A copula-based regime-switching GARCH model for optimal futures hedging

2009 ◽  
Vol 29 (10) ◽  
pp. 946-972 ◽  
Author(s):  
Hsiang-Tai Lee
Keyword(s):  
Regime Switching ◽  
Garch Model ◽  
Futures Hedging

scholarly journals A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns

2018 ◽  
Vol 22 (3) ◽  
Author(s):  
Markus Haas ◽  
Ji-Chun Liu
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Regime Switching ◽  
Markov Switching ◽  
Garch Model ◽  
Equity Returns ◽  
Out Of Sample ◽  
High Volatility ◽  
Simple Recursion ◽  
Global Stock

AbstractWe consider a multivariate Markov-switching GARCH model which allows for regime-specific volatility dynamics, leverage effects, and correlation structures. Conditions for stationarity and expressions for the moments of the process are derived. A Lagrange Multiplier test against misspecification of the within-regime correlation dynamics is proposed, and a simple recursion for multi-step-ahead conditional covariance matrices is deduced. We use this methodology to model the dynamics of the joint distribution of global stock market and real estate equity returns. The empirical analysis highlights the importance of the conditional distribution in Markov-switching time series models. Specifications with Student’stinnovations dominate their Gaussian counterparts both in- and out-of-sample. The dominating specification appears to be a two-regime Student’stprocess with correlations which are higher in the turbulent (high-volatility) regime.

Foreign Exchange Volatility Shifts and Futures Hedging: An ICSS-GARCH Approach

2007 ◽  
Vol 10 (03) ◽  
pp. 349-388 ◽  
Author(s):  
Iqbal Mansur ◽  
Steven J. Cochran ◽  
David Shaffer
Keyword(s):  
Garch Model ◽  
Volatility Persistence ◽  
Futures Hedging ◽  
Volatility Models ◽  
Out Of Sample ◽  
Competing Models ◽  
Foreign Exchange Volatility ◽  
The Garch Model ◽  
The Impact ◽  
Cumulative Sums

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.

scholarly journals Research on the Value at Risk of Basis for Stock Index Futures Hedging in China Based on Two-State Markov Process and Semiparametric RS-GARCH Model

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Liang Wang ◽  
Tingjia Xu ◽  
Longhao Qin ◽  
Chenge Liu
Keyword(s):  
At Risk ◽  
Markov Process ◽  
Value At Risk ◽  
Likelihood Function ◽  
Parametric Method ◽  
Garch Model ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Futures Hedging

This article aims to investigate the Value at Risk of basis for stock index futures hedging in China. Since the RS-GARCH model can effectively describe the state transition of variance in VaR and the two-state Markov process can significantly reduce the dimension, this paper constructs the parameter and semiparametric RS-GARCH models based on two-state Markov process. Furthermore, the logarithm likelihood function method and the kernel estimation with invariable bandwidth method are used for VaR estimation and empirical analysis. It is found that the three fitting errors (MSE, MAD, and QLIKE) of conditional variance calculated by semiparametric model are significantly smaller than that of the parametric model. The results of Kupiec backtesting on VaR obtained by the two models show that the failure days of the former are less than or equal to that of the latter, so it can be inferred that the semiparametric RS-GARCH model constructed in this paper is more effective in estimating the Value at Risk of the basis for Chinese stock index futures. In addition, the mean value and standard deviation of VaR obtained by the semiparametric RS-GARCH model are smaller than that of the parametric method, which can prove that the former model is more conservative in risk estimation.

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