The Asymmetrical Effects of inflation on Tehran Stock Exchange: A Regime Switching Model

2014 ◽  
Vol 4 (12) ◽  
pp. 209
Author(s):  
Abdolnaser Shojaei ◽  
Mohsen Khezri ◽  
Omid Mahmoodi Khoshro
2018 ◽  
Vol 11 (2) ◽  
pp. 169-186 ◽  
Author(s):  
Omokolade Akinsomi ◽  
Yener Coskun ◽  
Rangan Gupta ◽  
Chi Keung Marco Lau

PurposeThis paper aims to examine herding behaviour among investors and traders in UK-listed Real Estate Investment Trusts (REITs) within three market regimes (low, high and extreme volatility periods) from the period June 2004 to April 2016.Design/methodology/approachObservations of investors in 36 REITs that trade on the London Stock Exchange as at April 2016 were used to analyse herding behaviour among investors and traders of shares of UK REITs, using a Markov regime-switching model.FindingsAlthough a static herding model rejects the existence of herding in REITs markets, estimates from the regime-switching model reveal substantial evidence of herding behaviour within the low volatility regime. Most interestingly, the authors observed a shift from anti-herding behaviour within the high volatility regime to herding behaviour within the low volatility regime, with this having been caused by the FTSE 100 Volatility Index (UK VIX).Originality/valueThe results have various implications for decisions regarding asset allocation, diversification and value management within UK REITs. Market participants and analysts may consider that collective movements and market sentiment/psychology are determinative factors of risk-return in UK REITs. In addition, general uncertainty in the equity market, proxied by the impact of the UK VIX, may also provide a signal for increasing herding-related risks among UK REITs.


2019 ◽  
Vol 16 (1) ◽  
pp. 215-225
Author(s):  
Emmanuel K. Oseifuah ◽  
Carl H. Korkpoe

The study used the Markov regime switching model to investigate the presence of regimes in the volatility dynamics of the returns of JSE All-Share Index (ALSI). Volatility regimes are as a result of sudden changes in the underlying economy generating the market returns. In all, twelve candidate models were fitted to the data. Estimates from the regime switching model were compared to the industry standard non-switching GARCH (1,1) using the Deviance Information Criteria (DIC). The results show that the two-regime switching EGARCH model with skewed Student t innovations describes better the return of the JSE Index. Additionally, we backtest the model results in order to confirm our findings that the two-regime switching EGARCH is the best of the models for the sample period.


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