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Hanifa, Rezky Dwi, Mustafid Mustafid und Arief Rachman Hakim. „PEMODELAN AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DENGAN EFEK EXPONENTIAL GARCH (ARFIMA-EGARCH) UNTUK PREDIKSI HARGA BERAS DI KOTA SEMARANG“. Jurnal Gaussian 10, Nr. 2 (31.05.2021): 279–92. http://dx.doi.org/10.14710/j.gauss.v10i2.29933.
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Time series data is a type of data that is often used to estimate future values. Long memory phenomenon often occurs in time series data. Long memory is a condition that shows a strong correlation between observations even though they are quite far away. This phenomenon can be overcome by modeling time series data using the Autoregressive Fractional Integrated Moving Average (ARFIMA) model. This model is characterized by a fractional difference value. ARFIMA (Autoregressive Fractional Integrated Moving Average) model assumes that the residuals are normally distributed, mutually independent, and homogeneous. However, usually in financial data, the residual variants are not constant. This can be overcome by modeling variants. Standard equipment that can be used to model variants is the ARCH / GARCH (Auto Regressive Conditional Heteroscedasticity / Generalized Auto Regressive Conditional Heteroscedasticity) model. Another phenomenon that often occurs in GARCH models is the leverage effect on the residuals of the model. EGARCH (Exponential General Auto Regessive Conditional Heteroscedasticity) is a development of the GARCH model that is appropriate for data that has an leverage effect. The implementation of this model is by modeling financial data, so this study takes 136 monthly data on rice prices in Semarang City from January 2009 to April 2020. The purpose of this study is to create a long memory data forecasting model using the Exponential method. Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The best model obtained is ARFIMA (1, d, 1) EGARCH (1,1) which is capable of forecasting with a MAPE value of 3.37%.Keyword : Rice price, forecasting , long memory, leverage effect, GARCH, EGARCH
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Rossetti, Nara, Marcelo Seido Nagano und Jorge Luis Faria Meirelles. „A behavioral analysis of the volatility of interbank interest rates in developed and emerging countries“. Journal of Economics, Finance and Administrative Science 22, Nr. 42 (12.06.2017): 99–128. http://dx.doi.org/10.1108/jefas-02-2017-0033.
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Purpose This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market. Design/methodology/approach To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries. Findings The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events. Originality/value It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.
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Cheng, Cong, Ling Yu und Liu Jie Chen. „Structural Nonlinear Damage Detection Based on ARMA-GARCH Model“. Applied Mechanics and Materials 204-208 (Oktober 2012): 2891–96. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.2891.
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Two economic models, i.e. auto-regressive and moving average model (ARMA) and generalized auto-regressive conditional heteroscedasticity model (GARCH), are adopted to assess the conditions of structures and to detect structural nonlinear damage based on time series analysis in this study. To improve the reliability of the method for nonlinear damage detection, a new damage sensitive feature (DSF) for the ARMA-GARCH model is defined as a ratio of the standard deviation of the variance time series of ARMA-GARCH model residual errors in test condition to ones in reference condition. Compared to the traditional DSF defined as the ratio between the deviations of ARMA-GARCH model residual error in two conditions, the successful outcomes of the new DSF can give obvious explanation for the current states of structures and can detect the nonlinear damage exactly, which enhance the worth of structural health monitoring as well as condition-based maintenance in practical applications. This method is finally verified by a series of experimental data of three-story building structure made in Los Alamos National Laboratory USA.
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Sukono, Sukono, Emah Suryamah und Fujika Novinta S. „Application of ARIMA-GARCH Model for Prediction of Indonesian Crude Oil Prices“. Operations Research: International Conference Series 1, Nr. 1 (05.02.2020): 25–33. http://dx.doi.org/10.47194/orics.v1i1.21.
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Crude oil is one of the most important energy commodities for various sectors. Changes in crude oil prices will have an impact on oil-related sectors, and even on the stock price index. Therefore, the prediction of crude oil prices needs to be done to avoid the future prices of these non-renewable natural resources to increase dramatically. In this paper, the prediction of crude oil prices is carried out using the Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) models. The data used for forecasting are Indonesian Crude Price (ICP) crude oil data for the period January 2005 to November 2012. The results show that the data analyzed follows the ARIMA(1,2,1)-GARCH(0,3) model, and the crude oil price forecast for December 2012 is 105.5528 USD per barrel. The prediction results of crude oil prices are expected to be important information for all sectors related to crude oil.
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Sun, Kaiying. „Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model“. International Journal of Financial Research 8, Nr. 3 (12.06.2017): 154. http://dx.doi.org/10.5430/ijfr.v8n3p154.
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In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.
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Mirza, Hammad Hassan, und Naveed Mushtaq . „Stock Market Returns and Weather Anomaly: Evidence from an Emerging Economy“. Journal of Economics and Behavioral Studies 4, Nr. 5 (15.05.2012): 239–44. http://dx.doi.org/10.22610/jebs.v4i5.323.
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Financial economists believe that the arbitrage forces in the market are the main reason of market efficiency and these forces are the fundamental concept of efficient market hypothesis (EMH). During last few years, various theoretical and empirical evidences have been presented to support the work of financial modeling for the markets with less than rational investors whose trading strategies are based on psychological factors like mood and emotions. Weather condition is among the substantial factors affecting investors’ mood and emotions. Present study investigates the impact of temperature on stock market returns in emerging economy of Pakistan. Using the daily temperature records and stock market indices of Karachi and Islamabad, the study has employed auto regressive (AR) – generalized autoregressive conditional heteroscedasticity (GARCH) model from 2006 to 2010. Based on AR (1)-GARCH (1, 1) estimation the study has found that weather temperatures of both Karachi and Islamabad are negatively related with Karachi Stock Exchange (KSE) and Islamabad Stock Exchange (ISE) index returns, respectively.
