Regime-switching GARCH Model Research Articles

Exploring the nexus between monetary uncertainty and volatility in global crude oil: A contemporary approach of regime-switching

Understanding the complex relationship between global crude oil market volatility and monetary uncertainty motivates the study. Understanding crude oil's volatility is crucial because it affects the economy and geopolitics. Monetary uncertainty, caused by monetary policy decisions, exchange rate fluctuations, and global economic conditions, could affect crude oil markets. Thus, monetary uncertainty and crude oil volatility must be examined to understand the dynamics and inform policy. In this empirical work, we examine the effects of monetary uncertainty on global crude oil price volatility using a time-varying transition probabilities Markov regime-switching GARCH model (TVTP-MS-GARCH). Our approach differs from prior research by considering the potential nonlinearity and shifting patterns in the relationship between crude oil volatility and monetary uncertainty. Our in-sample analysis provides interesting evidence for regime changes between the two monetary variables. The results of our out-of-sample testing show that the asymmetric TVTP-MS-GARCH model outperforms the symmetric version in predicting crude oil volatility. This suggests that monetary uncertainty has an asymmetrical impact on crude oil price volatility. We perform several robustness tests to validate our findings, including alternate lag specifications of monetary uncertainty and varying estimation window sizes. These tests further reinforce the importance of considering the dynamic changes in monetary uncertainty when attempting to predict crude oil volatility. Overall, our research highlights the need for a more nuanced approach to understanding the relationship between crude oil prices and monetary uncertainty.

Optimal portfolio diversification with a multi-chain regime-switching spillover GARCH model

A multi-chain regime-switching spillover GARCH (MCRSSG) model is proposed for optimal portfolio diversification. MCRSSG specifies the within-regime time-varying correlation via a multi-chain state-dependent spillover factor and quantifies the magnitude of volatility spillovers under different regime combinations. MCRSSG is applied to investigate the diversification benefit of precious metals, crude oil, and financial securities for the Korean stock market at a sector level. The empirical results reveal that the Dow Jones Islamic market US total return index provides the best diversification benefit and MCRSSG exhibits superior effectiveness for risk-adjusted return and reward-to-semivariance ratio.

Comparison of value-at-risk forecast using regime-dependent models for the S&P 200 Energy Index

This study investigates the regimes developed in the S&P 200 Energy Index using the Markov regime-switching GARCH model with the asymmetry feature. We also undertake to compare the Markov regime-switching GJR-GARCH model with its single regime counterpart for the Value-at-Risk one-step ahead forecast with the Backtest settings. The results indicate two regimes for the S&P 200 Energy Index with different volatility characteristics. The MS-GJR-GARCH model performs better than the GJR-GARCH model in out-of-sample Value-at-Risk analysis as per the dynamic quantile test. The results are significant for the investors forming their trading strategy based on historical data.

An analysis of East African tea crop prices using the MCMC approach to estimate volatility and forecast the in-sample value-at-risk

In Eastern African countries, agriculture contributes significantly to the national economy. However, the prices of the essential crops fluctuate considerably due to climate change, the economic crisis, and surging food and fuel costs in the region. This paper aims to capture the dynamic volatility of tea crop prices, one of the region's most important cash crops. We applied the Markov-switching GARCH (MS-GARCH)-type specifications with different scedastic functions and error distributions to estimate volatility and forecast the in-sample value-at-risk (VaR) of tea price returns. This paper analyses monthly tea auction prices (i.e., Mombasa auction) in USD from January 1980 to June 2022. The parameters of the MS-GARCH model are estimated in a Bayesian framework via the MCMC approach. The findings evidenced that the EGARCH skewed Student-t model with three regimes was superior in estimating volatility. In terms of VaR evaluation, there is no superior method, but considering scedastic functions, fat tails, asymmetry, and regime switching is more effective. To manage volatility and risk in the East African tea sector, investors should consider regime switching GARCH models.

A regime-switching real-time copula GARCH model for optimal futures hedging

A regime-switching real-time copula GARCH (RSRTCG) model is suggested for optimal futures hedging. The specification of RSRTCG is to model the margins of asset returns with state-dependent real-time GARCH and the dependence structure of asset returns with regime switching copula functions. RSRTCG is faster in adjusting to the new level of volatility under different market regimes which is a regime-switching multivariate generalization of the state-independent univariate real-time GARCH. RSRTCG is applied to cross hedge the price risk of S&P 500 sector indices with crude oil futures. The empirical results show that RSRTCG possesses superior hedging performance compared to its nested non-real-time or state-independent copula GARCH models based on the criterion of percentage variance reduction, utility gain, model confidence set, model combination strategy, risk-adjusted return and reward-to-semivariance ratio.

