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19
Forecast Evaluation and Combination
 IN G.S. MADDALA AND C.R. RAO (EDS.), HANDBOOK OF STATISTICS
, 1996
"... It is obvious that forecasts are of great importance and widely used in economics and finance. Quite simply, good forecasts lead to good decisions. The importance of forecast evaluation and combination techniques follows immediately forecast users naturally have a keen interest in monitoring and ..."
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Cited by 85 (24 self)
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It is obvious that forecasts are of great importance and widely used in economics and finance. Quite simply, good forecasts lead to good decisions. The importance of forecast evaluation and combination techniques follows immediately forecast users naturally have a keen interest in monitoring and improving forecast performance. More generally, forecast evaluation figures prominently in many questions in empirical economics and finance, such as: Are expectations rational? (e.g., Keane and Runkle, 1990; Bonham and Cohen, 1995) Are financial markets efficient? (e.g., Fama, 1970, 1991) Do macroeconomic shocks cause agents to revise their forecasts at all horizons, or just at short and mediumterm horizons? (e.g., Campbell and Mankiw, 1987; Cochrane, 1988) Are observed asset returns "too volatile"? (e.g., Shiller, 1979; LeRoy and Porter, 1981) Are asset returns forecastable over long horizons? (e.g., Fama and French, 1988; Mark, 1995)
Should investors avoid all actively managed mutual funds? A study in Bayesian performance evaluation
 Journal of Finance
, 2001
"... This paper analyzes mutualfund performance from an investor’s perspective. We study the portfoliochoice problem for a meanvariance investor choosing among a riskfree asset, index funds, and actively managed mutual funds. To solve this problem, we employ a Bayesian method of performance evaluatio ..."
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Cited by 61 (5 self)
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This paper analyzes mutualfund performance from an investor’s perspective. We study the portfoliochoice problem for a meanvariance investor choosing among a riskfree asset, index funds, and actively managed mutual funds. To solve this problem, we employ a Bayesian method of performance evaluation; a key innovation in our approach is the development of a flexible set of prior beliefs about managerial skill. We then apply our methodology to a sample of 1,437 mutual funds. We find that some extremely skeptical prior beliefs nevertheless lead to economically significant allocations to active managers. ACTIVELY MANAGED EQUITY MUTUAL FUNDS have trillions of dollars in assets, collect tens of billions in management fees, and are the subject of enormous attention from investors, the press, and researchers. For years, many experts have been saying that investors would be better off in lowcost passively managed index funds. Notwithstanding the recent growth in index funds, active managers still control the vast majority of mutualfund assets. Are any of these active managers worth their added expenses? Should investors avoid all actively managed mutual funds? Since Jensen ~1968!, most studies have found that the universe of mutual funds does not outperform its benchmarks after expenses. 1 This evidence indicates that the average active mutual fund should be avoided. On the other hand, recent studies have found that future abnormal returns ~“alphas”! can be forecast using past returns or alphas, 2 past fund
Bayesian Inference in Asset Pricing Tests
, 1990
"... We test the meanvariance efficiency of a given portfolio using a Bayesian framework. Our test is more direct than Shanken's (1987b), because we impose a prior on all the parameters of the multivariate regression model. The approach is also easily adapted to other problems. We use Monte Carlo numeri ..."
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Cited by 22 (2 self)
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We test the meanvariance efficiency of a given portfolio using a Bayesian framework. Our test is more direct than Shanken's (1987b), because we impose a prior on all the parameters of the multivariate regression model. The approach is also easily adapted to other problems. We use Monte Carlo numerical integration to accurately evaluate 9Odimensional integrals. Posteriorodds ratios are calculated for 12 industry portfolios from 19261987. The sensitivity of the inferences to the prior is investigated by using three different distributions. The probability that the given portfolio is meanvariance efficient is small for a range of plausible priors.
Stock return predictability and asset pricing models
, 2001
"... Asset pricing models based on rational timevarying expected returns or on equity characteristics imply restrictions on stock return predictability. This paper develops a framework for investigating these pricing restrictions through the use of an economic metric that is based on asset allocation wi ..."
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Cited by 19 (2 self)
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Asset pricing models based on rational timevarying expected returns or on equity characteristics imply restrictions on stock return predictability. This paper develops a framework for investigating these pricing restrictions through the use of an economic metric that is based on asset allocation with estimation risk. The evidence shows that when portfolio weights are unconstrained, the deviations from the pricing models are economically significant. Incorporating constraints on leverage and short equity positions results in a sharp reduction in these deviations, which disappear in some cases, yet they remain economically significant in most cases. Finally, imposing factor model restrictions on predictive regressions generate smaller outofsample Sharpe ratios and larger mean square forecast errors. The results carry implications for various applications in financial economics using risk factors or equity characteristics as benchmarks.
Dynamic Portfolio Choice with Parameter Uncertainty and Economic Value of Analysts’ Recommendations, working paper
, 2004
"... We derive a closedform solution for the optimal portfolio of a nonmyopic utility maximizer who has incomplete information about the “alphas, ” or abnormal returns of risky securities We show that the hedging component induced by learning about the expected return can be a substantial part of the d ..."