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Abdullah, Ezatul Akma, Siti Meriam Zahari, S. Sarifah Radiah Shariff und Muhammad Asmu’i Abdul Rahim. „Modelling volatility of Kuala Lumpur composite index (KLCI) using SV and garch models“. Indonesian Journal of Electrical Engineering and Computer Science 13, Nr. 3 (01.03.2019): 1087. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp1087-1094.
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It is well-known that financial time series exhibits changing variance and this can have important consequences in formulating economic or financial decisions. In much recent evidence shows that volatility of financial assets is not constant, but rather that relatively volatile periods alternate with more tranquil ones. Thus, there are many opportunities to obtain forecasts of this time-varying risk. The paper presents the modelling volatility of the Kuala Lumpur Composite Index (KLCI) using SV and GARCH models. Thus, the aim of this study is to model the KLCI stock market using two models; Stochastic Volatility (SV) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH). This study employs an SV model with Bayesian approach and Markov Chain Monte Carlo (MCMC) sampler; and GARCH model with MLE estimator. The best model will be used to forecast the future volatility of stock returns. The study involves 971 daily observations of KLCI Closing price index, from 2 January 2008 to 10 November 2016, excluding public holidays. SV model is found to be the best based on the lowest RMSE and MAE values.
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Kaya Soylu, Pınar, Mustafa Okur, Özgür Çatıkkaş und Z. Ayca Altintig. „Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple“. Journal of Risk and Financial Management 13, Nr. 6 (29.05.2020): 107. http://dx.doi.org/10.3390/jrfm13060107.
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This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck’s tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns.
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Białek-Jaworska, Anna, und Tomasz Krawczyk. „Corporate bonds or bank loans? The choice of funding sources and information disclosure of Polish listed companies“. Central European Economic Journal 6, Nr. 53 (08.07.2020): 262–85. http://dx.doi.org/10.2478/ceej-2019-0017.
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AbstractThe paper aims to find what determines the choice of companies listed on the Warsaw Stock Exchange (WSE) between public debt (corporate bonds) and private debt (bank loans). For this purpose, we estimate logistic regression models and panel models of corporate borrowing determinants to compare the impact of enterprise characteristics on financing with the use of corporate bonds or bank loans. In this study, we are interested in explanatory variables that explain the role of transparency measured by the level of information disclosure; and a risk proxy of the variability of operational cash flows and investment risk (retrieved from generalised auto-regressive conditional heteroscedasticity [GARCH] models estimated on companies’ stocks [shares] trading on the WSE).
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Anand, C. „Comparison of Stock Price Prediction Models using Pre-trained Neural Networks“. March 2021 3, Nr. 2 (19.07.2021): 122–34. http://dx.doi.org/10.36548/jucct.2021.2.005.
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Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.
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Othman, Anwar Hasan Abdullah, Syed Musa Alhabshi und Razali Haron. „The effect of symmetric and asymmetric information on volatility structure of crypto-currency markets“. Journal of Financial Economic Policy 11, Nr. 3 (05.08.2019): 432–50. http://dx.doi.org/10.1108/jfep-10-2018-0147.
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Purpose This paper aims to examine whether the crypto-currencies’ market returns are symmetric or asymmetric informative, through analysing the daily logarithmic returns of bitcoin currency over the period of 2011-2017. Design/methodology/approach In doing so, the symmetric informative analysis is estimated by applying the generalised auto-regressive conditional heteroscedasticity (GARCH) (1,1) model, whereas asymmetric informative or leverage effects analysis is estimated by exponential GARCH (1,1), asymmetric power ARCH (1,1) and threshold GARCH (1,1) models. In addition, the generalized autoregressive conditional heteroskedasticity in mean (GARCH-M (1,1)) was applied to examine whether the risk-return trade-off phenomenon was persistent in crypto-currencies market. Findings The main findings indicate that bitcoin market return or volatility is symmetric informative and has a long memory to persist in the future. Furthermore, the sympatric volatility is found to be more sensitive to its past values (lagged) than to the new shock of the market values. However, asymmetric informative response of volatility to the negative and the positive shocks do not exist in the bitcoin market or, in other words, there is no leverage effect. This suggests that the bitcoin market is in harmony with the efficient market hypothesis (EMH) with respect to the asymmetric information and violated the EMH with regard to the symmetric information. Hence, the market price or return of bitcoin currency could not be predicted by simply exercising such past market information in the short-run investment. In addition, the estimated coefficient of conditional variance or risk premium (λ) in the mean equation of CHARCH–M (1,1) model is positive however, statistically insignificant. This indicates the absence of risk-return trade-off, in which case the higher market risk will not essentially lead to higher market returns. This paper has proposed that an investment in the crypto-currency market is more appropriate for risk-averse investors than risk takers. Originality/value The findings of the study will provide investors with necessary information about the bitcoin market price efficiency, hedging effectiveness and risk management.
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Indarwati, Septiana. „Benarkah Suku Bunga Memengaruhi Volatilitas Pasar Saham Syariah?“ Journal of Islamic Economics and Finance Studies 2, Nr. 1 (28.06.2021): 56. http://dx.doi.org/10.47700/jiefes.v2i1.2780.
