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Chapter 15 Bayesian Model Averaging in the Presence of Structural Breaks

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This chapter develops a return forecasting methodology that allows for instability in the relationship between stock returns and predictor variables, model uncertainty, and parameter estimation uncertainty. The predictive regression specification that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values by employing Bayesian model averaging. The implications of these three sources of uncertainty and their relative importance are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical investor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.

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... Chien [9] used a Lagrange Multiplier unit root test [26] to examine the issue of whether regime changes had broken down the stability of the ripple effect in Taiwan's housing market. Ravazzolo et al. [36] utilized Bayesian model averaging, an ensemble method, to estimate structural breaks in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values with application in the stock market. Vizek and Posedel [47] used threshold autoregressive (TAR) and momentum TAR (M-TAR) models, defined thresholds in terms of the changes in the error term, to test if housing prices in the United States, United Kingdom, Spain and Ireland were characterized by threshold effects. ...
... There are methods in Business and Economics, Lagrange Multiplier unit root test [9] and Bayesian model averaging [36] and GARCH model [3], which can detect thresholds in the relationships between variables. These models detect thresholds in the distribution of the time series variables, and are not very useful in terms of interpretation of the relationship. ...
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This paper gives a comprehensive review of the literature on the interaction between real stock returns, inflation, and money growth, with a special emphasis on the role of monetary policy. This is an area of research that has interested monetary and financial economists for a long time. Monetary economists have been interested in the question whether money has any effect on real stock prices, while financial economists have investigated whether equity is a good hedge against inflation. Empirical studies show that money can be helpful in predicting future stock returns. Empirical evidence also suggest that equity is not a good hedge against inflation in the short run but may be so in the long run. The short-run negative relation between stock returns and inflation can easily be explained by theoretical models. If the central bank conducts a countercyclical monetary policy this will result in a negative relation between inflation and stock returns, while if it conducts a procyclical policy we could observe a positive relation. According to both theoretical and empirical studies investors receive an inflation risk premium for holding equity. Copyright 2001 by Blackwell Publishers Ltd
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Numerous studies report that standard volatility models have low explanatory power, leading some researchers to question whether these models have economic value. We examine this question by using conditional mean-variance analysis to assess the value of volatility timing to short-horizon investors. We find that the volatility timing strategies outperform the unconditionally efficient static portfolios that have the same target expected return and volatility. This finding is robust to estimation risk and transaction costs.
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We examine how the evidence of predictabilityinasset returns a#ects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We #nd that even after incorporating parameter uncertainty, there is enough predictability in returns to make investors allocate substantially more to stocks, the longer their horizon. Moreover, the weak statistical signi#cance of the evidence for predictability makes it important to take estimation risk into account; a long-horizon investor who ignores it mayover-allocate to stocks by a sizeable amount. # Graduate School of Business, University of Chicago. I am indebted to John Campbell and Gary Chamberlain for guidance and encouragement. I also thank an anonymous referee, the editor Ren#e Stulz, and seminar participants at Harvard, the Wharton School, Chicago Business School, the Sloan School at MIT, UCLA, Rochester, NYU, Columbia, Stanford, IN...
  • K French
  • G Schwert
  • R Stambaugh
French, K., G. Schwert, and R. Stambaugh (1987), Expcted Stock Returns and Volatility, Journal of Financial Economics, 19, 3-29.
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Giordani, P., R. Kohn, and D. van Dijk (2006), A Unified Approach to Nonlinearity, Outliers and Structural Breaks, Journal of Econometrics, to appear.