprehensive measurement of the effects of external sentiment factors on stock-market returns and can improve the prediction effect of stock-return models.
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Ou, Jishun, Xiangmei Huang, Yang Zhou, Zhigang Zhou, and Qinghui Nie. "Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework." Entropy 24, no. 10 (September 29, 2022): 1392. http://dx.doi.org/10.3390/e24101392.
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Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient , the shift factor , and the rotation factor . Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten–Jagannathan–Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations.
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Faal, Maryam, and Farshad Almasganj. "ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome." Journal of Healthcare Engineering 2021 (July 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/4894501.
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This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying. So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), where the former captures the linearity property and the latter the nonlinear characteristics of the ECG signal. First, ECG signals are segmented into one-minute segments. The heteroskedasticity property is then examined through various tests such as the ARCH/GARCH test, kurtosis, skewness, and histograms. Next, the ARIMA model is applied to signals as a linear model and EGARCH as a nonlinear model. The appropriate orders of models are estimated by using the Bayesian Information Criterion (BIC). We assess the effectiveness of our model in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The data in this article is obtained from the Physionet Apnea-ECG database. Results show that the ARIMA-EGARCH model performs better than other models for modeling both apneic and normal ECG signals in sleep apnea syndrome.
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Qudratullah, Mohammad Farhan. "Analisis Tipologi Saham Syariah Di Bursa Efek Indonesia Berdasarkan Nilai Return Dan Resiko (Value At Risk) Pasca Krisis Global 2008." Jurnal Fourier 1, no. 1 (April 30, 2012): 17. http://dx.doi.org/10.14421/fourier.2012.11.17-26.
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Since the signed memorandum of understanding between BAPEPAM with Dewan Syariah Nasional-Majelis Ulama Indonesia (DSN-MUI) on the principle of Islamic capital market in 2003, the Islamic capital market in Indonesia has developed significantly. In each investment, including Islamic capital market investment, there are 2 (two) fundamental things that always accompany it, the return and risks. This paper discusses the analysis of return and risk of sharia stocks that always go in Jakarta Islamic Index (JII) after the global crisis in 2008, risk analysis tools using Value at risk (VaR) approach to model the Generalized Autoregressive Conditional Heteroscedastic (GARCH), then proceed with the analysis of the typology to determine the characteristics of these stocks. The results that shares sharia can be grouped into 4 (four) : 6 (six) shares entering the low return and low risk (TLKM, UNVR, SMGR, AALI, ELSA, and SGRO), 3 (three ) shares into group of low-return but high risk (INCO, ANTM, and TINS), 3 (three) shares enter the group of low risk but high return (PTBA, LSIP, and KLBF), and 4 (four) shares enter the group high return but high risk (ITMG, ASII, INTP, and BMTR).
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Handika, Rangga, and Iswahyudi Sondi Putra. "Commodities returns’ volatility in financialization era." Studies in Economics and Finance 34, no. 3 (August 7, 2017): 344–62. http://dx.doi.org/10.1108/sef-10-2015-0254.
Full textAbstract:
Purpose This paper aims to indirectly evaluate the accuracy of various volatility models using a value-at-risk (VaR) approach and to investigate the relationship between the accuracy of volatility modelling and investments performance in the financialized commodity markets. Design/methodology/approach This paper uses the VaR back-testing approach at six different commodities, seven different volatility models and five different time horizons. Findings This paper finds that the moving average (MA) VaR model tends to be the best for oil, copper, wheat and corn (long horizon) whereas the exponential generalized autoregressive conditional heteroscedastic (E-GARCH) VaR model tends to be the best for gold, silver and corn (short horizon). Our findings indicate that MA volatility model should be used for oil, copper, wheat and corn (for longer time horizons) commodities whereas E-GARCH volatility model should be used for gold, silver and corn (for short time horizons) commodities. We also find that there is a positive relationship between an accurate VaR performance and commodity return. This indicates that a good job in modelling volatility will be rewarded by higher returns in financialized commodity markets. Originality/value This paper indirectly evaluates the accuracy of volatility model via VaR measure and investigates the relationship between the accuracy of volatility and investments performance in financialized commodity markets. This paper contributes to the literature by offering VaR approach in evaluating volatility model performance and reporting the importance of performing accurate volatility modelling in financialized commodity markets.
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Yang Liu, Day, Ming Chen Chun, and Yi Kai Su. "The impacts of Covid-19 pandemic on the smooth transition dynamics of stock market index volatilities for the Four Asian Tigers and Japan." International Journal of Research in Business and Social Science (2147- 4478) 10, no. 4 (June 14, 2021): 183–94. http://dx.doi.org/10.20525/ijrbs.v10i4.1177.
Full textAbstract:
This rapid propagation of the Novel Coronavirus Disease (COVID-19) has caused the global healthcare system to break down. The infectious disease originated from East Asia and spread to the world. This unprecedented pandemic further damages the global economy. It seems highly probable that the COVID-19 recession changes stock market volatility. Therefore, this study resorts to the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model with a smooth transition method to capture the influences of the COVID-19 pandemic on the dynamic structure of the stock market index volatilities for some Asian countries (the Four Asian Tigers and Japan). The empirical results show that the shocks of the COVID-19 change the dynamic volatility structure for all stock market indices. Moreover, we acquire the transition function for all stock market index volatilities and find out that most of their regime adjustment processes start following the outbreak of the COVID-19 pandemic in the Four Asian Tigers except South Korea and Japan. Additionally, the estimated transition functions show that the stock market index volatilities contain U-shaped patterns of structural changes. This article also computes the corresponding calendar dates of structure change about dynamic volatility patterns. In the light of estimation of location parameters, we demonstrate that the structure changing the date of stock market index volatility for South Korea and Japan has occurred in late 2019.