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AbstractThis research attempts to explore to what extent the sensitivity volatility of Islamic stock markets in Indonesia toward macroeconomics. The writer examines inflation, exchange rates, money supply (JUB), interest rates (BIRATE), and Industrial Production Index (IPI) as part of the macroeconomic variables. Meanwhile, the writer also uses Jakarta Islamic Index (JII) as the measurements for Islamic stock markets. This research uses the calculation of the stock return volatility based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH (2, 1)) combined with Regressive Distributed Lag (ARDL) analysis. The writer uses monthly data from Indonesia Stock Exchange, starting from January 2006 to December 2019 as part of the data collection. This research found that BIRATE has a negative effect on the conventional stock market while the Islamic stock market has a positive and insignificant effect on the level α = 5%. It points out the Islamic principles that the interest rate is not a significant variable in explaining the stock market’s volatility. According to the finding of this research, the writer argues that stabilizing interest rates will not significantly impact the volatility of the Islamic stock market.AbstrakPenelitian ini mencoba untuk mengeksplorasi sejauh mana sensitivitas volatilitas pasar saham syariah di Indonesia terkait dengan ekonomi makro. Penulis menggunakan inflasi, nilai tukar rupiah, penawaran uang (JUB), suku bunga (BI rate) dan Indeks Produksi Industri (IPI) sebagai pengukuran dari ekonomi makro. Sementara itu, penulis menggunakan Jakarta Islamic index (JII) sebagai pengukuran pasar saham syariah. Penelitian ini menggunakan perhitungan volatilitas return saham dengan Generalized Autoregressive Conditional Heteroskedasticity (GARCH (2, 1) dikombinasikan dengan Analisis Autoregressive Distributed Lag (ARDL). Pengumpulan data dalam penelitian ini menggunakan data bulanan dari Bursa Efek Indonesia dari bulan Januari 2006 sampai Desember 2019. Penelitian ini menemukan bahwa, variable BI Rate tidak berpengaruh signifikan terhadap pasar saham syariah pada taraf α=5%. Ini menyoroti prinsip-prinsip Islam bahwa tingkat bunga bukanlah variabel yang signifikan dalam menjelaskan volatilitas pasar saham. Menurut temuan pada penelitian ini, penulis memberikan dukungan lebih lanjut bahwa menstabilkan suku bunga tidak akan berdampak signifikan pada volatilitas pasar saham syariah.
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Roni, Bhowmik, Ghulam Abbas und Shouyang Wang. „Return and Volatility Spillovers Effects: Study of Asian Emerging Stock Markets“. Journal of Systems Science and Information 6, Nr. 2 (08.05.2018): 97–119. http://dx.doi.org/10.21078/jssi-2018-097-23.
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Abstract This paper examines the extent of contagion and interdependence across the six Asian emerging countries stock markets (e.g., Bangladesh, China, India, Malaysia, the Philippine, and South Korea) and then try to quantify the extent of the Asian emerging market fluctuations which are described by intra-regional contagion effect. These markets experienced both fast growth and key upheaval during the sample period, and thus, provide potentially rich information on the nature of border market interactions. Using the daily stock market index data from January 2002 to December 2016 (breaking the 15 years data set into three sub periods; pre-crisis, crisis, and post crisis periods); particularly make attention to the global financial crisis of 2007∼2008. The return and volatility spillovers are modeled through the GARCH (generalized autoregressive conditional heteroscedasticity), pairwise Granger causality tests, and the forecast error variance decomposition in a generalized VAR (vector auto regression) models. This paper shows that volatility and return spillovers behave very differently over time, during the pre-crisis, crisis, and post crisis periods. Importantly, Asian emerging stock markets interaction is less before the global financial crisis period. The return and volatility spillover indices touch their respective historical peaks during the global financial crisis 2007∼2008, however Bangladeshi market faces this condition in 2009∼2010.
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Haider, Syed Kamran Ali, Shujahat Haider Hashmi und Ishtiaq Ahmed. „Systematic risk factors and stock return volatility“. Applied Studies in Agribusiness and Commerce 11, Nr. 1-2 (30.06.2017): 61–70. http://dx.doi.org/10.19041/apstract/2017/1-2/8.
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This study analyzes the transmission of systematic risk exhaling from macroeconomic fundamentals to volatility of stock market by using auto regressive generalized auto regressive conditional heteroskedastic (AR-GARCH) and vector auto regressive (VAR) models. Systematic risk factors used in this study are industrial production, real interest rate, inflation, money supply and exchange rate from 2000-2014. Results indicate that there exists relationship among the volatility of macroeconomic factors and that of stock returns in Pakistan. The relationship among the volatility of macroeconomic variables and that of stock returns is bidirectional; both affect each other in different dynamics.
JEL code: C32, C58, G11, G12
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Prawirosaputro, Bima, und Yudith Dyah Hapsari. „THE EFFECTS OF RUPIAH CURRENCY, WORLD OIL PRICES, AND WORLD GOLD PRICE ON COMPOSITED STOCK PRICE INDEX (IHSG) IN 2016“. Jurnal Manajemen 14, Nr. 2 (03.12.2017): 144–51. http://dx.doi.org/10.25170/jm.v14i2.784.
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This study aims to determine and analyze the effects ofIndonesian Rupiah exchange rate, world oil prices, and world gold priceson IDX Composite in 2016. This research utilizedGeneralized Auto-Regressive Conditional Heteroscedasticity (GARCH) method on 234 daily observations throughout the whole year. This study results demonstrated that the Indonesian Rupiah exchange rate has a significant and negative impact, while the world gold and oil prices have a significant and positive impact on the IDX Composite in 2016. In addition to these results, the discovery and usage of GARCH method as the optimum econometric model found the variance of residuals of IDX Composite in 2016, which is also affected by the previous day residuals, but it is not affected by the variance of previous days residuals.
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Rahardjo, Soemarso Slamet. „The Role of Speculative Factor in the Indonesian Stock Price Determination“. Economics and Finance in Indonesia 61, Nr. 1 (11.04.2015): 69. http://dx.doi.org/10.7454/efi.v61i1.498.
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This study observes the speculative element in the price determination and its mean reverting pattern. The existence of speculative element in the Indonesian stock market price determination was proven. Exponential Generalized Auto Regressive Conditional Heteroscedasticity (EGARCH) method indicates the non-stationary process of the residuals. There are systematic as well as unsystematic component embedded in the speculative behavior. Vector Error Correction Model (VECM) concludes that prices contain volatilities in the short run, but, it will revert to the mean in the long run. Investors’ behavior are neutral toward expected gain vis a vis losses in a stock trading.
