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Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets
Phichhang Ou,Hengshan Wang
International Journal of Economics and Finance , 2010, DOI: 10.5539/ijef.v2n1p51
Abstract: In this paper, we aim at comparing semi-parametric method, LSSVM (Least square support vector machine), with the classical GARCH(1,1), EGARCH(1,1) and GJR(1,1) models to forecast financial volatilities of three major ASEAN stock markets. More precisely, the experimental results suggest that using hybrid models, GARCH-LSSVM, EGARCH-LSSVM and GJR-LSSVM provides improved performances in forecasting the leverage effect volatilities, especially during the recently global financial market crashes in 2008.
A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting  [cached]
Leandro Maciel
Revista Brasileira de Finan?as , 2012,
Abstract: Forecasting stock market returns volatility is a challenging task that has attracted the attention of market practitioners, regulators and academics in recent years. This paper proposes a Fuzzy GJR-GARCH model to forecast the volatility of S&P 500 and Ibovespa indexes. The model comprises both the concept of fuzzy inference systems and GJR-GARCH modeling approach in order to consider the principles of time-varying volatility, leverage effects and volatility clustering, in which changes are cataloged by similarity. Moreover, a differential evolution (DE) algorithm is suggested to solve the problem of Fuzzy GJR-GARCH parameters estimation. The results indicate that the proposed method offers significant improvements in volatility forecasting performance in comparison with GARCH-type models and with a current Fuzzy-GARCH model reported in the literature. Furthermore, the DE-based algorithm aims to achieve an optimal solution with a rapid convergence rate.
Implementation of the Estimating Functions Approach in Asset Returns Volatility Forecasting Using First Order Asymmetric GARCH Models  [PDF]
Timothy Ndonye Mutunga, Ali Salim Islam, Luke Akong’o Orawo
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.55047
Abstract: This paper implements the method of estimating functions (EF) in the modelling and forecasting of financial returns volatility. This estimation approach incorporates higher order moments which are common in most financial time series, into modelling, leading to a substantial gain of information and overall efficiency benefits. The two models considered in this paper provide a better in-sample-fit under the estimating functions approach relative to the traditional maximum likely-hood estimation (MLE) approach when fitted to empirical time series. On this ground, the EF approach is employed in the first order EGARCH and GJR-GARCH models to forecast the volatility of two market indices from the USA and Japanese stock markets. The loss functions, mean square error (MSE) and mean absolute error (MAE), have been utilized in evaluating the predictive ability of the EGARCH vis-à-vis the GJR-GARCH model.
An Empirical Evaluation in GARCH Volatility Modeling: Evidence from the Stockholm Stock Exchange  [PDF]
Chaido Dritsaki
Journal of Mathematical Finance (JMF) , 2017, DOI: 10.4236/jmf.2017.72020
Abstract: In this paper, we use daily stock returns from the Stockholm Stock Exchange in order to examine their volatility. For this reason, we estimate not only GARCH (1,1) symmetric model but also asymmetric models EGARCH (1,1) and GJR-GARCH (1,1) with different residual distributions. The parameters of the volatility models are estimated with the Maximum Likelihood (ML) using the Marquardt algorithm (Marquardt [1]). The findings reveal that negative shocks have a large impact than positive shocks in this market. Also, indices for the return of forecasting have shown that the ARIMA (0,0,1)-EGARCH (1,1) model with t-student provide more precise forecasting on volatilities and expected returns of the Stockholm Stock Exchange.
The Effect of Money Supply on the Volatility of Korean Stock Market  [PDF]
Ki-Hong Choi, Seong-Min Yoon
Modern Economy (ME) , 2015, DOI: 10.4236/me.2015.65052
Abstract: We examined the potential relationships between changes in the money supplies of Korea and the United States and volatility of the Korean stock market using the GARCH, GJR-GARCH, and EGARCH models. We did not identify any such relationships, implying that changes in money supply do not influence the flow of information to the market. However, we found that the asymmetric effect of bad news on volatility was higher when contemporaneous changes in Korean and US money supply variables were included in the models. This indicates that changes in money supply did not affect Korean stock volatility directly. Finally, the results based on a variance model indicated that the money supply of the two countries had no effect on the Korean stock market. This formal study suggests that there is no significant forecasting power of past changes in money supply. Although stock returns and volatility are not directly affected by changes in the money supply, the influence of supply on macroeconomic activity should not be disregarded.
Forecasting crude oil market volatility: can the Regime Switching GARCH model beat the single-regime GARCH models?  [PDF]
Yue-Jun Zhang,Ting Yao,Ling-Yun He
Quantitative Finance , 2015,
Abstract: In order to obtain a reasonable and reliable forecast method for crude oil price volatility, this paper evaluates the forecast performance of single-regime GARCH models (including the standard linear GARCH model and the nonlinear GJR-GARCH and EGARCH models) and the two-regime Markov Regime Switching GARCH (MRS-GARCH) model for crude oil price volatility at different data frequencies and time horizons. The results indicate that, first, the two-regime MRS-GARCH model beats other three single-regime GARCH type models in in-sample data estimation under most evaluation criteria, although it appears inferior under a few of other evaluation criteria. Second, the two-regime MRS-GARCH model overall provides more accurate volatility forecast for daily data but this superiority dies way for weekly and monthly data. Third, among the three single-regime GARCH type models, the volatility forecast of the nonlinear GARCH models exhibit greater accuracy than the linear GARCH model for daily data at longer time horizons. Finally, the linear single-regime GARCH model overall performs better than other three nonlinear GARCH type models in Value-at-Risk (VaR) forecast.
Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models  [PDF]
Manamba Epaphra
Journal of Mathematical Finance (JMF) , 2017, DOI: 10.4236/jmf.2017.71007
Abstract: Policy makers need accurate forecasts about future values of exchange rates. This is due to the fact that exchange rate volatility is a useful measure of uncertainty about the economic environment of a country. This paper applies univariate nonlinear time series analysis to the daily (TZS/USD) exchange rate data spanning from January 4, 2009 to July 27, 2015 to examine the behavior of exchange rate in Tanzania. To capture the symmetry effect in exchange rate data, the paper applies both ARCH and GARCH models. Also, the paper employs exponential GARCH (EGARCH) model to capture the asymmetry in volatility clustering and the leverage effect in exchange rate. The paper reveals that exchange rate series exhibits the empirical regularities such as clustering volatility, nonstationarity, non-normality and serial correlation that justify the application of the ARCH methodology. The results also suggest that exchange rate behavior is generally influenced by previous information about exchange rate. This also implies that previous day’s volatility in exchange rate can affect current volatility of exchange rate. In addition, the estimate for asymmetric volatility suggests that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. The main policy implication of these results is that since exchange rate volatility (exchange-rate risk) may increase transaction costs and reduce the gains to international trade, knowledge of exchange rate volatility estimation and forecasting is important for asset pricing and risk management.
Forecasting Volatility of Gold Price Using Markov Regime Switching and Trading Strategy  [PDF]
Nop Sopipan, Pairote Sattayatham, Bhusana Premanode
Journal of Mathematical Finance (JMF) , 2012, DOI: 10.4236/jmf.2012.21014
Abstract: In this paper, we forecast the volatility of gold prices using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting gold price volatility. The MRS-GARCH is best performance model for gold price volatility in some loss function. Moreover, we forecast closing prices of gold price to trade future contract. MRS-GARCH got the most cumulative return same GJR model.
Testing and Predicting Volatility Spillover—A Multivariate GJR-GARCH Approach  [PDF]
Hira Aftab, Rabiul Alam Beg, Sizhong Sun, Zhangyue Zhou
Theoretical Economics Letters (TEL) , 2019, DOI: 10.4236/tel.2019.91008

This paper proposes a multivariate VAR-BEKK-GJR-GARCH volatility model to assess the dynamic interdependence among stock, bond and money market returns and volatility of returns. The proposed model allows for market interaction which provides useful information for pricing securities, measuring value-at-risk (VaR), and asset allocation and diversification, assisting financial regulators for policy implementation. The model is estimated by the maximum likelihood method with Student-t innovation density. The asymptotic chi-square tests for volatility spillovers and leverage effects are constructed and provide predictions of volatility and time-varying correlations of returns. Application of the proposed model to the Australia’s domestic stock, bond, and money markets reveals that the domestic financial markets are interdependent and volatility is predictable. In general, volatility spillovers from stock market to bond and to money markets due to common news. The empirical findings of this paper quantify the association among the security markets which can be utilized for improving agents’ decision-making strategies for risk management, portfolio selection and diversification.

Unravelling the Cipher of Indian Rupee’s Volatility: Testing the Forecasting Efficacy of the Rolling Symmetric and Asymmetric GARCH Models  [PDF]
Shalini Talwar, Aparna Bhat
Theoretical Economics Letters (TEL) , 2018, DOI: 10.4236/tel.2018.86079
Abstract: Modelling exchange rate volatility is crucially important because of its diverse implications on the profitability of corporations and decisions of policy makers. This paper empirically investigates exchange rate volatility of India’s currency by applying rolling symmetric and asymmetric GARCH models to the USDINR and EURINR daily exchange rates for a period spanning April 1, 2006 through January 31, 2018, resulting in total observations of 2861. To estimate GARCH (1,1) and EGARCH (1,1) models, the data window is rolled over five years with nearly 1200 observations and one month is used as forecast period for each window. Both, in-sample criteria like the log likelihood criteria, Akaike information criterion (AIC), the Bayesian information criterion (SIC) and Hannan Quinn criterion (HQC) as well as the out-of-sample criteria like Mean Squared Error (MSE) and Mean Absolute Error (MAE) have been used to test model fit and forecast accuracy of the models. To test the robustness of the findings, Diebold-Mariano test is used to compare the predictive accuracy of both the models. Further, the forecasting accuracy of the two models has also been tested by splitting the sample period into periods of tranquility and volatility in Indian exchange rate. Results show that GARCH (1,1) model with generalized error distribution is adequate to capture the mean and volatility process of USDINR and EURINR exchange rate returns.
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