Stock Return Predictability: A Bayesian Model Selection Perspective

Cremers, K. J. Martijn

doi:10.1093/rfs/15.4.1223

Cited by 326 publications

(166 citation statements)

References 38 publications

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“…The Bayesian model averaging is a quite general decision tool under various uncertainties, of which the parameter and model uncertainties are special cases. Cremers (2002) and Avramov (2004) apply it to aggregate predictive information across non-nested models.…”

Section: Bayesian Approachmentioning

confidence: 99%

Robust portfolios: contributions from operations research and finance

2009

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In this paper we provide a survey of recent contributions to robust portfolio strategies from operations research and finance to the theory of portfolio selection. Our survey covers results derived not only in terms of the standard mean-variance objective, but also in terms of two of the most popular risk measures, mean-VaR and mean-CVaR developed recently. In addition, we review optimal estimation methods and Bayesian robust approaches.

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Section: Bayesian Approachmentioning

confidence: 99%

Robust portfolios: contributions from operations research and finance

2009

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“…The idea was initially introduced by [32] and allows one to incorporate the uncertainty regarding the variety of available models [32]. It has been applied in statistics [33][34][35] and econometrics [36,37].…”

Section: Bayesian Techniques For Forecast Combination (Bma)mentioning

confidence: 99%

Electricity Price Forecasting by Averaging Dynamic Factor Models

2016

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Abstract:In the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in the task, both in the short and long run. However, specifying a single model for the unobserved factors is difficult, and it cannot be guaranteed that such a model exists. In this paper, model averaging is employed to overcome this difficulty, with the expectation that electricity prices would be better forecast by a combination of models for the factors than by a single model. Although our procedure is applicable in other markets, it is illustrated with an application to forecasting spot prices of the Iberian Market, MIBEL (The Iberian Electricity Market). Three combinations of forecasts are successful in providing improved results for alternative forecasting horizons.

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“…This approach can, however, only be applied under the assumption that the decision-maker has a single prior and that she shows no aversion against the ambiguity inherent in the model uncertainty. 16 Raftery et al [57] provide the technical details of Bayesian model averaging and Avramov [3], Cremers [16], and Dangl and Halling [17] are applications to return prediction. Bayesian model averaging treats model uncertainty just as an additional source of variation.…”

Section: Model Uncertaintymentioning

confidence: 99%

Risk Control in Asset Management: Motives and Concepts

Dangl

Randl

Zechner

2015

Innovations in Quantitative Risk Management

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In traditional portfolio theory, risk management is limited to the choice of the relative weights of the riskless asset and a diversified basket of risky securities, respectively. Yet in industry, risk management represents a central aspect of asset management, with distinct responsibilities and organizational structures. We identify frictions that lead to increased importance of risk management and describe three major challenges to be met by the risk manager. First, we derive a framework to determine a portfolio position's marginal risk contribution and to decide on optimal portfolio weights of active managers. Second, we survey methods to control downside risk and unwanted risks since investors frequently have nonstandard preferences, which make them seek protection against excessive losses. Third, we point out that quantitative portfolio management usually requires the selection and parametrization of stylized models of financial markets. We, therefore, discuss risk management approaches to deal with parameter uncertainty, such as shrinkage procedures or resampling procedures, and techniques of dealing with model uncertainty via methods of Bayesian model averaging.We thank Victor DeMiguel, Matthias Scherer, Neal Stoughton, Raman Uppal, Arne Westerkamp, Rudi Zagst and an anonymous referee for helpful comments.

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Stock Return Predictability: A Bayesian Model Selection Perspective

Cited by 326 publications

References 38 publications

Robust portfolios: contributions from operations research and finance

Robust portfolios: contributions from operations research and finance

Electricity Price Forecasting by Averaging Dynamic Factor Models

Risk Control in Asset Management: Motives and Concepts

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