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CHANG, B., und H. TSAI. „Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity“. Expert Systems with Applications 34, Nr. 2 (Februar 2008): 925–34. http://dx.doi.org/10.1016/j.eswa.2006.10.034.
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Shahateet, Mohammed, Najib Shrydeh und Suleiman Mohammad. „Testing the linkages of Arab stock markets: a multivariate GARCH approach“. Investment Management and Financial Innovations 16, Nr. 4 (06.12.2019): 192–204. http://dx.doi.org/10.21511/imfi.16(4).2019.17.
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The authors undertook to examine 720 monthly observations of activity in 15 Arab stock markets over four years (from 2014 to 2017) to identify the dynamic linkages among those markets. To achieve this, several forms of the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model were utilized. Both panel and individual stationarity, in addition to cointegration tests, were employed to highlight the interaction between these markets. The results suggest that Arab stock markets have weak linkages with the exception of those of the Gulf Cooperation Council (GCC). The authors also find out that the TARCH, EGARCH, PARCH, and Component GARCH (1,1) models are suitable in terms of passing the econometric analysis tests. Nevertheless, they conclude that the EGARCH model is the most appropriate for capturing the cross-market dynamic linkages, thereby outperforming the other GARCH specifications under study. The empirical findings bear special implications for economic literature regarding linkages of stock markets in the Arab world.
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Rostan, Pierre, Alexandra Rostan und Mohammad Nurunnabi. „Options trading strategy based on ARIMA forecasting“. PSU Research Review 4, Nr. 2 (07.06.2020): 111–27. http://dx.doi.org/10.1108/prr-07-2019-0023.
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Purpose The purpose of this paper is to illustrate a profitable and original index options trading strategy. Design/methodology/approach The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented. Findings The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading. Originality/value The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.
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Naik, Nagaraj, und Biju R. Mohan. „Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market“. Mathematics 9, Nr. 14 (07.07.2021): 1595. http://dx.doi.org/10.3390/math9141595.
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Volatility is the degree of variation in the stock price over time. The stock price is volatile due to many factors, such as demand, supply, economic policy, and company earnings. Investing in a volatile market is riskier for stock traders. Most of the existing work considered Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) models to capture volatility, but this model fails to capture when the volatility is very high. This paper aims to estimate the stock price volatility using the Markov regime-switching GARCH (MSGARCH) and SETAR model. The model selection was carried out using the Akaike-Informations-Criteria (AIC) and Bayesian-Information Criteria (BIC) metric. The performance of the model is evaluated using the Root mean square error (RMSE) and mean absolute percentage error (MAPE) metric. We have found that volatility estimation using the MSGARCH model performed better than the SETAR model. The experiments considered the Indian stock market data.
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Panda, Ajaya Kumar, und Swagatika Nanda. „Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies“. International Journal of Managerial Finance 14, Nr. 2 (03.04.2018): 245–62. http://dx.doi.org/10.1108/ijmf-11-2016-0206.
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Purpose The purpose of this paper is to capture the pattern of return volatility and information spillover and the extent of conditional correlation among the stock markets of leading South American economies. It also examines the connectedness of market returns within the region. Design/methodology/approach The time series properties of weekly stock market returns of benchmark indices spanning from the second week of 1995 to the fourth week of December 2015 are analyzed. Using univariate auto-regressive conditional heteroscedastic, generalized auto-regressive conditional heteroscedastic, and dynamic conditional correlation multivariate GARCH model approaches, the study finds evidence of returns and volatility linkages along with the degree of connectedness among the markets. Findings The findings of this study are consistent with increasing market connectedness among a group of leading South American economies. Stocks exhibit relatively fewer asymmetries in conditional correlations in addition to conditional volatility; yet, the asymmetry is relatively less apparent in integrated markets. The results demonstrate that co-movements are higher toward the end of the sample period than in the early phase. The stock markets of Argentina, Brazil, Chile, and Peru are closely and strongly connected within the region followed by Colombia, whereas Venezuela is least connected with the group. Practical implications The implication is that foreign investors may benefit from the reduction of the risk by adding the stocks to their investment portfolio. Originality/value The unique features of the paper include a large sample of national stock returns with updated time series data set that reveals the time series properties and empirical evidence on volatility testing. Unlike other studies, this paper uncovers the relation between the stock markets within the same region facing the same market condition.
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Ma, Lin, und Jean-Paul Delahaye. „An Algorithmic Look at Financial Volatility“. Algorithms 11, Nr. 11 (13.11.2018): 185. http://dx.doi.org/10.3390/a11110185.
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In this paper, we attempt to give an algorithmic explanation to volatility clustering, one of the most exploited stylized facts in finance. Our analysis with daily data from five exchanges shows that financial volatilities follow Levin’s universal distribution Kirchherr et al. (1997) once transformed into equally proportional binary strings. Frequency ranking of binary trading weeks coincides with that of their Kolmogorov complexity estimated byDelahaye et al. (2012). According to Levin’s universal distribution, large (resp. small) volatilities are more likely to be followed by large (resp. small) ones since simple trading weeks such as “00000” or “11111” are much more frequently observed than complex ones such as “10100” or “01011”. Thus, volatility clusters may not be attributed to behavioral or micro-structural assumptions but to the complexity discrepancy between finite strings. This property of financial data could be at the origin of volatility autocorrelation, though autocorrelated volatilities simulated from Generalized Auto-Regressive Conditional Heteroskedacity (hereafter GARCH) cannot be transformed into universally distributed binary weeks.
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Baryshych, Luka, und Dieudonne Dusengumukiza. „GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY MODELING OF ONEYEAR MATURITY GOVERNMENT BONDS OF GREECE DURING SOVEREIGN DEBT CRISIS OF EUROZONE IN 2010“. Scientific Bulletin of Mukachevo State University. Series “Economics” 1(13) (2020): 184–91. http://dx.doi.org/10.31339/2313-8114-2020-1(13)-184-191.
