Association between Markov regime-switching market volatility and beta risk: Evidence from Dow Jones industrial securities

In this paper, the volatility of the return generating process of the market portfolio and the slope coefficient of the market model is assumed to follow a Markov switching process of order one. The results indicate very strong evidence of volatility switching behaviour in a sample of returns in the S&P500 index. In three of the thirty securities in the Dow Jones index, the estimated slope in the market model show strong switching behaviour. In these three securities the low risk state is more persistent than the high-risk state. For each security we estimate the conditional probabilities that the security is in the high (low) risk state given the market is in the high (low) volatility regime and show that this information can be used to classify securities into three distinct groups. There is no association between these groups and the securities' constant beta estimated in the market model and the Sharpe index. Some directions for further research are discussed.Asset pricing, Markov regime-switching, market volatility, beta risk

Association between Markov regime-switching market volatility and beta risk: Evidence from Dow Jones industrial securities.

In this paper, the volatility of the return generating process of the market portfolio and the slope coefficient of the market model is assumed to follow a Markov switching process of order one. The results indicate very strong evidence of volatility switching behaviour in a sample of returns in the S&P500 index. In three of the thirty securities in the Dow Jones index, the estimated slope in the market model show strong switching behaviour. In these three securities the low risk state is more persistent than the high-risk state. For each security we estimate the conditional probabilities that the security is in the high (low) risk state given the market is in the high (low) volatility regime and show that this information can be used to classify securities into three distinct groups. There is no association between these groups and the securities' constant beta estimated in the market model and the Sharpe index. Some directions for further research are discussed.Asset pricing, Markov regime-switching, market volatility, beta risk

Beta Risk and Regime Shift in Market Volatility

In this paper, we relate the returns in the thirty securities in the Dow Jones index to regime shifts in stock market volatility. We apply a Markov switching process of order one to market volatility and examine the variation in the securities' returns in different volatility regimes. We test the significance of the risk premium in different market regimes and we find evidence of relationship between market volatility and securities beta risk.Markov regime-switching, market volatility, beta risk

Beta Risk and Regime Shift in Market Volatility

In this paper, we relate security returns in the thirty securities in the Dow Jones index to regime shifts in the market portfolio (S&P500) volatility. We model market volatility as a multiple-state Markov switching process of order one and estimate non-diversifiable security risk (beta) in the different market volatility regimes. We test the significance of the premium of the beta risk associated with the different market regimes and find evidence of a relationship between security return and beta risk when conditional on the up and down market movement.Markov regime-switching, Market volatility, Beta risk.

Non-linear Modelling of the Australian Business Cycle using a Leading Indicator

This paper develops a new non-linear model to analyse the business cycle by exploiting the relationship between the asymmetrical behaviour of the cycle and leading indicators. The model proposed is an innovations form of the structural model underlying simple exponential smoothing that is augmented by a latent Markov switching process. Furthermore, the probabilities that drive the Markov process vary with the growth of the leading indicator. The proposed model is used to analyse the Australian business cycle using the gross domestic product as a proxy and the industrial materials prices index as the exogenous leading indicator influencing the transition probabilities. Model parameters are estimated using a Gibbs sampling algorithm and subsequently used for forecasting purposes.Structural model; Markov switching regime; Gibbs sampling; Business cycle; Leading indicator.

Exponential Smoothing Methods of Forecasting and General ARMA Time Series Representations

The focus of this paper is on the relationship between the exponential smoothing methods of forecasting and the integrated autoregressive-moving average models underlying them. In this paper we derive, for the first time, the general linear relationship between their parameters. A method, suitable for implementation on computer, is proposed to determine the pertinent quantities in this relationship. It is illustrated on common forms of exponential smoothing. It is also applied to a new seasonal form of exponential smoothing with seasonal indexes which always sum to zero

Bayesian analysis of a structural model with regime switching

Abstract not availabl

Association Between Markov Regime-switching Market Volatility and Beta Risk: Evidence from Dow Jones Industrial Securities

In this paper, the volatility of the return generating process of the market portfolio and the slope coefficient of the market model is assumed to follow a Markov switching process of order one. The results indicate very strong evidence of volatility switching behaviour in a sample of returns in the S&P500 index. In three of the thirty securities in the Dow Jones index, the estimated slope in the market model show strong switching behaviour. In these three securities the low risk state is more persistent than the high-risk state. For each security we estimate the conditional probabilities that the security is in the high (low) risk state given the market is in the high (low) volatility regime and show that this information can be used to classify securities into three distinct groups. There is no association between these groups and the securities' constant beta estimated in the market model and the Sharpe index. Some directions for further research are discussed

Non-linear Modelling of the Australian Business Cycle Using a Leading Indicator

This paper develops a new non-linear model to analyse the business cycle by exploiting the relationship between the asymmetrical behaviour of the cycle and leading indicators. The model proposed is an innovations form of the structural model underlying simple exponential smoothing that is augmented by a latent Markov switching process. Furthermore, the probabilities that drive the Markov process vary with the growth of the leading indicator. The proposed model is used to analyse the Australian business cycle using the gross domestic product as a proxy and the industrial materials prices index as the exogenous leading indicator influencing the transition probabilities. Model parameters are estimated using a Gibbs sampling algorithm and subsequently used for forecasting purposes