Dynamic correlation or tail dependence hedging for portfolio selection

In this paper we solve for the optimal portfolio allocation in a dynamic setting, where both conditional correlation and dependence between extremes are considered. We demonstrate that there are substantial economic costs for investors in disregarding either the dynamics of conditional correlation or the clustering of extreme events. The welfare loss increases dramatically with the investment horizon, during bad economic and market conditions, and for low levels of the agent’s relative risk aversion. We illustrate that both correlation hedging demands and intertemporal hedges due to increased tail dependence have distinct portfolio implications and they cannot act as substitutes to each other. There is a substantial utility loss for disregarding dependence between extreme realizations, even when dynamic conditional correlation has already been accounted for, and vice versa. Our results are robust to the sample period, the choice of the dependence structure, and both varying levels of average correlation and tail dependence coefficients

Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

YesAlthough many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress

Option valuation with conditional heteroskedacity and nonnormality

We provide results for the valuation of European-style contingent claims for a large class of specifications of the underlying asset returns. Our valuation results obtain in a discrete time, infinite state space setup using the no-arbitrage principle and an equivalent martingale measure (EMM). Our approach allows for general forms of heteroskedasticity in returns, and valuation results for homoskedastic processes can be obtained as a special case. It also allows for conditional nonnormal return innovations, which is critically important because heteroskedasticity alone does not suffice to capture the option smirk. We analyze a class of EMMs for which the resulting risk-neutral return dynamics are from the same family of distributions as the physical return dynamics. In this case, our framework nests the valuation results obtained by Duan (1995) and Heston and Nandi (2000) by allowing for a time-varying price of risk and nonnormal innovations. We provide extensions of these results to more general EMMs and to discrete-time stochastic volatility models, and we analyze the relation between our results and those obtained for continuous-time models. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies