Technical paper/Monte Carlo simulation
Stressed in Monte Carlo
Stressed in Monte Carlo
Confidence in controlling risk measures
Insurers increasingly use stochastic simulation approaches for estimating risk capital, but numerical errors are rarely measured. A control variate method can improve the accuracy dramatically without increasing the number of simulations.
Valuation of commodity-based swing options
Reseach Papers
Fast correlation Greeks by adjoint algorithmic differentiation
Adjoint methods have recently been proposed as an efficient way to calculate risk through Monte Carlo simulation. Luca Capriotti and Mike Giles extend these ideas and show how adjoint algorithmic differentiation allows for fast calculation of price…
A rotationally invariant technique for rare event simulation
Because of their low probability, including extreme events in Monte Carlo calculations of the value-at-risk of a credit-risky portfolio requires many simulations. Here, Susanne Klöppel, Ranja Reda and Walter Schachermayer demonstrate a geometrically…
Calculation of variable annuity market sensitivities using a pathwise methodology
Under traditional finite difference methods, the calculation of variable annuity sensitivities can involve multiple Monte Carlo simulations, leading to high computational cost. A pathwise approach reduces this dramatically, while providing an unbiased…
Simulations with exact means and covariances
Attilio Meucci presents a simple method to generate scenarios from multivariate elliptical distributions with given sample means and covariances, and shows an application to the risk management of a book of options
Fast Monte Carlo Bermudan Greeks
In recent years, much effort has been devoted to improving the efficiency of the Libor market model. Matthias Leclerc, Qian Liang and Ingo Schneider extend the pioneering work of Giles & Glasserman (2006) and show how fast calculations of Monte Carlo…
Accelerated ensemble Monte Carlo simulation
Traditional vanilla methods of Monte Carlo simulation can be extremely time-consuming if accurate estimation of the loss distribution is required. Kevin Thompson and Alistair McLeod show that the ensemble Monte Carlo method, introduced here,…
Juggling snowballs
Previous work on the valuation of cancellable snowball swaps in the Libor market model suggested the use of nested Monte Carlo simulations was needed to obtain accurate prices. Here, Christopher Beveridge and Mark Joshi introduce new techniques that…
Beyond Black-Litterman in practice
In principle, the copula-opinion pooling (COP) approach extends the Black-Litterman methodology to non-normally distributed markets and views. However, the implementations of the COP framework presented so far rely on restrictive quasi-normal assumptions…
Computation methods - Smoking adjoints: fast Monte Carlo Greeks
Monte Carlo calculation of price sensitivities for hedging is often very time- consuming. Michael Giles and Paul Glasserman develop an adjoint method to accelerate the calculation. The method is particularly effective in estimating sensitivities to a…
Smoking adjoints: fast Monte Carlo Greeks
Monte Carlo calculation of price sensitivities for hedging is often very time-consuming. Michael Giles and Paul Glasserman develop an adjoint method to accelerate the calculation. The method is particularly effective in estimating sensitivities to a…
A credit loss control variable
Viktor Tchistiakov, Jeroen de Smet and Peter-Paul Hoogbruin explain and demonstrate how the efficiency of Monte Carlo simulation in valuing a portfolio of credit risky exposures is improved by the use of the Vasicek distribution as a control variable. An…
Operational and market risks of a regulated power utility
Victor Dvortsov and Ken Dragoon present an analytical method for including market and operational risks when estimating utility portfolio value-at-risk
Evaluating credit risk models using loss density forecasts
The evaluation of credit portfolio risk models is an important issue for both banks and regulators. It is impeded by the scarcity of credit events, long forecasthorizons, and data limitations. To make efficient use of available information, the…
VAR: history or simulation?
Greg Lambadiaris, Louiza Papadopoulou, George Skiadopoulos and Yiannis Zoulis assess theperformance of historical and Monte Carlo simulation in calculating VAR, using data from theGreek stock and bond market. They find that while historical simulation…