Portfolio Optimization on Multivariate Regime-Switching GARCH Model with Normal Tempered Stable Innovation

This paper uses simulation-based portfolio optimization to mitigate the left tail risk of the portfolio. The contribution is twofold. (i) We propose the Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. The volatility of each asset independently follows the regime-switch GARCH model, while the correlation of joint innovation of the GARCH models follows the Hidden Markov Model. (ii) We use tail risk measures, namely conditional value-at-risk (CVaR) and conditional drawdown-at-risk (CDaR), in the portfolio optimization. The optimization is performed with the sample paths simulated by the MRS-MNTS-GARCH model. We conduct an empirical study on the performance of optimal portfolios. Out-of-sample tests show that the optimal portfolios with tail measures outperform the optimal portfolio with standard deviation measure and the equally weighted portfolio in various performance measures. The out-of-sample performance of the optimal portfolios is also more robust to suboptimality on the efficient frontier.

Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework

In this paper, we investigate the dynamic impact of financial stress on crude oil volatility by employing a time-varying transition probabilities Markov regime-switching GARCH model (TVTP-MS-GARCH). Different from the existing work, we mainly consider the possible nonlinearity and regime changes among crude oil volatility and financial stress. First, the in-sample results strongly support the existence of the potential regime switches among the two financial fundamentals. Second, compared with the symmetric TVTP-MS-GARCH model, the model based on an asymmetric framework shows a better predictive performance in the out-of-sample findings. It implies that the dynamic impact of financial stress on crude oil exhibits an asymmetric feature. Finally, our findings are robust to several alternative checks, including other lags of financial stress and estimated window size. Thus, it is necessary to focus on the dynamic changes of financial stress for accurately predicting crude oil volatility.

A lattice approach for option pricing under a regime-switching GARCH-jump model

AbstractIn this study, we consider option pricing under a Markov regime-switching GARCH-jump (RS-GARCH-jump) model. More specifically, we derive the risk neutral dynamics and propose a lattice algorithm to price European and American options in this framework. We also provide a method of parameter estimation in our RS-GARCH-jump setting using historical data on the underlying time series. To measure the pricing performance of the proposed algorithm, we investigate the convergence of the tree-based results to the true option values and show that this algorithm exhibits good convergence. By comparing the pricing results of RS-GARCH-jump model with regime-switching GARCH (RS-GARCH) model, GARCH-jump model, GARCH model, Black–Scholes (BS) model, and Regime-Switching (RS) model, we show that accommodating jump effect and regime switching substantially changes the option prices. The empirical results also show that the RS-GARCH-jump model performs well in explaining option prices and confirm the importance of allowing for both jump components and regime switching.

A dynamic markov regime-switching asymmetric GARCH model and its cumulative impulse response function

In this paper, we consider the Markov regime-switching GJR-GARCH(1,1) model to capture both the cumulative impulse response and the asymmetry of the dynamic behavior of financial market volatility in stationary and explosive states. The model can capture regime shifts in volatility between two regimes as well as the asymmetric response to negative and positive shocks. A Monte Carlo simulation is conducted to validate the main theory and find that the regime-switching GJR-GARCH model performs better than the standard GJR-GARCH model. Applications to Brazilian stock market data show that the proposed model performs well in terms of cumulative impulse response.

A simulation study on the Markov regime-switching zero-drift GARCH model

A Zero-drift GARCH (ZD-GARCH) model is recently proposed to study conditional and unconditional heteroskedasticity together. Despite its attractive statistical properties, our research demonstrates that the stability test based on this model fails when structural changes are present. To overcome this issue, we allow the Markov regime-switching (MRS) feature within the ZD-GARCH framework and propose an MRS-ZD-GARCH model. A revised stability estimator is further derived. The effectiveness of our proposed approach to test the stability with and without structural changes is evidenced via simulation studies. Using the empirical data of the S&P 500, NASDAQ and Apple returns, we show that the new model can also outperform the ZD-GARCH model in practice and provide more informative results. Therefore, the MRS-ZD-GARCH model could be a widely useful tool to study the stability of financial data and help address risk management issues in other contexts.

Forecasting extreme risk using regime-switching GARCH models: a case from an energy commodity

PurposeThe purpose of this paper is to investigate regime-switching and single-regime GARCH models for the extreme risk forecast of the developed and the emerging crude oil markets.Design/methodology/approachThe regime-switching GARCH-type models and their single-regime counterparts are used in risk forecast of crude oil.FindingsThe author finds that the regime-switching GARCH-type models are suitable for the developed and the emerging crude oil markets in that they effectively measure the extreme risk of crude oil in different cases. Meanwhile, the model with switching regimes captures dynamic structures in financial markets, and these models are just only better than the corresponding single-regime in terms of long position risk forecast, instead of short position. That is, it just outperforms the single-regime on the downside risk forecast.Originality/valueThis study comprehensively compares risk forecast of crude oil in different situations through the competitive models. The obtained findings have strong implications to investors and policymakers for selecting a suitable model to forecast extreme risk of crude oil when they are faced with portfolio selection, asset allocation and risk management.

