Hull and White extend Ho and Lee's no-arbitrage model of the short interest rate to include mean reversion. This addition eliminates the problem of negative interest rates and has found wide application. To implement their model, Hull and White employ a sequential search process to identify the...

We extend the short rate model of Vasicek (1977) to include jumps in the local mean. Conditions ensuring existence of a unique equivalent martingale measure are given, implying that the model is arbitrage-free and complete. We develop efficient numerical methods for computation of zero coupon...

This paper presents a simple yet powerful new approach for valuing American options by simulation. The key to this approach is to use least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable in path-dependent and...

Low-discrepancy (quot;quasi-randomquot;) sampling methods offer the possibility of significantly enhancing the simulation models used in derivative valuation by using non-random quot;randomquot; numbers to generate simulated price paths. The idea is that a set of randomly generated values for...

According to IFRS 9, an Entity shall assess – by performing a quantitative assessment – the relevance of the modification of the time value of money element, i.e. the modification of the interest that can be observed, e.g. in all the instruments whose underlying interest rate tenors are...

This paper is an introduction to the measurement of market risk in financial markets, with examples drawn mainly from commodity markets. In particular, we present the concept of VaR, its limits, the problems related to its estimation and backtesting. This is done at single asset and at portfolio...

In this paper we develop efficient Monte Carlo methods for estimating American option sensitivities. The problem can be re-formulated as how to perform sensitivity analysis for a stochastic optimization problem when it has model uncertainty. We introduce a generalized infinitesimal perturbation...

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...

In this paper we consider the object oriented implementation of numerical algorithms where arithmetic operators (add, mult, exp) operate on objects with more complex structure (compared to floating point numbers). Examples are objects representing vectors instead of scalars, random variables,...

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...