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Dogo, Mela Yila, and Osman Nuri Aras. "The impact of COVID-19 on stock market returns: Empirical evidence from Nigeria." Eurasian Journal of Higher Education 3, no. 6 (March 24, 2022): 38–57. http://dx.doi.org/10.31039/ejohe.2022.6.70.
Full textAbstract:
The study investigates the causal relationship between COVID-19 and changes in the prices and volume of stocks in the Nigerian stock market, to identify whether there is a short and or long-run relationship between changes in the Nigerian Stock Exchange All Share Index 30 (NSE ASI 30) and NSE 30 traceable to the outbreak and continued presence of the coronavirus-19 diseases (COVID-19) during the period 31 December 2019 to June 30, 2020.
The paper seeks to estimate the effect of the COVID-19 shocks on the volatilities of returns in the NSE30 and ASI 30 stocks. After a property check on the times series data, a correlation matrix was drawn to understand the relationship between stock returns in NSE30 and ASI30 with total cases, test units, new cases, female and male smokers, and COVID-9 deaths. The ADF results helped us in selecting what the use in the representative model.
We then applied the Lagrange Multiplier (LM) test on the residuals of both the mean and the variance equations of the NSE30 and the Generalized Autoregressive Conditional Heteroscedastic (GARCH) to estimate the short and long-run return spillovers and conditional correlations between the shock from COVID-19 and stock market returns.
Generally, the results indicated a weak impact of COVID-19 on the returns and volume of both the NSE30 and NSE ASI 30 stocks. This is so because of some data issues and issues relating to empirics. More data will be sought, and a proper review of the paper undertaken, to ascertain its usefulness for policy.
The study concludes that to spur economic growth in COVID-19, Nigeria’s economic managers, particularly, the monetary, fiscal, and capital market regulators must learn to work as a team, to ensure complementary in their policies and thus, propel the economy out of a likely recession.
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Sreeram, Latha. "Volatility Estimation Using Symmetric and Asymmetric Models in Oil Exporting Emerging Markets." Asian Journal of Finance & Accounting 11, no. 1 (February 24, 2019): 41. http://dx.doi.org/10.5296/ajfa.v11i1.14202.
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The study empirically investigates the volatility pattern of thirteen emerging economies which are predominantly oil exporting countries. It is based on the time series data which consists of monthly closing price data of their index for a ten-year period from 01 January 2008 to 31 December 2017. Emerging markets are considered as investment destinations due to the presence of risk premium which has made the stock markets of these countries more volatile. Added to this is that these countries underwent crisis due to the sharp decline in crude oil prices as they were primarily dependent on oil exports. Hence it is a significant to study the volatility behavior of these countries. The study has been done by employing both symmetric and asymmetric models of generalized autoregressive conditional heteroscedastic. As per Akaike Information Criterion (AIC), Log likelihood and Schwarz Information Criterion (SIC) the study provides evidence that GARCH (1,1) and TGARCH(1,1) estimations are found to be the most appropriate model that fits symmetric and asymmetric volatility respectively for all the thirteen countries. There was evidence of volatility clustering and leptokurtic in all the countries considered in the study. While EGARCH model revealed no support of existence of leverage on the stock returns, TGARCH supported existence of leverage in case of four countries. The tests for asymmetries in volatility indicate the size effect of the news, reaffirmed through the results of sign bias tests and news impact curves, which indicate that the size effect is stronger for bad news than the good news for countries which supported existence of leverage.
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Azeem, Muhammad, Nisar Ahmad, Sarfraz Hussain, Muzammil Khurshid, and Safyan Majid. "IMPACT OF IMF LENDING ANNOUNCEMENTS ON THE PERFORMANCE OF STOCK MARKET: EMPIRICAL EVIDENCE FROM PAKISTAN." Humanities & Social Sciences Reviews 9, no. 3 (May 20, 2021): 467–76. http://dx.doi.org/10.18510/hssr.2021.9348.
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Purpose of the study: Stock markets have demonstrated varying reactions to IMF lending announcements across various economies. Announcements offered by IMF often be perceived negatively by the participants of the stock market, because of stringent conditions accompanied with the loan that may oppose the political and economic agenda of a borrowing nation. Thus, this study intends to investigate the impact of IMF’s announcements about extending loans to Pakistan on the performance of the Stock market in the debt-ridden economy.
Methodology: For regular returns from 1997 to 2017, the benchmarking indexes of KSE-100 and 30 were used. Meanwhile, IMF lending arrangements are categorized into three respective dummies (standby, extended credit facility, and extended fund facility). The Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was used to investigate the effect of IMF’s lending news on the regular stock returns.
Main findings: The results show a statistically significant effect of the IMF’s News about lending arrangements on the performance of the stock market in Pakistan. Surprisingly, the negative effect of IMF lending announcements on the performance of the stock market in Pakistan implies that the loans extended by IMF are not professed by speculators as good for the economic performance of the economy.
Application of this study: The findings of this study imply that simply extending loans is not a panacea for politically unstable and financially ruined nations. Lending strategies of IMF need to be favourable for the political and economic conditions of a borrowing country.
Originality/ Novelty: As for as the novelty is concerned, the study has highlighted the time-varying impact of IMF lending announcements on the performance of the stock market in a financially fragile country where a newborn government facing multiple challenges has made its best effort to avoid borrowing from IMF.