scholarly journals Regime Switching Model of US Crude Oil and Stock Market Prices: 1859 to 2013

2014 ◽  
Author(s):  
Mehmet Balcilar ◽  
Rangan Gupta ◽  
Stephen M. Miller
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Regime Switching ◽  
Market Prices ◽  
Regime Switching Model ◽  
Switching Model

scholarly journals Regime switching model of US crude oil and stock market prices: 1859 to 2013

2015 ◽  
Vol 49 ◽  
pp. 317-327 ◽  
Author(s):  
Mehmet Balcilar ◽  
Rangan Gupta ◽  
Stephen M. Miller
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Regime Switching ◽  
Market Prices ◽  
Regime Switching Model ◽  
Switching Model

Do global sentiment shocks spillover towards emerging and frontier markets?

2020 ◽  
Vol 47 (3) ◽  
pp. 433-465 ◽  
Author(s):  
Mobeen Ur Rehman ◽  
Nicholas Apergis
Keyword(s):  
Stock Market ◽  
Regime Switching ◽  
Linear Models ◽  
Market Returns ◽  
Markov Regime Switching ◽  
Stock Market Returns ◽  
Content Type ◽  
Regime Switching Model ◽  
Switching Model ◽  
Investor Sentiments

PurposeThis study aims to investigate the impact of sentiment shocks based on US investor sentiments, bearish and bullish market conditions. Earlier studies, though very few, only consider the effect of investor sentiments on stock returns of emerging frontier Asian (EFA) markets.Design/methodology/approachThis study uses the application of regime switching model because of its capability to explore time-varying causality across different regimes unlike traditional linear models. The Markov regime switching model uses regime switching probabilities for capturing the potential asymmetries or non-linearity in a model, in this study’s case, thereby adjusting investor sentiments shocks to stock market returns.FindingsThe results of the Markov regime switching method suggests that US sentiment, bullish and bearish market shocks act as a main contributors for inducing variation in EFA stock market returns. The study’s non-parametric robustness results highlight an asymmetric relationship across the mean series, whereas a symmetric relationship across variance series. The study also reports Thailand as the most sensitive market to global sentiment shocks.Research limitations/implicationsThe sensitivity of the EFA markets to these global sentiment shocks highlights their sensitivity and implications for investors relying merely on returns correlation and spillover. These findings also suggest that spillover from developed to emerging and frontier equity markets only in the form of returns following traditional linear models may not be appropriate.Practical implicationsThis paper supports the behavioral aspect of investors and resultant spillover from developed market sentiments to emerging and frontier market returns across international equity markets offering more rational justification for an irrational behavior.Originality/valueThe study’s motivation to use the application of regime switching models is because of its capability to explore time-varying causality across different regimes unlike traditional linear models. The Markov regime switching model uses regime switching probabilities for capturing the potential asymmetries or non-linearity in a model, in the study’s case, thereby adjusting investor sentiments shocks to stock market returns. It is also useful of the adjustment attributable to exogenous events.

Minimum variance hedging with bivariate regime-switching model for WTI crude oil

Energy ◽  
2011 ◽  
Vol 36 (5) ◽  
pp. 3050-3057 ◽  
Author(s):  
Jui-Cheng Hung ◽  
Yi-Hsien Wang ◽  
Matthew C. Chang ◽  
Kuang-Hsun Shih ◽  
Hsiu-Hsueh Kao
Keyword(s):  
Crude Oil ◽  
Regime Switching ◽  
Minimum Variance ◽  
Regime Switching Model ◽  
Switching Model

A segmented regime-switching model with its application to stock market indices

2011 ◽  
Vol 38 (10) ◽  
pp. 2241-2252 ◽  
Author(s):  
Beibei Guo ◽  
Yuehua Wu ◽  
Hong Xie ◽  
Baiqi Miao
Keyword(s):  
Stock Market ◽  
Regime Switching ◽  
Regime Switching Model ◽  
Switching Model
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