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Published: 4 June 2021
Last updated: 20 February 2023

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1

Sucarrat, Genaro. "garchx: Flexible and Robust GARCH-X Modeling." R Journal 13, no. 1 (2021): 276. http://dx.doi.org/10.32614/rj-2021-057.

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2

Teulon, Frederic, Khaled Guesmi, and Saoussen Jebri. "Risk Analysis Of Hedge Funds: A Markov Switching Model Analysis." Journal of Applied Business Research (JABR) 30, no. 1 (December 30, 2013): 243. http://dx.doi.org/10.19030/jabr.v30i1.8299.

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The paper applies Markov Regime Switching GARCH Model (SW-GARCH) to investigate the volatility behavior of strategies hedge fund monthly returns for the period 1997-2011. The results highlight two different regimes: The first regime is characterized by a high volatility for all strategies hedge fund monthly returns. The second is characterised by lower volatility and positive average returns (except Emerging Market strategy). Our results helped to capture even the short-lived crises along with the material crises of 2001 and 2008.
3

WU, EDMOND H. C., PHILIP L. H. YU, and W. K. LI. "VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS." International Journal of Neural Systems 16, no. 05 (October 2006): 371–82. http://dx.doi.org/10.1142/s0129065706000779.

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We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.
4

Ma, Dan, Tianxing Yang, Liping Liu, and Yi He. "Analysis of Factors Influencing Stock Market Volatility Based on GARCH-MIDAS Model." Complexity 2022 (January 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/6176451.

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This paper further extends the existing GARCH-MIDAS model to deal with the effect of microstructure noise in mixed frequency data. This paper has two highlights. First, according to the estimation of the long-term volatility components of the GARCH-MIDAS model, rAVGRV is adopted to substitute for the RV estimator. rAVGRV uses the rich data sources in tick-by-tick data and significantly corrects the impact of the microstructure noise on volatility estimation. Second, besides introducing macroeconomic variables (i.e., macroeconomic consistency index (MCI), deposits in financial institutions (DFI), industrial value-added (IVA), and M2), Chinese Economic Policy Uncertainty (CEPU) index and Infectious Disease Equity Market Volatility Tracker (EMV) are introduced in the long-run volatility component of the GARCH-MIDAS model. As indicated by the results of this paper, the rAVGRV-based GARCH-MIDAS is slightly better than the RV model-based GARCH-MIDAS. In addition to the common macroeconomic variables significantly impacting stock market volatility, CEPU also substantially impacts stock market volatility. Nevertheless, the effect of EMV on the stock market is insignificant.
5

Liesl le Roux, Corlise. "Co-Movement and Volatility Analysis of Sugar: Spot and Future." International Journal of Business Administration and Management Research 4, no. 2 (June 23, 2018): 1. http://dx.doi.org/10.24178/ijbamr.2018.4.2.01.

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Co-movement and volatility analysis between variables are an important considerations in investment related decisions. The relationships of spot and two future priced sugar contracts are examined against the currency and main index of Brazil, China, Colombia, India, Indonesia, Mexico, Pakistan, Philippines and Thailand. Sugar which is produced in many countries around the world is the world's largest crop by production in metric tons. Co-movement and volatility analysis includes correlation, vector autoregression, Johansen cointegration test, impulse responses, pairwise Granger causality test and three GARCH models. The three GARCH models are the GARCH, GJR-GARCH and EGARCH models. A long run relationships exists between the three sugar variables, the three sugar variables and the Shanghai SE A Share Index; as well as between the tree sugar variables and the Thai Bhat. Co-movement results indicate that unidirectional and bidirectional relationships exist among the variables. A bi-directional relationship exists between sugar spot and CSCE sugar 11 future as well as between sugar spot and Liffe sugar future. Sugar spot and sugar future have a uni-directional relationship with the indices of Bangkok, Indonesia, Philippines, China, and India. Sugar spot and sugar futures Granger causes the currencies of Brazil, Colombia, Indonesia, Philippines, Thailand and India. The volatility analysis done shows that the AIC and SIC results of the GARCH models which indicates that the original GARCH model fits the data the best for sugar spot and the CSCE sugar 11 future. The EGARCH model fits the data the best for Liffe sugar future.
6

Li, Yuanbo, Chi Tim Ng, and Chun Yip Yau. "GARCH-type factor model." Journal of Multivariate Analysis 190 (July 2022): 105001. http://dx.doi.org/10.1016/j.jmva.2022.105001.

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7

Yu, Zhi Tao. "Gold Investment Risk Analysis Model Based on Time Series." Advanced Materials Research 926-930 (May 2014): 3834–37. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3834.

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With the growing size of the gold market, all kinds of gold investment varieties are constantly emerging, namely, meet the residents needs and requirements of the investment risk, also makes the prime financial rise. This paper analyzes quantify the risk of gold market fundamentals, and has a deep research on the historical development of the global gold market, global gold market developing trends and factors affecting the gold price. This paper focuses on analysis of VAR risk management theory and VAR-GARCH model. VAR-GARCH model can be more effective on the VAR value forecast, which is a better way to estimate the gold market risk. In addition, VAR-GARCH conditional variance model is also analyzed, and high-risk the real market is the corresponding.
8

Fu, Sihan, Kexin He, Jialin Li, and Zheng Tao. "Exploring Apple’s Stock Price Volatility Using Five GARCH Models." Proceedings of Business and Economic Studies 5, no. 5 (October 21, 2022): 137–45. http://dx.doi.org/10.26689/pbes.v5i5.4322.

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The financial market is the core of national economic development, and stocks play an important role in the financial market. Analyzing stock prices has become the focus of investors, analysts, and people in related fields. This paper evaluates the volatility of Apple Inc. (AAPL) returns using five generalized autoregressive conditional heteroskedasticity (GARCH) models: sGARCH with constant mean, GARCH with sstd, GJR-GARCH, AR(1) GJR-GARCH, and GJR-GARCH in mean. The distribution of AAPL’s closing price and earnings data was analyzed, and skewed student t-distribution (sstd) and normal distribution (norm) were used to further compare the data distribution of the five models and capture the shape, skewness, and loglikelihood in Model 4 – AR(1) GJR-GARCH. Through further analysis, the results showed that Model 4, AR(1) GJR-GARCH, is the optimal model to describe the volatility of the return series of AAPL. The analysis of the research process is both, a process of exploration and reflection. By analyzing the stock price of AAPL, we reflect on the shortcomings of previous analysis methods, clarify the purpose of the experiment, and identify the optimal analysis model.
9

Mansur, Iqbal, Steven J. Cochran, and David Shaffer. "Foreign Exchange Volatility Shifts and Futures Hedging: An ICSS-GARCH Approach." Review of Pacific Basin Financial Markets and Policies 10, no. 03 (September 2007): 349–88. http://dx.doi.org/10.1142/s0219091507001112.

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In this study, the impact of volatility regime shifts on volatility persistence and hedge ratio estimation is determined for four major currencies using an iterated cumulative sums of squares (ICSS)-GARCH model. Employing a standard GARCH (1,1) model as the benchmark, within-sample results demonstrate that the inclusion of volatility shifts substantially reduces volatility persistence and the significance of the ARCH and GARCH coefficients. In terms of hedging effectiveness, the ICSS-GARCH model outperforms the standard GARCH model for all four currencies. In comparison to two constant volatility models, the standard GARCH model yields the lowest performance, whereas the ICSS-GARCH model performs at least as well as these models. In out-of-sample analysis, the GARCH model provides substantial variance reductions relative to the constant volatility models. Moreover, the ICSS-GARCH model yields positive variance reductions relative to all competing models, including the standard GARCH model. The results suggest that in cases where dynamic hedging is important, sudden shifts in volatility should not be ignored.
10

Choi, S. M., S. Y. Hong, M. S. Choi, J. A. Park, J. S. Baek, and S. Y. Hwang. "Analysis of Multivariate-GARCH via DCC Modelling." Korean Journal of Applied Statistics 22, no. 5 (October 31, 2009): 995–1005. http://dx.doi.org/10.5351/kjas.2009.22.5.995.

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11

Chiang, Thomas C., and Christine X. Jiang. "Empirical Analysis of Interdependency and Volatility among Asian Stock Markets." Review of Pacific Basin Financial Markets and Policies 01, no. 04 (December 1998): 437–59. http://dx.doi.org/10.1142/s0219091598000260.

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This paper presents an integrated time series model to analyze the interdependence and volatility among five major Asian stock markets, including Taiwan, Hong Kong, Korea, Singapore, and Japan. The model accounts for autoregression, cross correlation, error correction term, and GARCH effect. The evidence indicates that these five Asian stock markets follow at least one common stochastic trend. The stock returns for four of these Asian markets are contemporaneously correlated with those of Japan, while their correlations with the US stock returns take a one-day lag. Our evidence also shows some dynamic adjustment involving an error correcting process. Finally, the GARCH effect is present in all of the variance equations although we fail to find the GARCH-in-mean supported by the data.
12

Jati, Kumara. "Analysis of Sugar Prices Volatility Using ARMA and ARCH/GARCH." International Journal of Trade, Economics and Finance 5, no. 2 (2014): 136–41. http://dx.doi.org/10.7763/ijtef.2014.v5.356.

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13

Rahayu, Meinar Fithria, Wen-I. Chang, and Ratya Anindita. "Volatility Analysis and Volatility Spillover Analysis of Indonesia's Coffee Price Using Arch/Garch, and Egarch Model." Journal of Agricultural Studies 3, no. 2 (April 23, 2015): 37. http://dx.doi.org/10.5296/jas.v3i2.7185.

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This study aims to analyze the best model to expect volatility of Indonesia’s coffee price using ARCH/GARCH model and to measure the coffee price volatility spillover of International market for Indonesia’s coffee price using EGARCH model. These models use different conditional variance specifications to catch up the asymmetry. The empirical results show that GARCH (1.1) model seems to better describe the Indonesia’s coffee price volatility. From the EGARCH analysis known that International coffee price has an asymmetric effect on Indonesia’s return coffee price and indicate that domestic coffee market is not efficient.
14

Ou, Phich Hang, and Heng Shan Wang. "Applications of Neural Networks in Modeling and Forecasting Volatility of Crude Oil Markets: Evidences from US and China." Advanced Materials Research 230-232 (May 2011): 953–57. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.953.

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Previous researches on oil price volatility have been done with parametric models of GARCH types. In this work, we model volatility of crude oil price based on GARCH(p,q) by using Neural Network which is one of powerful classes of nonparametric models. The empirical analysis based on crude oil prices in US and China show that the proposed models significantly generate improved forecasting accuracy than the parametric model of normal GARCH(p,q). Among nine different combinations of hybrid models (for p = 1,2,3 and q = 1,2,3), it is found that NN-GARCH(1,1) and NN-GARCH(2,2) perform better than the others in US market whereas, NN-GARCH(1,1) and NN-GARCH(3,1) outperform in Chinese case.
15

Yuliana, Ashalia Fitri, and Robiyanto Robiyanto. "Revisit the Dynamic Portfolio Formation Between Gold and Stocks in Indonesia in The Period Before and During the COVID-19 Pandemic." Journal of Accounting and Strategic Finance 5, no. 1 (February 20, 2022): 1–21. http://dx.doi.org/10.33005/jasf.v5i1.161.

