Markov Switching Models: An Example for a Stock Market Index

2019 ◽  
Author(s):  
Erik Kole
Keyword(s):  
Stock Market ◽  
Markov Switching ◽  
Stock Market Index ◽  
Markov Switching Models ◽  
Market Index ◽  
Switching Models

scholarly journals Did the Stock Market Regime Change after the Inauguration of the New Cabinet in Japan?

2014 ◽  
Vol 2 (1) ◽  
pp. 98
Author(s):  
Chikashi Tsuji
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
Markov Switching ◽  
Sample Period ◽  
Full Sample ◽  
Markov Switching Models ◽  
Switching Models ◽  
Market Regime

This paper explored whether the Japanese stock market regime changed after the inauguration of the new Abe cabinet in Japan. Our application of Markov switching models to the Japanese stock price index returns and examinations of the price spreads in terms of the Japanese stock price indices derive the following evidence. First, (1) after the Abe cabinet started, regime of the Japanese stock markets changed. Second, (2) the regimes as to the JASDAQ Index and Tokyo Stock Exchange (TSE) Mothers Index more strongly and earlier changed than that of TOPIX. Third, (3) in our full sample period from January 4, 2011 to March 20, 2014, average positive price spreads over TOPIX were observed as to the JASDAQ, TSE Mothers, TOPIX Small, and TSE Second Section Index.

scholarly journals A influência da pandemia COVID-19 na volatilidade dos índices de mercado de ações (Ibovespa): Aplicação do modelo Markov Switching Autoregressivo / The influence of the COVID-19 pandemic on the volatility of stock market index (Ibovespa): Application of the Markov Switching Autoregressive model

2021 ◽  
Vol 3 (3) ◽  
pp. 2445-2458
Author(s):  
Carlos Alberto Gonçalves Da Silva
Keyword(s):  
Stock Market ◽  
Autoregressive Model ◽  
Markov Switching ◽  
Stock Market Index ◽  
Market Index ◽  
Lehman Brothers

O presente artigo utiliza o modelo Markov Switching Autoregressivo de dois estados desenvolvido por Hamilton (1989), para capturar mudanças de regime tanto na média quanto na variância dos retornos mensais do índice de mercado de ações (Ibovespa) entre janeiro de 2000 e março de 2021. Na matriz de transação e persistência dos regimes, verifica-se que o regime 1 (baixa volatilidade) é mais persistente, ou seja, a probabilidade de permanecer neste regime em período posterior é de 96,49% e no regime 2 (alta volatilidade) a probabilidade de continuar neste regime no período t+1 corresponde a 48,55%. Os resultados obtidos do modelo MS(2)-AR(1) detectaram  momento das mudanças de regimes dos retornos, por causa do atentado terrorista de 11/09/2001, do momento de transição da política brasileira (vitória de Lula na eleição presidencial 2002), crises financeiras 2008 (falência do banco de investimentos dos EUA, o Lehman Brothers) e a pandemia COVID-19 (2020/2021).

scholarly journals A Markov Switching Vector Error Correction Model on Oil Price and Gold Price Effect on Stock Market Returns

2013 ◽  
Vol 5 (7) ◽  
pp. 331-336
Author(s):  
Seuk Wai Phoong ◽  
Siok Kun Sek .
Keyword(s):  
Stock Market ◽  
Error Correction ◽  
Markov Switching ◽  
Oil Price ◽  
Vector Error Correction Model ◽  
Stock Market Index ◽  
Correction Model ◽  
Vector Error Correction ◽  
Market Index ◽  
Vector Error

Stock market index represent a country growth and always as an interest for economist and statisticians. In this paper, the effect of oil price and gold price on stock market index on Malaysia, Singapore, Thailand and Indonesia are investigated and a two-regime Markov Switching Vector Error Correction model is used to examine the nonlinear properties model. Moreover, a two regime mean adjusted Markov Switching Vector Error Correction model is used in the study to capture the filtered and smoothed probabilities of the time series sequence in the economic model. Results found that the oil price and gold price affect the movement of the Malaysia, Singapore, Thailand and Indonesia stock market index and there is an asymmetric cycle since 97% of the total sample size is recorded in the growth state.

Presidential Elections and the Stock Market: Comparing Markov-Switching and Fractionally Integrated GARCH Models of Volatility

2004 ◽  
Vol 12 (3) ◽  
pp. 296-322 ◽  
Author(s):  
David Leblang ◽  
Bumba Mukherjee
Keyword(s):  
Stock Market ◽  
Presidential Election ◽  
Statistical Models ◽  
Stock Price ◽  
Markov Switching ◽  
Price Volatility ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Markov Switching Models ◽  
Switching Models

Existing research on electoral politics and financial markets predicts that when investors expect left parties—Democrats (US), Labor (UK)—to win elections, market volatility increases. In addition, current econometric research on stock market volatility suggests that Markov-switching models provide more accurate volatility forecasts and fit stock price volatility data better than linear or nonlinear GARCH (generalized autoregressive conditional heteroskedasticity) models. Contrary to the existing literature, we argue here that when traders anticipate that the Democratic candidate will win the presidential election, stock market volatility decreases. Using two data sets from the 2000 U.S. presidential election, we test our claim by estimating several GARCH, exponential GARCH (EGARCH), fractionally integrated exponential GARCH (FIEGARCH), and Markov-switching models. We also conduct extensive forecasting tests—including RMSE and MAE statistics as well as realized volatility regressions—to evaluate these competing statistical models. Results from forecasting tests show, in contrast to prevailing claims, that GARCH and EGARCH models provide substantially more accurate forecasts than the Markov-switching models. Estimates from all the statistical models support our key prediction that stock market volatility decreases when traders anticipate a Democratic victory.

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

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