Suppose a fund manager uses predictors in changing port-folio allocations over time. How does predictability translate into portfolio decisions? To answer this question we derive a new model within the Bayesian framework, where managers are assumed to modulate the systematic risk in part by...

Market option prices in last 20 years confirmed deviations from the Black and Scholes (BS) models assumptions, especially on the BS implied volatility. Implied binomial trees (IBT) models capture the variations of the implied volatility known as "volatility smile". They provide a discrete...

Modelling portfolio credit risk is one of the crucial challenges faced by financial services industry in the last few years. We propose the valuation model of collateralized debt obligations (CDO) based on copula functions with up to three parameters, with default intensities estimated from...

Based on the theory of static replication of variance swaps we assess the sign and magnitude of variance risk premiums in foreign exchange markets. We find significantly negative risk premiums when realized variance is computed from intraday data with low frequency. As a likely consequence of...

This study uses a comprehensive data set of VIX and CDS markets to propose pairs trading strategies that represent the dynamic relation between market risk and credit risk in an equilibrium framework with a common non stationary factor. This involves the analysis of price discovery between VIX...

It is crucial to model, quantify and understand the variables and dynamics that underlie the well-known extreme behaviour of spot electricity prices in wholesale markets. We explicitly model the conditional volatility and skewness of electricity prices. A GARCH-type model allowing for...

Statistical theory has been relatively absent in the exercise of estimating parameters of an option pricing model from cross-sectional data at a fixed point of calendar time. The cross-sectional data typically consists of prices for options at various strikes and maturities at market close. The...

This paper uses an exclusive proprietary data set of European Credit Derivatives and VIX markets, covering a sample of 5 to 7 years, to study the nature of the link between credit risk and market risk, widely acknowledged in the academic literature. This allows us to establish cointegration in...

Artificial Neural Networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between...

We consider the subject of approximating tail probabilities in the general compound renewal process framework, where severity data are assumed to follow a heavy-tailed law (in that only the first moment is assumed to exist). By using weak convergence of compound renewal processes to Lévy...