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This research aims to review the formation of the dynamic portfolio of individual stocks and gold using the DCC-GARCH and ADCC-GARCH analysis techniques in the periods before and during the COVID-19 pandemic. This is done so that individual investors and investment managers will be able to apply this method. This research uses data from the period of October 2019 - September 2020 with a research sample of nine stocks that are included in the IDX-30. The results showed that the DCC-GARCH analysis technique before the COVID-19 pandemic and the performance of the dynamic portfolios that were unhedged and hedged had no difference. This is due to the conditions in the period before the COVID-19 pandemic which still tended to be stable, thus, no safe-haven asset is needed. Meanwhile, in the period during the COVID-19 pandemic, using the DCC-GARCH analysis technique, there were differences because conditions have started to fluctuate in uncertainty which resulted in the need for safe-haven assets. On the other hand, using the ADCC-GARCH analysis technique on the periods before and during the COVID-19 pandemic, the performance of the dynamic portfolios that were unhedged and hedged showed a difference. Because the ADCC-GARCH technique is able to see asymmetric volatility for the future, adding gold to a portfolio can reduce risk when there is uncertainty. This research also found that the ADCC-GARCH technique had better performance than the DCC-GARCH technique.
16

Jinling, Liang, and Deng Guangming. "An Empirical Analysis on the Volatility of Return of CSI 300 Index." International Journal of Accounting and Finance Studies 4, no. 2 (October 18, 2021): p1. http://dx.doi.org/10.22158/ijafs.v4n2p1.

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In order to better observe the trend of the stock market, this paper selects the daily closing price data of CSI 300 index from April 12, 2016 to September 30, 2021, and makes an empirical analysis on the logarithmic return of CSI 300 index. It is found that: (1) the return series of the CSI 300 index shows the statistical characteristics of peak, thick tail, bias, asymmetry and persistence. The ARMA (2,3) model can effectively fit the yield series and predict the future trend to a certain extent. (2) The residuals of ARMA model show obvious cluster effect and ARCH effect (conditional heteroscedasticity). GARCH (1,1) model can better fit the conditional heteroscedasticity, so as to eliminate the ARCH effect. (3) By constructing GARCH (1,1) model, it is found that the sum of ARCH term coefficient and GARCH term coefficient is very close to 1, indicating that GARCH process is wide and stable, the impact on conditional variance is lasting, and the market risk is large, that is, the impact plays an important role in all future forecasts.
17

Dewia, Intani, Rita Nurmalina, Andriyono Kilat Adhi, and Bernhard Brümmer. "Price Volatility Analysis in Indonesian Beef Market." KnE Life Sciences 2, no. 6 (November 26, 2017): 403. http://dx.doi.org/10.18502/kls.v2i6.1062.

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The Indonesian beef price movement increasing erratically and tends to be volatile in recent years. Based on the price monitoring in several production centers, there are beef price fluctuations in the consumer level across time and between provinces. This study tries to present the relationship between the beef price volatility and Indonesia’s efforts to ensure food security through self-sufficiency in beef. We consider a series of consumer daily beef price from January 2006 to December 2013, with total T=2086 observations to understand beef price volatility in Indonesia, and to analyze the impact of beef self-sufficiency program to the beef price volatility in Indonesia. Data was obtained from Ministry of Trade, Government of Indonesia and it was collected through market survey from three different markets in 33 capital provinces in Indonesia. The methodology follows GARCH model to measure the beef price volatility. The GARCH (1.1) model gives information that beef price movements are influenced by the volatility from the previous period and yesterday’s variance. The volatility of beef price was driven more by its own variance rather than external shocks. GARCH (1.1) model shows that the beef price volatility will tend to be smaller and persistence in the future. Parameter of the third dummy variable in the variance equation to capture the change policy is statistically significant. It indicates that the beef self-sufficiency program may lower the beef price volatility. Keywords: beef price, garch model, price volatility, self sufficiency
18

Leung, Pui-Lam, and Wing-Keung Wong. "Three-factor profile analysis with GARCH innovations." Mathematics and Computers in Simulation 77, no. 1 (February 2008): 1–8. http://dx.doi.org/10.1016/j.matcom.2006.12.011.

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19

Klüppelberg, Claudia, Alexander Lindner, and Ross Maller. "A continuous-time GARCH process driven by a Lévy process: stationarity and second-order behaviour." Journal of Applied Probability 41, no. 3 (September 2004): 601–22. http://dx.doi.org/10.1239/jap/1091543413.

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We use a discrete-time analysis, giving necessary and sufficient conditions for the almost-sure convergence of ARCH(1) and GARCH(1,1) discrete-time models, to suggest an extension of the ARCH and GARCH concepts to continuous-time processes. Our ‘COGARCH’ (continuous-time GARCH) model, based on a single background driving Lévy process, is different from, though related to, other continuous-time stochastic volatility models that have been proposed. The model generalises the essential features of discrete-time GARCH processes, and is amenable to further analysis, possessing useful Markovian and stationarity properties.
20

Klüppelberg, Claudia, Alexander Lindner, and Ross Maller. "A continuous-time GARCH process driven by a Lévy process: stationarity and second-order behaviour." Journal of Applied Probability 41, no. 03 (September 2004): 601–22. http://dx.doi.org/10.1017/s0021900200020428.

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We use a discrete-time analysis, giving necessary and sufficient conditions for the almost-sure convergence of ARCH(1) and GARCH(1,1) discrete-time models, to suggest an extension of the ARCH and GARCH concepts to continuous-time processes. Our ‘COGARCH’ (continuous-time GARCH) model, based on a single background driving Lévy process, is different from, though related to, other continuous-time stochastic volatility models that have been proposed. The model generalises the essential features of discrete-time GARCH processes, and is amenable to further analysis, possessing useful Markovian and stationarity properties.
21

O O, Lawal, Nwakuya M T, and Biu O E. "Trend Analysis and GARCH Model for COVID-19 National Weekly Confirmed Cases in Nigeria for Abuja and Lagos State." Quarterly Journal of Econometrics Research 8, no. 1 (February 24, 2022): 1–10. http://dx.doi.org/10.18488/88.v8i1.2931.