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ination of international trade imbalances, the impact of the global crisis from 2007 to 2012, failure in bailout approaches of European governments that troubled banking industries and private bondholders, high-risk lending and borrowing policies enforced by unrestricted credit requirements during the period from 2002 to 2008 and fiscal policy choices related to government revenues and expenses. The objective is to model the boiling state of the Greek local financial market before the peak of the Sovereign Debt Crisis of Eurozone in 2009, modelling the insights of foreign investors and credit rating organizations. We will identify a set of primary risk factors and their effect on both the local economy and the markets involved to validate the analysis done. In this paper will use both statistical analysis and macroeconomic data modelling techniques to identify a set of primary risk factors or economic variables and their effect on both the local economy of Greece and the markets involved. The selected method of modeling is Generalized autoregressive conditional heteroskedasticity models. The research is based on the data provided by World Bank Data Portal. Results obtained are fitted of 2006-2009 years data Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, forecasting market volatility in 2010 and on. We have discovered, that the Auto Regressive Integrated Moving Average model is not suitable for this problem as there was no notable autocorrelation. The volatility seems to fade out. This observation coincides with reality, as the crisis is about to peak and descend. Systemic risk indicators, primarily used for forecasting state-wide risk, are usually built on insider data of rating agencies or financial institutions. In this paper we obtain results close to Systemic Stress Indicator provided by European Central Bank (ECB) using ARCH and GARCH models on public data. The practical importance is model generation principle, which allows creating a risk indicator based on public financial data. Key words: economy, Single Financial Market, macroeconomic models, commodities prices, risk indicators.
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Nusantara, Aji Cahya, und Budhi Haryanto. „The Influence of Sex Appeal on Consumers Attitude toward the Ads Moderated by Product Factors“. Jurnal Dinamika Manajemen 9, Nr. 2 (27.09.2018): 250–58. http://dx.doi.org/10.15294/jdm.v9i2.15938.
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This study wants to examine the relationship between sex appeal and attitude towards the ads, and more than, this study also wants to examine the role of product factors in moderating the relationship of this two variables. Experimental design is done to control the relation among the variables observed in this study. The participants consist of 100 males’ undergraduate students of Faculty of Economics and Business, Universitas Sebelas Maret, Surakarta-Indonesia, who are divided into 4 groups. Generalized Auto Regression Conditional Hetero-regressive (GARCH) is statistical method chosen to analysis the data. The results showed that sex appeal is an effective stimulus affects the individual positive attitude toward an advertisement. As well as product factor is another stimulus, which effectively influence positive attitudes toward advertising. But in this study also found that the product factor is not moderate the relationship between sex appeal and positive attitude toward advertising. In addition to this study also discusses the implications of both theoretical and practical, as well as the limitations of the study.
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Kang, Seok-Kyu. „A Study on the Price Discovery in Korea Stock Index Markets: KODEX200, KOSPI200, and KOSPI200 Futures“. Journal of Derivatives and Quantitative Studies 17, Nr. 3 (31.08.2009): 67–97. http://dx.doi.org/10.1108/jdqs-03-2009-b0003.
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This paper examines the price discovery process among the Korea stock index markets using the vector error correction model (VECM) and the multivariate generalized auto regressive conditional heteroskedasticity (M-GARCH) model. The minute-by-minute price series of the KOSPI200 index, KOSPI200 futures, and KODEX200 are cointegrated. The empirical results are summarized as follows: First, VECM estimation results indicate that when the cointegrating relationship is perturbed by the arrival of ntis, the KODEX200(ETF) does not adjusted to restore equilibrium. This is the task of the KOSPI200 futures and spot. These two index securities use the KODEX200 to represent the ntioequilibrium price, with the KOSPI200 futures responding faster than the KOSPI200 spot. When the cointegrating relationship betweeiesOSPI200 spot and futues is perturbed by the arrival of ntis, the KOSPI200 spot does adjusted to restore equilibrium. Next, the results from the multivariate GARCH modes indicate that the volatilities of esOSPI200 spot and futures markets suggest unidirectiona1volatility spillover from KOSPI200 futures to KOSPI200 spot. KODEX200(ETF) volatilities spill over bothesOSPI200 spot and futures markets. and this happen in the reverse direction with a strong effect from the KODEX200 to KOSP200 futures and spot. The overall findings indicate that the KODEX200(ETF) market dominates KOSPI200 futures and spot in the price discovery process. The regulation of Instutional traders on trading on futures markets explains its superior price discovery function.
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Aftab, Muhammad, und Ijaz Ur Rehman. „Exchange rate risk and the bilateral trade between Malaysia and Singapore“. Studies in Economics and Finance 34, Nr. 3 (07.08.2017): 407–26. http://dx.doi.org/10.1108/sef-08-2015-0188.
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Purpose This paper aims to examine the influence of exchange rate risk on the bilateral trade of two closely connected East Asian open economies – Malaysia and Singapore – at industry level. Design/methodology/approach This study estimates import and export demand models considering 65 import and 65 export industries of Malaysia, with Singapore using monthly data over the period 2000-2014. Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is used to measure the exchange rate risk, and autoregressive distributed lag (ARDL) approach to co-integration is used to examine the study empirical models. Findings The findings suggest that exchange risk has an impact on a moderate number of industries in the short run; however, this influence endures in very few industries in the long run. It is interesting to note that exchange rate volatility expedites import demand for the large Malaysian import industries like gas and plastic. Originality/value No prior study has explored the topic at industry level focusing on the bilateral trade flows between Malaysia and Singapore. This research serves important implications while thinking about exchange rate risk and trade linkage in a case of open economies trade pairs that are highly integrated in presence of a variety of bilateral trade agreements and economic groupings.
