Sequential Learning and Economic Benefits from Dynamic Term Structure Models

Tomasz Dubiel-Teleszynski (), Konstantinos Kalogeropoulos () and Nikolaos Karouzakis ()
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Tomasz Dubiel-Teleszynski: Department of Statistics, London School of Economics, London WC2A 2AE, United Kingdom
Konstantinos Kalogeropoulos: Department of Statistics, London School of Economics, London WC2A 2AE, United Kingdom
Nikolaos Karouzakis: Alba Graduate Business School, The American College of Greece, Athens 115 28, Greece; University of Sussex Business School, University of Sussex, Brighton BN1 9RH, United Kingdom

Management Science, 2024, vol. 70, issue 4, 2236-2254

Abstract: We explore the statistical and economic importance of restrictions on the dynamics of risk compensation from the perspective of a real-time Bayesian learner who predicts bond excess returns using dynamic term structure models (DTSMs). The question on whether potential statistical predictability offered by such models can generate economically significant portfolio benefits out-of-sample is revisited while imposing restrictions on their risk premia parameters. To address this question, we propose a methodological framework that successfully handles sequential model search and parameter estimation over the restriction space in real time, allowing investors to revise their beliefs when new information arrives, thus informing their asset allocation and maximizing their expected utility. Empirical results reinforce the argument of sparsity in the market price of risk specification since we find strong evidence of out-of-sample predictability only for those models that allow for level risk to be priced and, additionally, only one or two of these risk premia parameters to be different than zero. Most importantly, such statistical evidence is turned into economically significant utility gains, across prediction horizons, different time periods and portfolio specifications. In addition to identifying successful DTSMs, the sequential version of the stochastic search variable selection scheme developed can be applied on its own and offer useful diagnostics monitoring key quantities over time. Connections with predictive regressions are also provided.

Keywords: bond return predictability; economic value; dynamic term structure models; Bayesian sequential learning; parameter & model uncertainty (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:70:y:2024:i:4:p:2236-2254

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