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The trend analysis and GARCH model for COVID-19 pandemic spread between FCT/Lagos and the National Weekly confirmed pandemic cases were carried out using the statistical software Minitab17 and Gretl. Four models trend behavior were considered, which are linear, quadratic, cubic and quartic trends with respect to R-square value, Adjusted R-square value, Analysis of Variance (ANOVA) p-value and the estimated coefficients p-values. In addition, GARCH(0,1),GARCH(1,0) and GARCH(1,1) models were built separately for both FCT/Lagos on the Nigeria National Weekly confirmed pandemic cases; to determine which model has best fit for predicting weekly confirmed cases of COVID-19 pandemic in those areas. The four common information criteria was used to selected the best model, which are the Akaike Information Criteria (AIC), Schwarz-Bayesian Information Criteria (BIC), Hannan-Quinn Information Criteria (HQC) and Likelihood Criteria (LKH).This study established the quadratic trend and GARCH(1,0) as the best model that describes the data sets for FCT. Hence, both models can be used to forecasts the weekly pandemic confirmed cases in these areas.
22

Engle, Robert. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics." Journal of Economic Perspectives 15, no. 4 (November 1, 2001): 157–68. http://dx.doi.org/10.1257/jep.15.4.157.

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ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial applications. These models are especially useful when the goal of the study is to analyze and forecast volatility. This paper gives the motivation behind the simplest GARCH model and illustrates its usefulness in examining portfolio risk. Extensions are briefly discussed.
23

Fink, Holger, Andreas Fuest, and Henry Port. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates." Risks 6, no. 3 (August 20, 2018): 84. http://dx.doi.org/10.3390/risks6030084.

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A functional ARMA-GARCH model for predicting the value-at-risk of the EURUSD exchange rate is introduced. The model implements the yield curve differentials between EUR and the US as exogenous factors. Functional principal component analysis allows us to use the information of basically the whole yield curve in a parsimonious way for exchange rate risk prediction. The data analyzed in our empirical study consist of the EURUSD exchange rate and the EUR- and US-yield curves from 15 August 2005–30 September 2016. As a benchmark, we take an ARMA-GARCH and an ARMAX-GARCHX with the 2y-yield difference as the exogenous variable and compare the forecasting performance via likelihood ratio tests. However, while our model performs better in one situation, it does not seem to improve the performance in other setups compared to its competitors.
24

Lin, Feng. "Prediction and Analysis of Financial Volatility Based on Implied Volatility and GARCH Model." Modern Economics & Management Forum 3, no. 1 (February 28, 2022): 48. http://dx.doi.org/10.32629/memf.v3i1.650.

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Based on the data information of Shanghai and Shenzhen CSI 300 stock index futures, the performance of the GARCH model and implied volatility prediction in different time periods were studied. This paper mainly discusses the volatility of index returns and uses Matlab and Minitab to measure the performance of the GARCH model and implied volatility model in volatility prediction, and then comments on the prediction results. The results show that the GARCH model has a good prediction effect in the short term, while the implied volatility has a good prediction power in the long term. Option prices can mirror market information in a more comprehensive way. As a result, implied volatility is more reasonable to predict future volatility.
25

Wang, Yuling, Yunshuang Xiang, and Huan Zhang. "Comparison and Forecasting of VaR Models for Measuring Financial Risk: Evidence from China." Discrete Dynamics in Nature and Society 2022 (March 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/5510721.

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With increasing extremal risk, VaR has been becoming a popular methodology because it is easy to interpret and calculate. For comparing the performance of extant VaR models, this paper makes an empirical analysis of five VaR models: simple VaR, VaR based on RiskMetrics, VaR based on different distributions of GARCH-N, GARCH-GED, and GARCH-t. We exploit the daily closing prices of the Shanghai Composite Index from January 4, 2010, to April 8, 2020, and divide the entire sample into two periods for empirical analysis. The rolling window is used to update the daily estimation of risk. Based on the failure rates under different significance levels, we test whether a specific VaR model passes the back-testing. The results indicate that all models, except the RiskMetrics model, pass the test at a 5% level. According to the ideal failure rate, only the GARCH-GED model can pass the test at a 1% level. For the Kupiec confidence interval, the GARCH-t model can also pass the back-testing at all aforementioned levels. Particularly, we find that the GARCH-GED model has the lowest forecasting failure rate in the class of GARCH models.
26

Nuryatin, Atin. "Comparative Analysis of ARIMA and GARCH Methods to Predict Stock Prices." Almana : Jurnal Manajemen dan Bisnis 4, no. 3 (December 17, 2020): 405–15. http://dx.doi.org/10.36555/almana.v4i3.1483.

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Investment has a very important role in economic growth, when investors invest, GDP tends to rise when investment falls, so GDP also tends to decline. Investors must be vigilant in investing in banking companies. One of the ways to predict stock prices with technical analysis is by using the ARIMA and GARCH methods. The purpose of this study is to determine whether the ARIMA and GARCH methods are accurate in predicting stock prices. The research method used in this research is descriptive and verification methods with a quantitative approach. Sources of data taken in this study are secondary data sources for the bank sub-sector found on the Indonesia Stock Exchange (IDX), namely the annual stock price reports for the years 2014, 2015, 2016, 2017, and 2018 as many as 39 companies. Processing data from this study using the ARIMA and GARCH methods with an evaluation of forecasting errors using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), or Mean Absolute Percentage Error (MAPE) analysis results using the E-View 9 program. shows that the ARIMA Method is accurate in predicting stock prices in 2015, 2016, and 2018. Meanwhile, the GARCH Method is accurate in predicting stock prices in 2014 and 2017.
27

Chung, Victor, and Jessie Bravo. "Analysis of Exchange Rate Volatility in Peru in the Presence of Structural Breaks." Journal of Hunan University Natural Sciences 49, no. 4 (April 30, 2022): 281–87. http://dx.doi.org/10.55463/issn.1674-2974.49.4.28.