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Osahon Osazevbaru, Henry, und Emmanuel Mitaire Tarurhor. „Unobservable characteristics of board directors and the performance of financial services firms in Nigeria“. Investment Management and Financial Innovations 17, Nr. 4 (18.12.2020): 378–88. http://dx.doi.org/10.21511/imfi.17(4).2020.32.
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This paper examines the intricate link between unobservable characteristics of directors on the corporate board and firm performance. It aims to extend the literature on corporate governance and firm strategic performance from the perspective of emerging African economies. A mix of performance measures were used (Tobin Q, return on assets, and share price) and unobservable characteristics were captured as a stochastic element or heterogeneity of observable board characteristics (board activity, gender diversity, size, and independence). The study applied non-linear generalized auto-regressive conditional heteroscedasticity model to examine the data set consisting of 299 firm-year observations from 23 financial firms listed on the Nigerian Stock Exchange from 2006 to 2018. Positive skewness and leptokurtic distribution were found for all the variables. Correlation matrix revealed no multicollinearity, as the highest value was 0.2386. Empirical results suggest that unobservable characteristics significantly and positively influence firm performance as measured by return on assets and share price. This is because the coefficient of the lagged-value of the variance scaling parameter is positive and significant at the 1% level. However, with respect to Tobin Q measure, the result was positive but not significant at the 5% level. Implicitly, the result is sensitive to performance proxies. Accordingly, this study concludes that unobservable characteristics drive firm performance. It is recommended that boards and regulators should pay attention to unobservable characteristics.
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Yavas, Burhan F., und Fahimeh Rezayat. „Country ETF returns and volatility spillovers in emerging stock markets, Europe and USA“. International Journal of Emerging Markets 11, Nr. 3 (18.07.2016): 419–37. http://dx.doi.org/10.1108/ijoem-10-2014-0150.
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Purpose – The purpose of this paper is to investigate the linkages among equity exchange traded funds (ETF) returns and transmission of volatilities of the USA, Europe and key emerging countries’ stock markets. Standard & Poor’s 500 (spy) and iShares Europe are used to represent the USA and European stock markets, the emerging market part of the data set consists of daily returns of equity ETF representing broad equity market indices of the BRIC countries (Brazil, Russia, India and China); the mist countries (Mexico, Indonesia, South Korea and Turkey) and South Africa and covers the period of February 3, 2012-February 28, 2014. Design/methodology/approach – The paper utilizes multi-variate auto-regressive moving-averages (MARMA) methodology to study equity market returns and spillovers. Second, generalized auto-regressive conditional heteroskadasticity (GARCH) modeling is employed to model volatility persistence and transmissions. Findings – The findings include the existence of significant co-movement of returns among all country ETFs; however, despite increasing interdependencies among the global stock markets there are still very good opportunities for diversification. For example, USA and Europe based investors may do well to ignore opportunities in each other’s markets but can realize diversification benefits by investing in ETFs representing China, South Africa and Turkey. As far as volatilities are concerned, the findings indicate that no ETF volatility is transmitted from the sample countries to USA, Brazil, China and South African stock markets. Also, US market volatility is transmitted to India, Russia, Mexico and Turkey while European volatility spills over to Mexico and South Korea. The presence of spillovers among stock markets’ return series and persistence of volatilities are useful to investors interested in diversifying their portfolios and to traders/fund managers who are interested in maximizing returns. Research limitations/implications – The implications include: first, investors should not only rely on current domestic news to guide their investment decisions, but also take into consideration international news for there are substantial spillovers. Second, given that volatilities can proxy for risk, there are lessons for both individual and institutional investors in terms of further examining pricing securities, hedging and other trading strategies as well as framing regulatory policies. Third, investors should be able to ride the financial cycle by following closely monetary policies of the FED and European Central Bank and resulting credit expansion or contraction since research indicates (and as corroborated in this study) equity prices are linked to VIX which is also correlated with capital flows and credit expansion and interest rates. Limitations include: first, the investigation could be expanded to include individual countries in Europe instead of using one Europe-wide ETF. As ETFs for other emerging markets become available it is also possible to include additional countries. Second, ETFs may not be the best vehicles for diversification. Originality/value – Methodology (MARMA and GARCH) is widely used for analyzing financial data. The use of BRIC and MIST countries and the interaction among them may be novel. Spillovers among emerging financial markets is a fairly new area. Typically, the authors see studies of spillovers from the developed countries to the developing ones. The data period is important since it covers both credit expansion and contraction (or the start of it) by the FED and is current.
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Chaffai, Mustapha, und Imed Medhioub. „Herding behavior in Islamic GCC stock market: a daily analysis“. International Journal of Islamic and Middle Eastern Finance and Management 11, Nr. 2 (18.06.2018): 182–93. http://dx.doi.org/10.1108/imefm-08-2017-0220.
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Purpose This paper aims to examine the presence of herd behaviour in the Islamic Gulf Cooperation Council (GCC) stock markets following the methodology given by Chiang and Zheng (2010). Generalized auto regressive conditional heteroskedasticity (GARCH)-type models and quantile regression analysis are used and applied to daily data ranging from 3 January 2010 to 28 July 2016. Results show evidence of herd behaviour in the GCC stock markets. When the data are divided into down and up market periods, herd information is found to be statistically significant and negative during upward market periods only. These results are similar to those reported in some emerging markets such as China, Japan and Hong Kong, where stock returns perform more similarly during down market periods and differently during rising markets. Design/methodology/approach The authors present a brief literature on herd behaviour. Second, the authors provide some specificity of the GCC Islamic stock market, followed by the presentation of the methodology and the data, results and their interpretation. Findings The authors take into account the difference existing in market conditions and find evidence of herding behaviour during rising markets only for GCC markets. This result was confirmed after using the quantile regression method, as evidence of herding was observed only in highly extreme periods. Stock returns perform more similarly when market is down in Islamic GCC stock market. Research limitations/implications The research limitation consists in the fact that this work can be extended to compare the GCC stock markets with other markets in Asia such as Malaysia and Indonesia. Practical implications The principal implication consists in the fact that herding behaviour is limited in the GCC markets and Islamic finance can have an important contribution to moderate the behaviour in the financial markets. Social implications The work focusses on the role of ethics in the financial markets and their ability to reduce the impact of behavioural biases. Originality/value The paper studies the behaviour of investors in the Islamic financial markets and gives an idea about the importance of the behaviour in this particular market regarding its characteristics.
