We will investigate valuation of derivatives with payoff defined as a nonlinear though close to linear function of tradable underlying assets. Derivatives involving Libor or swap rates in arrears, i.e. rates paid in a wrong time, are a typical example. It is generally tempting to replace the...

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...

We present two methods based on functional principal component analysis (FPCA) for the estimation of smooth derivatives of a sample of random functions, which are observed in a more than one-dimensional domain.We apply eigenvalue decomposition to a) the dual covariance matrix of the derivatives,...

This work discusses the calibration of instantaneous Libor correlations in the Libor market model. We extend existing calibration strategies by incorporation of spread option implied correlation information. The correlation structure implied by CMS spread options observed in the present-day's...

Abstract: In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model with a constant recovery rate outperforms the market practice of directly comparing bonds' credit spreads to default swap premiums. We find that the model works...

We propose a simple multidimensional jump-diffusion process for pricing exotic derivatives with multiple underlyings. This process ensures the possibility of sudden drops in asset prices, fits several well-known empirical properties of asset returns, and incorporates dependencies between...

Implied volatility and other forward-looking measures of option-implied uncertainty help investors carefully evaluate market sentiment and expectations. We construct several measures of implied uncertainty in European government bond futures. In the first part, we create new volatility indices,...

We develop an accurate valuation setup for freight options, featuring an exponential mean-reverting model for the freight rate with distinct reversion scales for its jump and diffusion components. We calibrate to Baltic option prices and analyze the freight rate dynamics. More specifically, we...

We consider a seasonal mean-reverting model for energy commodity prices with jumps and Heston-type stochastic volatility, as well as three nested models for comparison. By exploiting the affine form of the log-spot models, we develop a general valuation framework for futures and discrete...

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...