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The presence of structural breaks in the analysis of volatility in the financial field has been ignored in research done in Latin America. This paper fills this gap by analyzing the behavior of the dollar exchange rate in Peru by evaluating the impact of structural breaks in volatility forecasting. The return behavior was analyzed for the period 05/01/2010 to 09/30/2021. Econometric analysis was used, which consisted of: (1) use of the modified Iterative Cumulative Sum of Squares (ICSS) algorithm to determine the structural breakpoints; (2) estimation of GARCH Models for the subsamples originated by the identified breakpoints; and (3) comparison of alternative models with the GARCH(1,1) expanding window model for horizons of 1, 20, 60 and 120 days. The ICSS algorithm identified 8 breaks in volatility behavior. The models were compared based on out-of-sample forecast performance. It was determined that the GARCH model that considers structural breaks is only effective for a one-day horizon. Finally, the GARCH(1,1) 0.25 rolling window model provides a better strategy for forecasting the volatility of exchange rate returns in Peru for longer horizons.
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Pradewita, Wella Cintya, Nur Karomah Dwidayati, and Sugiman Sugiman. "Peramalan Volatilitas Risiko Berinvestasi Saham Menggunakan Metode GARCH–M dan ARIMAX–GARCH." Indonesian Journal of Mathematics and Natural Sciences 44, no. 1 (April 12, 2021): 12–21. http://dx.doi.org/10.15294/ijmns.v44i1.32701.

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Model GARCH–M merupakan pengembangan model GARCH yang dimasukkan variansi bersyarat ke dalam persamaan mean. Model ARIMAX–GARCH merupakan penggabungan model ARIMAX dan GARCH. Kedua model tersebut dapat digunakan untuk mengatasi masalah heteroskedastisitas pada data. Penelitian ini bertujuan menemukan model terbaik untuk peramalan volatilitas risiko berinvestasi saham. Penelitian ini menggunakan literature dengan tahapan perumusan masalah, pengumpulan data, pengolahan dan analisis data, serta penarikan kesimpulan. Dalam analisis dan pembahasan meliputi statistika deskriptif, uji stasioneritas, pembentukan dan menentukan model terbaik kedua model, pembandingan kedua model, dan peramalan volatilitas saham. Dari hasil penelitian ini diperoleh model terbaik untuk peramalan volatilitas saham yaitu GARCH (1,1) – M dengan nilai MAPE=118,0299 lebih kecil dibanding nilai MAPE pada model ARIMAX (2,1,2)– GARCH (1,1) =191,3115. Berdasarkan model terbaik tersebut diperoleh hasil peramalan volatilitas saham sebesar 0,07629 dan apabila dana yang dialokasikan oleh investor saham sebesar Rp 200.000.000, 00 maka nilai VaR yang diperoleh sebesar Rp 85.615.826,00.GARCH-M is an expansion of the GARCH model that entered conditional variance into the mean equation. ARIMAX - GARCH is combination of ARIMAX model and GARCH model. Both models can be used to solve the problem of heteroscedasticity on data. The purpose of this research was to find the best model for forecasting of the risk of investing in stocks. The method of this research was problem formulation, data collection, data processing and analysis, and conclusions. In the analysis and discussion include descriptive statistics, stationary test, estimate and determine the best models of both models, comparison of both models, and stock volatility forecasting. The results of this research obtained the best model for forecasting of stock volatility is GARCH (1,1) - M with MAPE value = 118.0299 smaller than MAPE value of ARIMAX (2,1,2) - GARCH (1,1) = 191, 3115. Based on the best model is obtained forecasting of stock volatility is 0.07629 and if the fund allocated by investors are Rp 200,000,000.00, so the value of VaR obtained Rp 85.615.826,00.
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Akhtar, Sohail, Maham Ramzan, Sajid Shah, Iftikhar Ahmad, Muhammad Imran Khan, Sadique Ahmad, Mohammed A. El-Affendi, and Humera Qureshi. "Forecasting Exchange Rate of Pakistan Using Time Series Analysis." Mathematical Problems in Engineering 2022 (August 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/9108580.

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Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of the data series and moving average technical analysis are used in both models. Explanatory factors were used as indicators, and the prediction performance was assessed using a variety of commonly known statistical metrics. These statistical metrics suggested the presence of conditional heteroscedasticity. Thus, the process turns to capture the volatility effect of conditional heteroscedasticity through GARCH modeling. It may be inferred based on the results of tentative models; that the ARCH model outperforms the GARCH model in terms of predicting the USD/PKR exchange rate.
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Charfi, Sahar, and Farouk Mselmi. "Modeling exchange rate volatility: application of GARCH models with a Normal Tempered Stable distribution." Quantitative Finance and Economics 6, no. 2 (2022): 206–22. http://dx.doi.org/10.3934/qfe.2022009.

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<abstract><p>The aim of this paper is to examine exchange rate volatility using GARCH models with a new innovation distribution, the Normal Tempered Stable. We estimated daily exchange rate volatility using different distributions (Normal, Student, NIG) in order to specify the performed model. In addition, a forecasting analysis is performed to check which distribution reveals the best out-of-sample results. We found that the estimated parameters of GARCH-NTS model outperform the GARCH-N and GARCH-t ones for all currencies. Besides, we asserted that GARCH-NTS and EGARCH-NTS are the preferred models in terms of out-of sample forecasting accuracy. Our results indicating the performance of GARCH models with NTS distribution contribute to increase the accuracy of risk measures which is very important for international traders and investors.</p></abstract>
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Chlebus, Marcin. "Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?" Przegląd Statystyczny 63, no. 3 (September 30, 2016): 329–50. http://dx.doi.org/10.5604/01.3001.0014.1212.

