Revisiting the bull and bear markets notions in the Tunisian stock market: New evidence from multi-state duration-dependence Markov-switching models

2016 ◽  
Vol 59 ◽  
pp. 529-545 ◽  
Author(s):  
Azza Bejaoui ◽  
Adel Karaa
Keyword(s):  
Stock Market ◽  
Markov Switching ◽  
Duration Dependence ◽  
Markov Switching Models ◽  
Bear Markets ◽  
State Duration ◽  
Switching Models ◽  
New Evidence ◽  
Bull And Bear Markets

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.

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.

scholarly journals A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1030
Author(s):  
Oscar V. De la Torre-Torres ◽  
Evaristo Galeana-Figueroa ◽  
José Álvarez-García
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Portfolio Performance ◽  
Trading Costs ◽  
Weighting Method ◽  
Treasury Bills ◽  
Markov Switching Models ◽  
Switching Models ◽  
Asset Portfolio ◽  
Stock Portfolio

In the present paper, we test the benefit of using Markov-Switching models and volatility futures diversification in a Euro-based stock portfolio. With weekly data of the Eurostoxx 50 (ESTOXX50) stock index, we forecasted the smoothed regime-specific probabilities at T + 1 and used them as the weighting method of a diversified portfolio in ESTOXX50 and ESTOSS50 volatility index (VSTOXX) futures. With the estimated smoothed probabilities from 9 July 2009 to 29 September 2020, we simulated the performance of three theoretical investors who paid different trading costs and invested in ESTOXX50 during calm periods (low volatility regime) or VSTOXX futures and the three-month German treasury bills in distressed or highly distressed periods (high and extreme volatility regimes). Our results suggest that diversification benefits hold in the short-term, but if a given investor manages a two-asset portfolio with ESTOXX50 and our simulated portfolios, the stock portfolio’s performance is enhanced significantly, in the long term, with the presence of trading costs. These results are of use to practitioners for algorithmic and active trading applications in ESTOXX50 ETFs and VSTOXX futures.

scholarly journals Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

2021 ◽  
Vol 3 (8) ◽  
Author(s):  
Majid Javari
Keyword(s):  
Environmental Planning ◽  
Rainfall Variability ◽  
Markov Switching ◽  
Daily Rainfall ◽  
First Order ◽  
Markov Switching Models ◽  
Markov Transition Matrix ◽  
Switching Models ◽  
Markov Transition ◽  
Modeling And Forecasting

AbstractThis paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.

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