Option implied information and portfolio allocation

Abstract

A key concern in the financial literature is if asset prices can be predicted. In the past, studies mainly investigated on the predictability of past asset prices or financial ratios. This thesis takes an alternative route and centres on information derived from options. Utilising options is appealing for two major reasons: First, the structure enables to obtain a forward looking or implied estimate. Second, the cross-section gives detailed insight about the future states of the underlying asset. The starting point of this thesis is to investigate on option implied risk-aversion. A simulation study is developed to analyse risk-aversion estimates from option prices. The simulation suggests that risk-aversion varies drastically over time and the volatility of the underlying has a strong impact. Subsequently, the thesis addresses the use of option implied information within portfolio allocation. The considered investor is infinitely lived with a time varying risk-premium. The dynamics of the risk-premium are derived using various implied predictor variables. They are extracted considering different assumptions about the underlying asset price density. The findings suggest that the option implied risk-premium is the preferable predictor and flexible densities do not benefit notably. Furthermore, performance measures suggest that option implied predictors are superior to historical predictors and similar to financial ratios. The final chapter elaborates further on the previously considered portfolio allocation. It proposes a novel estimation method to obtain consistent estimates of the risk-premium process. The method relies on the observable option implied risk-premium. It avoids the use of a multivariate framework when working with financial ratios. Only with the proposed method, the dynamics of the state variable are economically reasonable. Further, the applied performance measures improve.

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Identifiers

ORCID for Jose Olmo: orcid.org/0000-0002-0437-7812

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