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Dzingirai, Canicio, und Nixon S. Chekenya. „Longevity swaps for longevity risk management in life insurance products“. Journal of Risk Finance 21, Nr. 3 (27.06.2020): 253–69. http://dx.doi.org/10.1108/jrf-05-2019-0085.
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Purpose The life insurance industry has been exposed to high levels of longevity risk born from the mismatch between realized mortality trends and anticipated forecast. Annuity providers are exposed to extended periods of annuity payments. There are no immediate instruments in the market to counter the risk directly. This paper aims to develop appropriate instruments for hedging longevity risk and providing an insight on how existing products can be tailor-made to effectively immunize portfolios consisting of life insurance using a cointegration vector error correction model with regime-switching (RS-VECM), which enables both short-term fluctuations, through the autoregressive structure [AR(1)] and long-run equilibria using a cointegration relationship. The authors also develop synthetic products that can be used to effectively hedge longevity risk faced by life insurance and annuity providers who actively hold portfolios of life insurance products. Models are derived using South African data. The authors also derive closed-form expressions for hedge ratios associated with synthetic products written on life insurance contracts as this will provide a natural way of immunizing the associated portfolios. The authors further show how to address the current liquidity challenges in the longevity market by devising longevity swaps and develop pricing and hedging algorithms for longevity-linked securities. The use of a cointergrating relationship improves the model fitting process, as all the VECMs and RS-VECMs yield greater criteria values than their vector autoregressive model (VAR) and regime-switching vector autoregressive model (RS-VAR) counterpart’s, even though there are accruing parameters involved. Design/methodology/approach The market model adopted from Ngai and Sherris (2011) is a cointegration RS-VECM for this enables both short-term fluctuations, through the AR(1) and long-run equilibria using a cointegration relationship (Johansen, 1988, 1995a, 1995b), with a heteroskedasticity through the use of regime-switching. The RS-VECM is seen to have the best fit for Australian data under various model selection criteria by Sherris and Zhang (2009). Harris (1997) (Sajjad et al., 2008) also fits a regime-switching VAR model using Australian (UK and US) data to four key macroeconomic variables (market stock indices), showing that regime-switching is a significant improvement over autoregressive conditional heteroscedasticity (ARCH) and generalised autoregressive conditional heteroscedasticity (GARCH) processes in the account for volatility, evidence similar to that of Sherris and Zhang (2009) in the case of Exponential Regressive Conditional Heteroscedasticity (ERCH). Ngai and Sherris (2011) and Sherris and Zhang (2009) also fit a VAR model to Australian data with simultaneous regime-switching across many economic and financial series. Findings The authors develop a longevity swap using nighttime data instead of usual income measures as it yields statistically accurate results. The authors also develop longevity derivatives and annuities including variable annuities with guaranteed lifetime withdrawal benefit (GLWB) and inflation-indexed annuities. Improved market and mortality models are developed and estimated using South African data to model the underlying risks. Macroeconomic variables dependence is modeled using a cointegrating VECM as used in Ngai and Sherris (2011), which enables both short-run dependence and long-run equilibrium. Longevity swaps provide protection against longevity risk and benefit the most from hedging longevity risk. Longevity bonds are also effective as a hedging instrument in life annuities. The cost of hedging, as reflected in the price of longevity risk, has a statistically significant effect on the effectiveness of hedging options. Research limitations/implications This study relied on secondary data partly reported by independent institutions and the government, which may be biased because of smoothening, interpolation or extrapolation processes. Practical implications An examination of South Africa’s mortality based on industry experience in comparison to population mortality would demand confirmation of the analysis in this paper based on Belgian data as well as other less developed economies. This study shows that to provide inflation-indexed life annuities, there is a need for an active market for hedging inflation in South Africa. This would demand the South African Government through the help of Actuarial Society of South Africa (ASSA) to issue inflation-indexed securities which will help annuities and insurance providers immunize their portfolios from longevity risk. Social implications In South Africa, there is an infant market for inflation hedging and no market for longevity swaps. The effect of not being able to hedge inflation is guaranteed, and longevity swaps in annuity products is revealed to be useful and significant, particularly using developing or emerging economies as a laboratory. This study has shown that government issuance or allowing issuance, of longevity swaps, can enable insurers to manage longevity risk. If the South African Government, through ASSA, is to develop a projected mortality reference index for South Africa, this would allow the development of mortality-linked securities and longevity swaps which ultimately maximize the social welfare of life assurance policy holders. Originality/value The paper proposes longevity swaps and static hedging because they are simple, less costly and practical with feasible applications to the South African market, an economy of over 50 million people. As the market for MLS develops further, dynamic hedging should become possible.
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Amo Baffour, Alexander, Jingchun Feng, Liwei Fan und Beryl Adormaa Buanya. „Forecasting Volatility Returns of Oil Price Using Gene Expression Programming Approach“. Journal of Time Series Econometrics 11, Nr. 2 (04.01.2019). http://dx.doi.org/10.1515/jtse-2017-0022.