Multivariate Regime Switching GARCH Model Application to Portfolio and Risk Management

Regime switching dynamic correlation (RSDC) model allows the correlations to be constant with the regimes themselves however, it differs across regimes. RSDC model has retained many good properties from CCC multivariate GARCH and DCC multivariate GARCH. For example, RSDC does not suffer from the curse of dimensional due to the two steps estimation for volatility. Positive semi definite is also easy to achieve from its decomposition from covariances. In this research, the feasibility of the constant correlations assumptions have been tested using Lagrange Multiplier test developed by Tse (2000) on the RSDC model.

Modeling volatility of precious metals markets by using regime-switching GARCH models

This paper aims to test the existence of regime changes by using MSGARCH models for modeling volatility of the four most famous precious metals, i.e. Gold, Silver, Palladium, and Platinum. We fitted around 72 MSGARCH models with different regimes (k=1,2,3) to the log-returns of each precious metal to test in-sample analysis of volatility. We compare 72 models for in-sample analysis by using the Akaike information criterion (AIC) and choose the best models for all precious metals’ series. Further, one-day ahead Value-at-Risk forecasting was conducted by the best MSGARCH. Our finding suggests the existence of regime changes in the GARCH process in most cases analyzed. We also find that regime-switching GARCH models outperform single–regime GARCH specifications when predicting the Value-at-Risk. The results indicate that using MSGARCH models may provide accurate Value-at-Risk predictions, and hence effective in portfolio optimization, pricing of derivatives and risk management etc.

Additive Endogenous Regime Switching GARCH Model

Additive Endogenous Regime Switching GARCH Model

Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices

Recent research suggests that long memory and regime switching can be effectively distinguished, if the cause of the confusion between them is properly controlled for. Motivated by this idea, our study aims to distinguish between them in modelling stock return volatility. We firstly model long memory and regime switching in volatility via the Long-Memory GARCH (LMGARCH) and Markov Regime-Switching GARCH (MRS-GARCH) models, respectively. A theoretical cause of the confusion between those processes is proposed with simulation evidence. Adopting the ideas of existing studies, an MRS-LMGARCH framework is further developed to control for this cause. Our Monte Carlo studies show that this model can effectively distinguish between the pure LMGARCH and pure MRS-GARCH processes. Finally, empirical studies of NASDAQ and S&P 500 index returns are conducted to demonstrate that our MRS-LMGARCH model can provide potentially more reliable estimates of the long-memory parameter, identify the volatility states and outperform both the LMGARCH and MRS-GARCH models.

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

Abstract We 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’s t innovations dominate their Gaussian counterparts both in- and out-of-sample. The dominating specification appears to be a two-regime Student’s t process with correlations which are higher in the turbulent (high-volatility) regime.

Measures of volatility for the Romanian Stock Exchange: a regime switching approach

Abstract This paper aims to identify if regime-switching GARCH models perform better than singlestate GARCH models for the Romanian stock market. There will be used two approaches: in-sample and out-of-sample. All estimations are going to be made for the BET Index, which is the most relevant index from the selected market. The results will be ranked based on statistical loss functions for each of the two considered approaches. These rankings should ensure an accurate comparison for models’ performance and they succeeded to return about the same results as in the relevant literature. Hence, for the in-sample evaluation there was no model which performs best for all loss-functions, but one can notice that for the out-of-sample evaluation the regime-switching models performed better especially on short-term (1-day observation period). All of these results were used further to improve some risk management strategies based on VaR, for which the volatility could be estimated through regimeswitching GARCH models, than considering historical volatility.

A dynamic Markov regime-switching GARCH model and its cumulative impulse response function

This paper concerns with a generalized regime-switching GARCH model to capture dynamic behavior of volatility in financial market. Four-state Markov chain regime-switching is adopted with white noise, stationary, integrated and explosive states. We consider time-dependent transition probabilities of the Markov chain and derive time-dependent probability of each state under the assumption of conditional normality on the noise of the GARCH model. Multi-step ahead volatility is formulated and cumulative impulse response function, which is a measure of persistence in volatility, is discussed. A Monte-Carlo experiment shows the dynamics of the volatilities and time-dependent probabilities as well as the behaviors of the cumulative impulse response functions.

A generalized regime-switching integer-valued GARCH(1, 1) model and its volatility forecasting

A generalized regime-switching integer-valued GARCH(1, 1) model and its volatility forecasting

Regime Related Volatility in Oil Futures Prices

Oil futures prices, which have undergone major changes, maintain an important research topic for academics. Oil prices, which tended to decline for political and economic reasons in the 1990s, fell to as low as 12 US dollars after the Asian Crisis, rising again in 2002. The oil prices, which have fallen again after the 2008 crisis, have not reached the level of 100 US dollars again. This study explains the volatility of petroleum futures contracts as low and high volatility in two regimes by the Markov Regime Switching GARCH model. In the study based on 7077 observations in a long sample period from January 1990 to October 2017, the transition probabilities and durations between two different volatility regimes of oil futures prices are explained. The volatility of the oil futures contract is switching between two regimes with low volatility and high volatility depending on a markov process.