We present a multi-period mean-variance optimization program which allows for a joint optimization of the balance and off-balance sheet. Our first finding is the proof of a conjecture of Li and Ng (2000), Leippold, Trojani and Vanini (2004, 2003) about the equivalence of the original...

Geometric Arbitrage Theory, where a generic market is modelled with a principal fibre bundle and arbitrage corresponds to its curvature, is applied to credit markets to model default risk and recovery, leading to closed form no arbitrage characterizations for corporate bonds.

In this work, we identify the most general measure of arbitrage for any market model governed by It\^o processes. We show that our arbitrage measure is invariant under changes of num\'{e}raire and equivalent probability. Moreover, such measure has a geometrical interpretation as a gauge...

Optimal asset allocation well-fitting investors' goals is a pressing challenge in risk management. Making a step forward to the Sharpe ratio, the parameter-dependent Sortino-Satchell, Generalized Rachev and Farinelli-Tibiletti performance ratios are suggested for personalizing asset allocation....

As the assumption of normality in return distributions is relaxed, classic Sharpe ratio and its descendants become questionable tools for constructing optimal portfolios. In order to overcome the problem, asymmetrical parameter-dependent performance ratios have been recently proposed in the...

We propose two structural models for stochastic losses given default which allow to model the credit losses of a portfolio of defaultable financial instruments. The credit losses are integrated into a structural model of default events accounting for correlations between the default events and...

Purpose – Recent literature discusses the persistence of skewness and tail risk in hedge fund returns. The aim of this paper is to suggest an alternative skewness measure, Azzalini's skewness parameter delta, which is derived as the normalized shape parameter from the skew-normal distribution....