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AbstractThis study employs four (4) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) variants namely GARCH (1, 1), Glosten–Jagannathan–Runkle (GJR), Auto Regressive Integrated Moving Average (ARIMA)-GARCH and ARIMA-GJR as benchmark models to assess the performance of a proposed novel Gene Expression Programming (GEP) based univariate time series modeling approach used to conduct ex ante oil price volatility forecasts. The report illustrates that the GEP model is more superior to any of the traditional models on issues relating to both loss functions applied. The GEP model is of a greater volatility forecasting precision at different forecast horizons, therefore. There is also the existence of evidence that GJR and ARIMA-GJR differ in their loss functions, the performance is nevertheless better than GARCH (1, 1) and ARIMA-GARCH. This study conducted herein achieves importance in literature by broadening the application of gene algorithms in finance and forecasting. It also solves the problem of high error associated with the use of GARCH related models in oil price volatility forecasting.
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Nurlita, Vina, und Prima Naomi. „Do Political Events Affect Stock Return Volatility On Indonesian Stock Exchange“. Journal of Economics, Business & Accountancy Ventura 22, Nr. 1 (18.06.2019). http://dx.doi.org/10.14414/jebav.v22i1.1215.
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This study has the purpose to examine the influence of political events on the volatility of stocks traded on the Indonesia Stock Exchange (IDX). Furthermore, this study also sees whether such political events also influence the shares that have direct links with the participants in presidential elections. The political event that was examined was the Indonesian Presidential Election held in 2014. We use the daily data on the shares of all companies listed on the Indonesia Stock Exchange (IDX) in 2014. Analysis and hypothesis testing were performed using the GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) estimation and its derivatives namely EGARCH (Exponential GARCH) and TARCH (Threshold GARCH). This study findings that the 2014 Presidential Election asymmetrically affected stock return volatility on IDX and contrary to the leverage effect, which means that positive shocks (good news) have better influence than negative shocks (bad news). Out of all listed companies that have direct links with participants in the presidential election, 3 companies have their stock volatility affected by this Presidential Election; some with symmetric effect and some others with asymmetric effect.
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Wang, Shizhen, und David Hartzell. „What influences real estate volatility in Hong Kong? An ARMA-GARCH approach“. International Journal of Housing Markets and Analysis ahead-of-print, ahead-of-print (17.02.2021). http://dx.doi.org/10.1108/ijhma-08-2020-0099.
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PurposeThis paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories in Hong Kong were analyzed from February 1993 to February 2019 to test whether volatility clusters are present in the real estate market. Real estate price determinants were also investigated.Design/methodology/approachAutoregressive conditional heteroscedasticity–Lagrange multiplier test is used to examine the volatility clustering effects in these four kinds of real estate. An autoregressive and moving average model–generalized auto regressive conditional heteroskedasticity (GARCH) model was used to identify real estate price volatility determinants in Hong Kong.FindingsThere was volatility clustering in all four kinds of real estate. Determinants of price volatility vary among different types of real estate. In general, housing volatility in Hong Kong is influenced primarily by the foreign exchange rate (both RMB and USD), whereas commercial real estate is largely influenced by unemployment. The results of the exponential GARCH model show that there were no asymmetric effects in the Hong Kong real estate market.Research limitations/implicationsThis volatility pattern has important implications for investors and policymakers. Residential and commercial real estate have different volatility determinants; investors may benefit from this when building a portfolio. The analysis and results are limited by the lack of data on real estate price determinants.Originality/valueTo the best of the authors’ knowledge, this paper is the first study that evaluates volatility in the Hong Kong real estate market using the GARCH class model. Also, this paper is the first to investigate commercial real estate price determinants.
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Wijeyakulasuriya, D. A., und W. N. Wickremasinghe. „Measuring Extreme Market Risk: The Sri Lankan Context“. Asia-Pacific Journal of Risk and Insurance 9, Nr. 2 (01.01.2015). http://dx.doi.org/10.1515/apjri-2014-0026.
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AbstractMany empirical studies have been carried out in developed and emerging markets using methods to account for extreme market risk. A front-runner in the methods used is Extreme Value Theory (EVT). In this study, these methods are applied to the All Share Price Index (ASPI) of the Colombo Stock Exchange (CSE) to obtain risk forecasts for VaR (Value at Risk) and ES (Expected Shortfall) at 99% confidence level. Recognizing the merits of both conditional and unconditional models for risk measures the most suitable unconditional model and conditional model are found for the ASPI. Contrary to other studies the Historical Simulation method was found to be more appropriate for obtaining static estimates than the static EVT model. The Two-Step Approach of McNeil and Frey which combines EVT and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) methodologies was found to be the most appropriate conditional model. Backtesting of models is done using a binomial test, Christoferssen’s conditional coverage test and McNeil and Frey’s ES test.
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Kashif, Muhammad, Asra Shaikh und Mobeen Ur Rehman. „ARE FINANCIAL INVESTORS PRONE TO EXOGENOUS (CRICKET) SENTIMENTS THAT AFFECTS EQUITY INVESTMENT DECISION AND INDUCES VOLATILITY IN STOCK MARKET?“ Studies of Applied Economics 38, Nr. 2 (27.05.2020). http://dx.doi.org/10.25115/eea.v38i2.3190.
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This paper examines the volatility in stock returns due to mood-swings of financial investors affected by the outcome of one-day international (ODI) cricket matches played by Pakistan against cricketing nations. The impact of matches is analyzed on same-day and for next-day volatility in returns by using Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH 1,1) and Glosten, Jagannathan & Runkle (GJR 1,1) methodology, supported by Engle (arch), L-Jung Q-stats (auto-correlation) and Jarque-Bera (normality) tests. Empirical time-series results show volatility can be predicted through past volatility and can be generalized. The win or loss position of Pakistan in ODI has a significant influence on next day volatility of stock returns. However, GJR analysis provides strong evidence of asymmetric behavior on next day in Karachi Stock Exchange (KSE)-100 index, states bad-news resulting from ODI matches has a significant negative influence on the next-day volatility of stock returns, due to less trading on the subsequent day of the match.