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...

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...

Under standard assumptions the deterministic reduced-form credit risk model is not capable of accurately pricing the two fundamental credit risk instruments - bonds and credit default swaps (CDS) - simultaneously. Using a data set of euro-denominated corporate bonds and CDS our paper quantifies...

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A...

In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate...

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We...

This paper investigates the lead-lag relationship in daily returns and volatilities between price movements of FTSE/ASE-20 futures and the underlying FTSE/ASE-20 cash index of the Athens Stock Exchange. The results suggest that there is a bidirectional causality between spot and futures returns,...

Many pricing and risk management models need credit spread curves as an input. In the corporate bond market the estimation of credit spread curves is not trivial. Most issuers have only too few bonds outstanding and frequently these bonds are denominated in different currencies. To ensure a...

The traditional method of credit spread estimation is based on subtracting independently estimated risk-free and risky term structures of interest rates which in many cases yields unrealistically shaped and often irregular credit spread curves. A parsimonious joint estimation of the risk-free...

We develop two neo-classical methods for function approximations, the generalized stochastic sampling (gSS) and the functional tensor train (fTT) methods, that are high-performing alternatives to generic deep neural networks (DNNs) currently routinely proposed for function approximations in...