A discussion on the innovation distribution of the Markov regime-switching GARCH model

Shi, Yanlin; Feng, Lingbing

doi:10.1016/j.econmod.2015.11.018

Cited by 23 publications

(8 citation statements)

References 37 publications

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“…Guidolin, 2011), allowing for fat-tailed innovations can improve both in-sample fit and out-of-sample forecasting performance of MS GARCH models, as pointed out, e.g. by Ardia (2009), Klaassen (2002), and Shi and Feng (2016). Due to the dependence structure implied by the multivariate t distribution, this also holds for Pelletier's (2006) model where volatility is regime-independent; see Section 4 for a detailed discussion and illustration.…”

Section: Introductionmentioning

confidence: 81%

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

Haas

Liu

2018

Studies in Nonlinear Dynamics &Amp; Econometrics

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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 Markovswitching 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.

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Section: Introductionmentioning

confidence: 81%

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

Haas

Liu

2018

Studies in Nonlinear Dynamics &Amp; Econometrics

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“…Furthermore, in relation to the first point, the robust methodology may improve the reliability of a fitted structural‐change model. For instance, using a Markov‐switching‐type (MS) model, Shi and Feng (2016) demonstrate that even the popular quasimaximum likelihood estimator is not consistent, if tail events (outliers) do exist in the data set. Haas and Paolella (2012) suggest that this may be caused by the fact that the assumed Gaussian distribution cannot accommodate such extreme observations, within an MS‐type model.…”

Section: Discussionmentioning

confidence: 99%

Robust information share measures with an application on the international crude oil markets

Shi

2021

Journal of Futures Markets

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This paper proposes a robust estimation approach of the popular information share using a robust vector error correction model. Via simulation studies, we show that the proposed measures lead to more accurate estimates than the existing measures in the presence of outliers. The proposed measures are then investigated using high‐frequency crude oil prices data of the West Texas Intermediate and Brent over November 2019–October 2020. The results suggest that the abnormally large price movement in April 2020 may cause biased estimates of the ordinary measures, whereas the robust measures produce rather consistent results.

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“…One major problem of the standard GARCH model is that it assumes that the conditional volatility follows only one regime over the entire period (Shi & Feng, 2016). To solve this Hamilton (1985Hamilton ( , 1989) proposed Markov Regime-Switching (MRS) models to allow for parameters to transit between state spaces.…”

Section: Public Interest Statementmentioning

confidence: 99%

“…To solve this Hamilton (1985Hamilton ( , 1989) proposed Markov Regime-Switching (MRS) models to allow for parameters to transit between state spaces. Recently, many studies have incorporated MRS specification into GARCH framework known as MRS-GARCH models (e.g., Gao et al, 2018;Naeem et al, 2019;Shi & Feng, 2016;Zhang et al, 2019;Zolfaghari & Sahabi, 2017). These models have been extensively examined on stock markets (e.g., Abounoori et al, 2016;Shi & Feng, 2016;Zhipeng & Shenghong, 2018).…”

Section: Public Interest Statementmentioning

confidence: 99%

Impact of exchange rate on uncertainty in stock market: Evidence from Markov regime-switching GARCH family models

Zolfaghari

Hoseinzade

2020

Cogent Economics & Finance

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We employ the Markov Regime-Switching GARCH (MRS-GARCH) family models under the normal, Student's t-, and GED distributions to measure the uncertainty of the industry index returns (IIR) of Tehran Stock Exchange over the period of 2013-2019. The models distinguish between two different regimes in both conditional mean and conditional variance. The results show that the MRS-EGARCH-in-mean (MRS-EGARCH-M) models under GED and Student's t-distributions have the best performance to model the IIR volatility. We find evidence of regime-switching behaviour in Iran's stock market. After removing the forecastable component (expected variation) from the best fitted models, we measure the time series of the IIR uncertainty (unforecastable component) and estimate the impact of exchange rate fluctuations on them using an autoregressive distributed lag (ARDL) model. We find that foreign exchange rate fluctuations have a significant and distinct impact on the IIR uncertainty across various regimes. The results show that the exchange rate generally has a negative and positive impact on the IIR uncertainty for export and import-oriented industries, respectively, under both regimes.

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A discussion on the innovation distribution of the Markov regime-switching GARCH model

Cited by 23 publications

References 37 publications

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

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

Robust information share measures with an application on the international crude oil markets

Impact of exchange rate on uncertainty in stock market: Evidence from Markov regime-switching GARCH family models

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