Hedge Funds Managerial Skill Revisited: A Quantile Regression Approach

In this paper we revisit the question of measurement of hedge fund managerial skill. Using a plethora of different models, from the simplest ones, employing a linear regression approach, to the more advanced ones, employing a quantile regression approach, we are able to identify and exploit managerial skill. The quantile regression approach enables us to produce robust and accurate estimates of the managerial skill utilizing two different sources of information: (a) the distribution information, regarding how the relationship between the return of the fund and a given variable varies across the conditional quantiles of returns and (b) factor information, regarding the different models that can be used for pricing inference. We show that estimates of the managerial skill based on quantile regressions and robust combination are superior compared to the relevant estimates from the linear pricing equations

Risk Classification for Claim Counts and Losses Using Regression Models for Location, Scale and Shape

This paper presents and compares different risk classi?cation models for the frequency and severity of claims employing regression models for location, scale and shape. The differences between these models are analyzed through the mean and the variance of the annual number of claims and the costs of claims of the insureds, who belong to different risk classes and interesting results about claiming behaviour are obtained. Furthermore, the resulting a priori premiums rates are calculated via the expected value and standard deviation principles with independence between the claim frequency and severity components assumed

Bonus-Malus Systems with Two Component Mixture Models Arising from Different Parametric Families

Two component mixture distributions defined so that the component distributions do not necessarily arise from the same parametric family are employed for the construction of Optimal Bonus-malus Systems (BMS) with frequency and severity components. The proposed modelling framework is used for the first time in actuarial literature research and includes an abundance of alternative model choices to be considered by insurance companies when deciding on their Bonus-Malus pricing strategies. Furthermore, we advance one step further by assuming that all the parameters and mixing probabilities of the two component mixture distributions are modelled in terms of covariates, extending our previous work in Tzougas, Vrontos and Frangos (2014). Applying Bayes theorem we derive optimal BMS either by updating the posterior probability of the policyholders' classes of risk or by updating the posterior mean and the posterior variance. The resulting tailor-made premiums are calculated via the expected value and variance principles and are compared to those based only on the a posteriori criteria. The use of the variance principle in a Bonus-Malus ratemaking scheme in a way that takes into consideration both the number and the costs of claims based on both the a priori and the a posterior classification criteria has not yet been proposed and can alter the resulting premiums significantly, providing the actuary with useful alternative tariff structures

Quantile regression analysis of hedge fund strategies

Extending previous work on hedge fund pricing, this paper introduces the idea of modelling the conditional quantiles of hedge fund returns using a set of risk factors. Quantile regression analysis provides a way of understanding how the relationship between hedge fund returns and risk factors changes across the distribution of conditional returns. We propose a Bayesian approach to model comparison which provides posterior probabilities for different risk factor models that can be used for model averaging. The most relevant risk factors are identified for different quantiles and compared with those obtained for the conditional expectation model. We find differences in factor effects across quantiles of returns, which suggest that the standard conditional mean regression method may not be adequate for uncovering the risk-return characteristics of hedge funds. We explore potential economic impacts of our approach by analysing hedge fund single strategy return series and by constructing style portfolios

Hedge fund pricing and model uncertainty

This article uses Bayesian model averaging to study model uncertainty in hedge fund pricing. We show how to incorporate heteroscedasticity, thus, we develop a framework that jointly accounts for model uncertainty and heteroscedasticity. Relevant risk factors are identified and compared with those selected through standard model selection techniques. The analysis reveals that a model selection strategy that accounts for model uncertainty in hedge fund pricing regressions can be superior in estimation/inference. We explore potential impacts of our approach by analysing individual funds and show that they can be economically important. © 2007 Elsevier B.V. All rights reserved