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In the study, two-step EWS-GARCH models to forecast Value-at-Risk are analysed. The following models were considered: the EWS-GARCH models with lognormal, Weibull or Gamma distributions as a distributions in a state of turbulence, and with GARCH(1,1) or GARCH(1,1) with the amendment to empirical distribution of random error models as models used in a state of tranquillity. The evaluation of the quality of the Value-at-Risk forecasts was based on the Value-at-Risk forecasts adequacy (the excess ratio, the Kupiec test, the Christoffersen test, the asymptotic test of unconditional coverage and the backtesting criteria defined by the Basel Committee) and the analysis of loss func-tions (the Lopez quadratic loss function, the Abad & Benito absolute loss function, the 3rd version of Caporin loss function and the function of excessive costs). Obtained results show that the EWS-GARCH models with lognormal, Weibull or Gamma distributions may compete with EWS-GARCH models with exponential and empirical distributions. The EWS-GARCH model with lognormal, Weibull or Gamma distributions are relatively less conservative, but using them is less expensive than using the other EWS-GARCH models.
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Liu, Ji-Chun. "INTEGRATED MARKOV-SWITCHING GARCH PROCESS." Econometric Theory 25, no. 5 (October 2009): 1277–88. http://dx.doi.org/10.1017/s0266466608090506.

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This paper investigates stationarity of the so-called integrated Markov-switching generalized autoregressive conditionally heteroskedastic (GARCH) process, which is an important subclass of the Markov-switching GARCH process introduced by Francq, Roussignol, and Zakoïan (2001, Journal of Time Series Analysis 22,197–220) and a Markov-switching version of the integrated GARCH (IGARCH) process. We show that, like the classical IGARCH process, a stationary solution with infinite variance for the integrated Markov-switching GARCH process may exist. To this purpose, an alternative condition for the existence of a strictly stationary solution of the Markov-switching GARCH process is presented, and some results obtained in Hennion (1997, Annals of Probability 25, 1545–1587) are employed. In addition, we also discuss conditions for the existence of a strictly stationary solution of the Markov-switching GARCH process with finite variance, which is a modification of Theorem 2 in Francq et al. (2001).
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Chlebus, Marcin. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk." Central European Economic Journal 3, no. 50 (December 18, 2018): 01–25. http://dx.doi.org/10.1515/ceej-2017-0014.

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Abstract In the study, the two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns or Value-at-Risk forecasting models to be used in Value-at-Risk forecasting depending on a forecasted state of the financial time series. In the study EWS-GARCH with GARCH(1,1) and GARCH(1,1), with the amendment to the empirical distribution of random errors as a Value-at-Risk model in a state of tranquillity and empirical tail, exponential or Pareto distributions used to forecast Value-at-Risk in a state of turbulence were considered. The evaluation of Value-at-Risk forecasts was based on the Value-at-Risk forecasts and the analysis of loss functions. Obtained results indicate that EWS-GARCH models may improve the quality of Value-at-Risk forecasts generated using the benchmark models. However, the choice of best assumptions for the EWS-GARCH model should depend on the goals of the Value-at-Risk forecasting model. The final selection may depend on an expected level of adequacy, conservatism and costs of the model.
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Takaishi, Tetsuya. "Analysis of Spin Financial Market by GARCH Model." Journal of Physics: Conference Series 454 (August 12, 2013): 012041. http://dx.doi.org/10.1088/1742-6596/454/1/012041.

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Seth, Neha, and Monica Singhania. "Volatility in frontier markets: a Multivariate GARCH analysis." Journal of Advances in Management Research 16, no. 3 (July 15, 2019): 294–312. http://dx.doi.org/10.1108/jamr-02-2018-0017.

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Purpose The purpose of this paper is to analyze the existence of volatility spillover effect in frontier markets. This study also examines whether any linkages exist among these markets or not. Design/methodology/approach Monthly data of regional frontier markets, from 2009 to 2016, are analyzed using Multivariate GARCH (BEKK and Dynamic Conditional Correlation (DCC)) models. Findings The result of cointegration test shows that the sample frontier markets are not linked in long run, and Granger causality test reveals that the markets under consideration do not cause each other even in the short run. BEKK test says that the effect of the arrival of shock from the own market does not last for longer, whereas shock from other markets lasts with the stronger persistence, and according to DCC test, the volatility spillover exists for all the markets. Practical implications The results of present study suggest that the frontier markets are not cointegrated in the long run as well as in the short run, which opens the doors for long-term investments in these markets in future, which may lead to decent returns. Long-term investors may draw the benefits from including the financial assets in their portfolios from these non-integrated frontier markets; nevertheless, they have to consider and implement diversification and hedging strategies during the period of financial turmoil, so as to protect themselves against economic and financial distress. Originality/value Significant work has been done on developed, developing and emerging markets but frontier markets are not explored much so far. This paper is an attempt to see the status of frontier stock markets as potential financial markets for diversification benefits.
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Kim, Jong-Min, and Sunghae Jun. "Integer-valued GARCH processes for Apple technology analysis." Industrial Management & Data Systems 117, no. 10 (December 4, 2017): 2381–99. http://dx.doi.org/10.1108/imds-01-2017-0023.

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Purpose The keywords from patent documents contain a lot of information of technology. If we analyze the time series of keywords, we will be able to understand even more about technological evolution. The previous researches of time series processes in patent analysis were based on time series regression or the Box-Jenkins methodology. The methods dealt with continuous time series data. But the keyword time series data in patent analysis are not continuous, they are frequency integer values. So we need a new methodology for integer-valued time series model. The purpose of this paper is to propose modeling of integer-valued time series for patent analysis. Design/methodology/approach For modeling frequency data of keywords, the authors used integer-valued generalized autoregressive conditional heteroskedasticity model with Poisson and negative binomial distributions. Using the proposed models, the authors forecast the future trends of target keywords of Apple in order to know the future technology of Apple. Findings The authors carry out a case study to illustrate how the methodology can be applied to real problem. In this paper, the authors collect the patent documents issued by Apple, and analyze them to find the technological trend of Apple company. From the results of Apple case study, the authors can find which technological keywords are more important or critical in the entire structure of Apple’s technologies. Practical implications This paper contributes to the research and development planning for producing new products. The authors can develop and launch the innovative products to improve the technological competition of a company through complete understanding of the technological keyword trends. Originality/value The retrieved patent documents from the patent databases are not suitable for statistical analysis. So, the authors have to transform the documents into structured data suitable for statistics. In general, the structured data are a matrix consisting of patent (row) and keyword (column), and its element is an occurred frequency of a keyword in each patent. The data type is not continuous but discrete. However, in most researches, they were analyzed by statistical methods for continuous data. In this paper, the authors build a statistical model based on discrete data.
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Horváth, Lajos, Piotr Kokoszka, and Ričardas Zitikis. "Distributional analysis of empirical volatility in GARCH processes." Journal of Statistical Planning and Inference 138, no. 11 (November 2008): 3578–89. http://dx.doi.org/10.1016/j.jspi.2007.02.014.

