scholarly journals Time-varying equity premium forecasts based on industry indexes

2020 ◽  
Vol 9 ◽  
pp. 132-142
Author(s):  
Nuno Silva
Keyword(s):  
Stock Market ◽  
Real Time ◽  
Linear Models ◽  
Equity Premium ◽  
Time Varying ◽  
Markov Switching Models ◽  
Switching Models ◽  
Constant Coefficients ◽  
Business Conditions ◽  
Mean Variance

Various studies report that the ability of industry indexes to predict the broad market disappeared during the most recent years. I revisit this theme using more flexible switching models and imposing economically motivated constraints on the predictions. My results show that traditional constant coefficients linear models are unable to forecast the stock market over the period considered, but restricting the equity premium to be non-negative, five industries predict the market. I also show that the Markov-switching models exhibit a dismal performance, which is even worse than the ones from the constant coefficients model. Finally, I test a model with two regimes- recession and expansion- which are identified in real-time through the Arouba-Diebold-Scotti Business Conditions Index. Using this model, I find that 8 out of 33 industries can successfully forecast the market. Furthermore, a mean-variance investor who bases his decisions on it obtains sizeable utility gains, relative to another investor who uses, exclusively, the historical returns.

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 Forecasting Brazilian output and its turning points in the presence of breaks: a comparison of linear and nonlinear models

2006 ◽  
Vol 36 (1) ◽  
pp. 5-46 ◽  
Author(s):  
Brisne J. V. Céspedes ◽  
Marcelle Chauvet ◽  
Elcyon C. R. Lima
Keyword(s):  
Business Cycle ◽  
Structural Breaks ◽  
Linear Models ◽  
Nonlinear Models ◽  
Markov Switching Models ◽  
Real Gdp ◽  
Switching Models ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Linear And Nonlinear

This paper compares the forecasting performance of linear and nonlinear models under the presence of structural breaks for the Brazilian real GDP growth. The Markov switching models proposed by Hamilton (1989) and its generalized version by Lam (1990) are applied to quarterly GDP from 1975:1 to 2000:2 allowing for breaks at the Collor Plans. The probabilities of recessions are used to analyze the Brazilian business cycle. The in-sample and out-of-sample forecasting ability of growth rates of GDP of each model is compared with linear specifications and with a non-parametric rule. We find that the nonlinear models display a better forecasting performance than linear models. The specifications with the presence of structural breaks are important in obtaining a representation of the Brazilian business cycle and their inclusion improves considerably the models forecasting performance within and out-of-sample.

A Study on Information Spillover Effects from NASDAQ 100 to KOSDAQ 50 Index Futures Markets

2003 ◽  
Vol 11 (1) ◽  
pp. 145-167
Author(s):  
Gyu Hyeon Mun ◽  
Jeong Hyo Hong
Keyword(s):  
Stock Market ◽  
Real Time ◽  
Futures Markets ◽  
Spillover Effects ◽  
Time Varying ◽  
Volatility Spillover ◽  
Index Futures ◽  
Conditional Mean ◽  
Time Basis ◽  
Information Spillover

This paper studies the information spillover effects over price and volatility across countries by using open-to-close (daytime) returns and close-to-open (overnight) returns of NASDAQ 100 and KOSDAQ 50 index futures data from January 1, 2001 to December 31, 2001. Based on the time-varying AR(1)-GARCH (1,1)-M models, we document that statistically significant conditional mean and volatility spillover effects from the daytime returns of NASDAQ 100 index futures to both overnight returns and daytime returns of KOSDAQ 50 index futures were observed. We also find that there were information spillover effects from overnight returns of NASDAQ 100 index futures to daytime returns of KOSDAQ 50 index futures returns because investors in Korean stock markets can get information on U.S. stock market movement on real time basis due to the ECN transaction with its trading hour overlapped. Finally, we find that the daytime returns of KOSDAQ 50 index futures significantly influence the overnight and daytime returns of the NASDAQ 100 index futures.

Re-examining covariance risk dynamics in international stock markets using quantile regression analysis

2011 ◽  
Vol 61 (1) ◽  
pp. 33-59 ◽  
Author(s):  
M. Li ◽  
S. Yen
Keyword(s):  
Quantile Regression ◽  
Stock Markets ◽  
Markov Switching ◽  
Garch Models ◽  
Time Varying ◽  
Bear Market ◽  
Markov Switching Models ◽  
International Stock Markets ◽  
Switching Models ◽  
Risk Dynamics

This investigation is one of the first to adopt quantile regression (QR) technique to examine covariance risk dynamics in international stock markets. Feasibility of the proposed model is demonstrated in G7 stock markets. Additionally, two conventional random-coefficient frameworks, including time-varying betas derived from GARCH models and state-varying betas implied by Markov-switching models, are employed and subjected to comparative analysis. The empirical findings of this work are consistent with the following notions. First, the beta smile (beta skew) curve for the Italian, U.S. and U.K. (Canadian, French and German) markets. That is, covariance risk among global stock markets in extremely bull and/or bear market states is significantly higher than in stable periods. Additionally, the Japanese market provides a special case, and its beta estimate at extremely bust state is significantly lower, not higher than that at the middle region. Second, the quantile-varying betas are identified as possessing two key advantages. Specifically, the comparison of the system with quantile-varying betas against that with time-varying betas implied by GARCH models provides meaningful implications for correlation-volatility relationship among international stock markets. Furthermore, the quantile-varying beta design in this study relaxes a simple dual beta setting implied by Markov-switching models of Ramchand — Susmel (1998) and can identify dynamics of asymmetry in betas.

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.

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