Portfolio Selection with Return Predictability and Periodically Observable Predictive Variables PDF Download
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Author: Hong Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
We consider the optimal portfolio selection problem for a constant relative risk aversion (CRRA) investor who derives utility from his terminal wealth. The stock returns are predictable, but the predictive variables are only periodically observable with noise. We obtain the investor's value function in an explicit form. The theoretical results are then used to study an empirical model after calibration. We show that lack of continuous or precise observation of the predictive variables has a large impact on the optimal trading strategy. We demonstrate how information value changes with risk aversion, investment horizon, observation error volatility and other parameters. In addition, we find that although the benefit of incorporating predictability is significantly reduced due to the periodic and noisy observation, the cost of ignoring predictability is still substantial.
Author: Hong Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
We consider the optimal portfolio selection problem for a constant relative risk aversion (CRRA) investor who derives utility from his terminal wealth. The stock returns are predictable, but the predictive variables are only periodically observable with noise. We obtain the investor's value function in an explicit form. The theoretical results are then used to study an empirical model after calibration. We show that lack of continuous or precise observation of the predictive variables has a large impact on the optimal trading strategy. We demonstrate how information value changes with risk aversion, investment horizon, observation error volatility and other parameters. In addition, we find that although the benefit of incorporating predictability is significantly reduced due to the periodic and noisy observation, the cost of ignoring predictability is still substantial.
Author: Abhay Abhyankar
Publisher:
ISBN:
Category :
Languages : en
Pages : 45
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Book Description
In this paper we investigate the empirical performance of unconditionally efficient portfolios strategies for a number of commonly used predictive variables. These strategies, which optimally utilize asset return predictability in portfolio formation were studied by Hansen and Richard (1987) and Ferson and Siegel (2001). Our criterion is to maximize various ex-post performance measures and we conduct both in-sample as well as out-of-sample analysis. Our analysis allows us to determine the economic value of using different predictor variables and also groups of predictor variables.Overall we find that the optimal use of conditioning information significantly improves the risk-return tradeoff available to a mean-variance investor relative to fixed weight strategies. These findings are consistent across portfolio efficiency measures such as Sharpe ratios, portfolio variance subject to a mean constraint or portfolio mean subject to a volatility constraint as well as measures of economic value such as switching costs.In addition we also compare the performance of the unconditionally efficient strategies with conditionally efficient strategies from an investment-based perspective. We find that the performance of the two strategies is quite different due to the differing response of the portfolio weights of the two strategies to conditioning information.
Author: Marcos Escobar
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
The problem of optimal wealth allocation is solved under the assumptions that interest rates are stochastic and stock returns are predictable with observed and unobserved factors. The stock risk premium is taken to be an affine function of the predictive variables and the stock return volatility is assumed to depend on the observed factor. The latent factor is estimated based on the observations. It is shown that the stock return predictability can significantly impact the optimal bond portfolio. The welfare loss from ignoring learning can be considerable.
Author: Bradley Steele Paye
Publisher:
ISBN:
Category : Asset allocation
Languages : en
Pages : 380
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Book Description
Author: Yacine Ait-Sahalia
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
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Book Description
We study asset allocation when the conditional moments of returns are partly predictable. Rather than first model the return distribution and subsequently characterize the portfolio choice, we determine directly the dependence of the optimal portfolio weights on the predictive variables. We combine the predictors into a single index that best captures time-variations in investment opportunities. This index helps investors determine which economic variables they should track and, more importantly, in what combination. We consider investors with both expected utility (mean-variance and CRRA) and non-expected utility (ambiguity aversion and prospect theory) objectives and characterize their market-timing, horizon effects, and hedging demands.
Author: Min Zhu
Publisher:
ISBN:
Category :
Languages : en
Pages : 34
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Book Description
The inquiries to return predictability are traditionally limited to the first two moments, mean and volatility. Analogously, literature on portfolio selection also stems from a moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regression and copulas, using the quantile approach to extract information in marginal distributions and copulas to capture dependence structure. A nonlinear utility function is proposed for portfolio selection which utilizes the full underlying return distribution. An empirical application to US data highlights not only the predictability of the stock and bond return distributions, but also the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.
Author: Min Zhu
Publisher:
ISBN:
Category : Investment analysis
Languages : en
Pages : 350
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Author: Michael W. Brandt
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
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Book Description
We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.
Author: Yacine Aït-Sahalia
Publisher:
ISBN:
Category : Asset allocation
Languages : en
Pages : 68
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Book Description
We study asset allocation when the conditional moments of returns are partly predictable. Rather than first model the return distribution and subsequently characterize the portfolio choice, we determine directly the dependence of the optimal portfolio weights on the predictive variables. We combine the predictors into a single index that best captures time-variations in investment opportunities. This index helps investors determine which economic variables they should track and, more importantly, in what combination. We consider investors with both expected utility (mean-variance and CRRA) and non-expected utility (ambiguity aversion and prospect theory) objectives and characterize their market-timing, horizon effects, and hedging demands.
Author: Deniz Kebabci
Publisher:
ISBN:
Category :
Languages : en
Pages : 149
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Book Description
How investors should allocate assets to their portfolios in the presence of predictable components in asset returns is a question of great importance in finance. While early studies took the return generating process as given, recent studies have addressed issues such as parameter estimation and model uncertainty. My dissertation develops Bayesian methods for portfolio choice - and industry allocation in particular - under parameter and model uncertainty. The first chapter of my dissertation, Allocation to Industry Portfolios under Markov Switching Returns, addresses the effect of parameter estimation error on the relation between asset holdings and the investment horizon. This paper assumes that returns follow a regime switching process with unknown parameters. Parameter uncertainty is accounted for through a Gibbs sampling approach. After accounting for parameter estimation error, buy-and-hold investors are generally found to allocate less to stocks the longer the investment horizon. When the dividend yield and T-bill rates are included as predictor variables, the effect of these predictor variables is minimal, and the allocation to stocks is still smaller, the longer the investor's horizon. The second chapter of my dissertation, Portfolio Choice Implications of Parameter and Model Uncertainty in Factor Models, uses industry portfolios to examine the implications of incorporating uncertainty about a range of (conditionally) linear factor models. The paper specifically examines a CAPM, a linear factor model with different predictor variables (dividend yield, price to book ratio, price to earnings ratio, and price to sales ratio) and a time-varying CAPM specification. All approaches incorporate parameter uncertainty in a mean-variance framework. Time-varying CAPM specifications are intuitive in the sense that one cannot expect the environment for each industry to stay constant through time, and so the underlying parameters can be expected to be time-varying as well. Accounting for time- variation in market betas improves the portfolio performance as measured, e.g., by the Sharpe ratio compared to both an unconditional CAPM and a linear factor model with different predictor variables. The paper also looks at the implications for portfolio performance of utilizing a Black-Litterman approach versus a standard mean-variance approach in the asset allocation step. The former can be thought as a model averaging approach and thus can be expected to help dealing with model uncertainty besides the parameter estimation uncertainty. The third chapter of my dissertation, Style Investing with Uncertainty, develops methods to look at style investing. This paper analyzes the determinants that affect style investing, such as style momentum, and predictor variables such as different macro variables (e.g. yield spread, inflation, term structure, industrial production, etc.) and looks at how learning about these variables affects the predictability of returns. Uncertainty in this paper is incorporated using a time-varying parameter model. Returns on style portfolios such as value and size appear to be related to inflation and other macro variables.
