Monte Carlo simulation
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…
Speed tests
Counterparty Credit Risk
Valid Assumptions Required: Monte Carlo VaR
Brett Humphreys discusses the many decisions associated with the calculation of a Monte Carlo value-at-risk.
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…
Back to the future
Current developments in exotic interest rate products push the demand for more sophisticatedinterest rate models. Here, Jesper Andreasen presents a new class of stochastic volatility multifactoryield curve models enabling quick calibration and efficient…
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…
The Monte Carlo mindset
There is a rich seam to be mined in the provision of tools to calculate counterparty credit risk. Clive Davidson looks at what's on offer so far, and what could be coming on to the market.
Simulating spots
Abstract: The use of Monte Carlo simulation is becoming increasingly importantin energy trading and risk management. Here, Les Clewlow and ChrisStrickland present the first in a series of articles looking at the implementation of simulationtechniques and…
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
To store or not to store
Natural Gas
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…
A true test for value-at-risk
The three classic approaches for measuring portfolio value-at-risk do not compare like with like, argues Richard Sage. Here he presents a test portfolio to highlight the differences between calculation methods