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Kleibergen, F., and H. K. van Dijk. "Non-stationarity in garch models: A bayesian analysis." Journal of Applied Econometrics 8, S1 (December 1993): S41—S61. http://dx.doi.org/10.1002/jae.3950080505.

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Chen, Kuo-Shing, and Shen-Ho Chang. "Volatility Co-Movement between Bitcoin and Stablecoins: BEKK–GARCH and Copula–DCC–GARCH Approaches." Axioms 11, no. 6 (May 29, 2022): 259. http://dx.doi.org/10.3390/axioms11060259.

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This paper aims to investigate and measure Bitcoin and the five largest stablecoin market volatilities by incorporating various range-based volatility estimators to the BEKK- GARCH and Copula-DCC-GARCH models. Specifically, we further measure Bitcoins’ volatility related to five major stablecoins and examine the connectedness between Bitcoin and the stablecoins. Our empirical findings document that the connectedness between Bitcoin and stablecoin market volatility behaviors exhibits the presence of stable interconnection. This study is of particular importance since it is crucial for market participation in the ongoing crypto assets to be informed about both the volatility patterns of major cryptocurrencies and the relative volatility of Bitcoin against the stablecoin markets. Eventually, we find that there is no systematic evidence for the various parity deviations of the stablecoins that are profoundly impacted by Bitcoin volatility. Thus, Bitcoin and the largest stablecoin Tether could stabilize together. However, Bitcoin shall not be generalized to other stablecoins in terms of stability results.
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Vengesai, Edson. "COVID-19 and Stock Market Volatility in South Africa: A Cross-Sector Analysis." Asian Economic and Financial Review 12, no. 7 (June 30, 2022): 473–93. http://dx.doi.org/10.55493/5002.v12i7.4533.

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The COVID-19 pandemic has created enormous economic and market uncertainty in the global economy. However, businesses and industries weren’t affected homogeneously; whilst others suffered, some blossomed. Equity markets were not spared from the detrimental effects of the pandemic. This study investigates the impact of COVID-19 on stock returns’ conditional volatility in different South African stock market sectors using standard symmetrical and asymmetrical GARCH models. The MDCC-GARCH model was employed to understand the dynamics of conditional correlations between the leading indices. The results suggest that COVID-19 has increased return volatility for the majority of the sectors; however, the sectors weren’t affected in the same way. The DCC-GARCH model shows significant, high, positive correlations between the major and Small Cap indices, suggesting insignificant diversification benefits during the pandemic. The alternative exchange (ALTX) was found to have declining correlations with the main sectors, indicating an increase in diversification benefits offered by the ALTX following the pandemic shock.
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Hasnanda, Sri, and Ratna Ratna. "The Generalized Autoregressive Conditional Heteroscedasticity Model Application on Inflation and Consumers Price Index in Aceh." Journal of Malikussaleh Public Economics 3, no. 1 (November 29, 2020): 8. http://dx.doi.org/10.29103/jmpe.v3i1.3191.

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This study aimed to analyze the application of Generalized Autoregression Conditional Heteroscedasticity (GARCH) on inflation and consumer price index in Aceh Province. The data used in this study is secondary data sourced from the Central Statistics Agency (BPS) of Aceh Province with a period of 2013-2018 on a monthly basis. The data analysis method used is the ARIMA for mean model GARCH for variance models. The results of this study indicate that the Mean Model for Inflation uses the AR (1) and MA (1) components, while the Mean Model for consumer price index is AR (1). Meanwhile, the Variance Model with GARCH estimates for inflation and consumer price index data has insignificant RESID^2 (1) and GARCH (1).Keywords: Inflation, CPI and Generalized Autoregression Conditional Heteroscedasticity (GARCH)
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Dinku, Tirngo, Worku Gardachw, and Ngozi Adeleye. "Price Volatility for Selected Agricultural Commodities in Ethiopia: Evidence from GARCH Models." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 18 (November 11, 2021): 1380–88. http://dx.doi.org/10.37394/23207.2021.18.127.

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This study models the volatility of returns for selected agricultural commodity prices in Ethiopia using the generalized autoregressive conditional heteroskedasticity (GARCH) approach. GARCH family models, specifically threshold GARCH and exponential GARCH were employed to analyze the time varying volatility of selected agricultural commodities prices from 2010 to 2021. The data analysis results revealed that, out of the GARCH specifications, the EGARCH model with the normal distributional assumption of residuals was a better fit model for the price volatility of “teff” and “red pepper” in which their return series reacted differently to the “good” and “bad” news. The study indicated the existence of a leverage effect, which implied that the “bad” news could have a larger effect on volatility than the “good” news of the same magnitude, and the asymmetric term was statistically significant.
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Kim, Geon Cheol, Sung Sik Park, Soo Jin Bang, and Gwang Bin Lee. "Regional correlation analysis of housing price using multivariate GARCH model." Journal of Housing and Urban Finance 5, no. 2 (December 2020): 19–38. http://dx.doi.org/10.38100/jhuf.2020.5.2.19.

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Omari-Sasu, Akoto Yaw, Nana Kena Frempong, Maxwell Akwasi Boateng, and Richard Kena Boadi. "Modeling Stock Market Volatility Using GARCH Approach on the Ghana Stock Exchange." International Journal of Business and Management 10, no. 11 (October 26, 2015): 169. http://dx.doi.org/10.5539/ijbm.v10n11p169.