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Cited by 8 (0 self)
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We derive a closedform solution for the optimal portfolio of a nonmyopic utility maximizer who has incomplete information about the “alphas, ” or abnormal returns of risky securities We show that the hedging component induced by learning about the expected return can be a substantial part of the demand. Using our methodology, we perform an “ex ante ” empirical exercise, which shows that the utility gains resulting from optimal allocation are substantial in general, especially for long horizons, and an “ex post ” empirical exercise, which shows that analysts ’ recommendations are not very
return predictability, conditional asset pricing models and portfolio selection, Doctoral thesis
"... I examine an investor’s portfolio allocation problem across multiple risky assets in the presence of return predictability when, in addition to the predictability evidence, the investor uses conditional asset pricing models to guide him in the portfolio selection decision. I also explore how the unc ..."
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Cited by 5 (2 self)
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I examine an investor’s portfolio allocation problem across multiple risky assets in the presence of return predictability when, in addition to the predictability evidence, the investor uses conditional asset pricing models to guide him in the portfolio selection decision. I also explore how the uncertainty associated with the model dynamics affects the investor’s optimal portfolio. To analyze this, I introduce Bayesian techniques that have not been used before in the asset pricing literature. Using a market index and a small capitalization or a value portfolio, I find that the sample evidence on predictability plays a major role in the investor’s portfolio allocation decision. The optimal portfolio also depends on his beliefs about the extent to which this predictability can be attributed to time variation in risk premia and betas. Finally, I show that the portfolio allocation Consider an investor who observes a number of variables that may predict stock returns and has some knowledge of asset pricing theory. How can he use this information to allocate funds between a riskless asset and a portfolio of risky assets? In this paper, I address this question by examining the portfolio allocation problem of a Bayesian investor when returns may be predictable. In addition
Daily Exchange Rate Behaviour and Hedging of Currency Risk
 Journal of Applied Econometrics
, 1999
"... Exchange rates typically exhibit timevarying patterns in both means and variances. ..."
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Cited by 5 (4 self)
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Exchange rates typically exhibit timevarying patterns in both means and variances.
The Conditional Distribution of Real Estate Returns: Are higher moments time varying?
, 2002
"... and participants at the 2001 CambridgeMaastricht Symposium for Real Estate Finance and Economics for helpful comments. Remaining errors are, of course, the responsibility of the authors. Previous research has shown that the returns on individual properties and listed property securities are skewed ..."
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Cited by 2 (2 self)
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and participants at the 2001 CambridgeMaastricht Symposium for Real Estate Finance and Economics for helpful comments. Remaining errors are, of course, the responsibility of the authors. Previous research has shown that the returns on individual properties and listed property securities are skewed (Lizieri and Ward 2001, Young and Graff 1995 and Liu et al. 1992). This claim is investigated in the context of listed UK property companies and US REITs. In particular, the shape of the conditional distribution of total monthly returns is examined for a group of 20 UK companies and 20 REITS listed continuously since 1970 and 1977, respectively. Also investigated is the claim of Young and Graff that the skewness found in property returns varies over time. Using the model of Hansen (1994) it is found that while a large portion of property security returns in the sample do exhibit skewness in the conditional distribution only in a few instances is there evidence of time variation in the skewness parameter. When time varying skewness is found there is little evidence to suggest it is associated with the economic cycle. The link between time varying skewness models and downside risk measures is also discussed and estimates of conditional downside risk are calculated for those companies exhibiting the time varying skewness property.
Bayesian and NonBayesian Approaches to Scientific Modeling and Inference in Economics and Econometrics
"... After brief remarks on the history of modeling and inference techniques in economics and econometrics, attention is focused on the emergence of economic science in the 20th century. First, the broad objectives of science and the PearsonJeffreys' "unity of science" principle will be reviewed. Second ..."
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Cited by 1 (0 self)
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After brief remarks on the history of modeling and inference techniques in economics and econometrics, attention is focused on the emergence of economic science in the 20th century. First, the broad objectives of science and the PearsonJeffreys' "unity of science" principle will be reviewed. Second, key Bayesian and nonBayesian practical scientific inference and decision methods will be compared using applied examples from economics, econometrics and business. Third, issues and controversies on how to model the behavior of economic units and systems will be reviewed and the structural econometric modeling, time series analysis (SEMTSA) approach will be described and illustrated using a macroeconomic modeling and forecasting problem involving analyses of data for 18 industrialized countries over the years since the 1950s. Point and turning point forecasting results and their implications for macroeconomic modeling of economies will be summarized. Last, a few remarks will be made ...
Daily Exchange Rate Behaviour and Daily Exchange Rate Behaviour and Hedging of Currency Risk Hedging of Currency Risk
, 2001
"... We construct models which enable a decisionmaker to analyze the implications of typical time series patterns of daily exchange rates for currency risk management. Our approach is Bayesian where extensive use is made of Markov chain Monte Carlo methods. The effects of several model characteristics ( ..."
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We construct models which enable a decisionmaker to analyze the implications of typical time series patterns of daily exchange rates for currency risk management. Our approach is Bayesian where extensive use is made of Markov chain Monte Carlo methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed disturbance densities) are investigated in relation to the hedging strategies. Consequently, we can make a distinction between statistical relevance of model specifications, and the economic consequences from a risk management point of view. We compute payoffs and utilities from several alternative hedge strategies. The results indicate that modelling time varying features of exchange rate returns may lead to improved hedge behaviour within currency overlay management.