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The study examined and modeled stock market volatility of financial return series for three listed equities on the Ghana Stock Exchange (GSE). A historical data from 25<sup>th</sup> June 2007 to 31<sup>st</sup> October 2014 was considered for the analysis. The series for each of the three equities were tested for stationarity using the KPSS test. Series found to be non-stationary were transformed to be stationary. The study fitted a GARCH (p, q) model for volatility. GARCH (1, 1), GARCH (1, 2), GARCH (2, 1) and the GARCH (2, 2) models were fitted to residual series of some three equities. Results revealed the presence of volatilities in all three equities and also showed that volatility though present was not persistent in the three equities. For each of the companies under study, the GARCH (1, 1) model was found to outperform the other three models based on the comparison of the AICc for each model. The study recommended the use and comparison of other variants of the GARCH model in estimation of volatility.
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Jiang, Jing Jing, and Bin Ye. "Value-at-Risk Estimation of Carbon Spot Market Based on the Combined GARCH-EVT-VaR Model." Advanced Materials Research 1065-1069 (December 2014): 3250–53. http://dx.doi.org/10.4028/www.scientific.net/amr.1065-1069.3250.

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Based on the analysis of the dynamics of carbon price volatility, this article proposes to develop a combined extreme value theory and conditional variance based Value-at-Risk model (GARCH-EVT-VaR) for short-term risk measurement and estimation of the carbon spot market under the European Union Emission Trading Scheme (EU ETS). The model is implied to the EUA spot market and compared with the traditional GARCH-VaR model, the empirical results show that the GARCH based model underestimates market risks by overlooking the great price shocks, but the GARCH-EVT based model has the ability to take those extreme jumps into its risk estimations.
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Wu, Maoguo, and Zeyang Li. "Risk Analysis of Shanghai Inter-Bank Offered Rate - A GARCH-VaR Approach." European Scientific Journal, ESJ 13, no. 22 (August 31, 2017): 252. http://dx.doi.org/10.19044/esj.2017.v13n22p252.

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The inter-bank offered rate widely used by Chinese commercial banks is Shanghai Inter-Bank Offered Rate (Shibor). Shibor has experienced significant development since it was created. It offers different products by duration. Despite its importance in China’s financial market, Shibor’s risk has largely remained unexplored. Making contribution to existing literature on risk management of Shibor, this paper investigates risk of Shanghai Inter- Bank Offered Rate (Shibor) utilizing GARCH-VaR method. The VaR of each product is calculated and compared while GARCH model is designed for a simpler calculation. In order to have a clearer view of Chinese commercial banks, the data selected is Shibor data sample from 2006 to 2016, which is measured by GARCH-VaR model and verified effectiveness by chi-square test. Empirical results show strong evidence for the need of Chinese commercial banks to change the status quo so that the great fluctuation and abnormal situation can be avoided. Policy implication, involving the interest rate management and internal problem in commercial banks, is proposed for financial regulators.
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Li, Hai-Feng, Dun-Zhong Xing, Qian Huang, and Jiang-Cheng Li. "Roles of GARCH and ARCH effects on the stability in stock market crash." Europhysics Letters 136, no. 4 (November 1, 2021): 48003. http://dx.doi.org/10.1209/0295-5075/ac4527.

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Abstract We theoretically stochastically simulate and empirically analyze the escape process of stock market price non-equilibrium dynamics under the influence of GARCH and ARCH effects, and explore the impact of ARCH and GARCH effects on stock market stability. Based on the nonlinear GARCH model of econophysics, and combined with GARCH and ARCH effects of volatility, we propose a delay stochastic monostable potential model. We use the mean escape time, or mean hitting time, as an indicator for measuring price stability, as first introduced in Valenti D. et al., Phys. Rev. E, 97 (2018) 062307. Based on the comparative analysis of actual Chinese A-share data, the theoretical and empirical findings of this paper are as follows: 1) The theoretical simulation results and actual data are consistent. 2) There exist optimal GARCH and ARCH effects maximally enhancing stock market stability.
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Domańska, Sylwia. "DOPASOWANIE MODELI GARCH A JAKOŚĆ UZYSKANYCH PROGNOZ." Metody Ilościowe w Badaniach Ekonomicznych 21, no. 3 (December 23, 2020): 121–33. http://dx.doi.org/10.22630/mibe.2020.21.3.12.

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Analysis of the adaptation of GARCH models presented in the paper consists of three parts. Firstly, the theoretical considerations in this regard was made, referring to the recommendations identified in the literature. Secondly, practical aspects of the proper selection of GARCH model for the time series based on the values of information criteria was presented. Thirdly, the relation between the quality of adaptation and the quality of obtained forecasts was indicated, using the tools of quantitative analysis. The aim of this paper is to verify if the improvement of the goodness of GARCH models results in better volatility forecasts.
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Xie, Danni, Xin Liang, and Ruilin Liang. "Self-Weighted Quasi-Maximum Likelihood Estimators for a Class of MA-GARCH Model." Symmetry 14, no. 8 (August 18, 2022): 1723. http://dx.doi.org/10.3390/sym14081723.

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In financial time series analysis, symmetric and asymmetric GARCH models have become essential models for measuring the characteristics of economic volatility. In this article, we propose the consistency and asymptotic normality properties of the self-weighted quasi-maximum likelihood estimation without assuming the existence of the second moment for the moving average model with a class of GARCH error. Numerical simulation shows that the parameter estimation performs well; empirical analysis shows that the self-weighted quasi-maximum likelihood estimation of the moving average model with a class of GARCH error can improve the data fitting effect and prediction ability.
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Comte, F., and O. Lieberman. "Asymptotic theory for multivariate GARCH processes." Journal of Multivariate Analysis 84, no. 1 (January 2003): 61–84. http://dx.doi.org/10.1016/s0047-259x(02)00009-x.

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