We show that VAR calculation speedup of an order of magnitude can be obtained using Smart Monte Carlo with a sophisticated interpolator. As a byproduct, we give some encouraging numerical results for evaluating N-dimensional Gaussian integrals without doing any integrals at all

We present “Smart Monte Carlo” or SMC, improving the efficiency of Monte Carlo (MC) simulations. SMC has two “stages”. The first stage, run adaptively for each deal, produces equivalent results to standard MC simulation using fewer calls to the time-consuming pricing functions. The...

The inherent assumption with most Monte Carlo techniques is that one may ignore autocorrelations, but doing so compromises the quality of the prediction from the data. Simulations that do not take account of autocorrelation will not properly model reality, as there is significant autocorrelation...

This paper provides an efficient way to generate a set of random choices on a set of budgets which satisfy the Generalised Axiom of Revealed Preferences (GARP), that is, they are consistent with utility maximisation. The choices are drawn from an approximate uniform distribution on the...

Bayesian methods have become increasingly popular in the past two decades. With the constant rise of computational power even very complex models can be estimated on virtually any modern computer. Moreover, interest has shifted from conditional mean models to probabilistic distributional models...

In several scientific fields, like bioinformatics, financial and macro-economics, important theoretical and practical issues exist that involve multimodal data distributions. We propose a Bayesian approach using mixtures distributions to approximate accurately such data distributions. Shape and...

Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...

Benchmarking methods are widely used in the regulation of firms in network industries working under heterogeneous exogenous environments. In this paper we compare three recently developed estimators, namely conditional DEA (Daraio and Simar, 2005, 2007b), latent class SFA (Orea and Kumbhakar, 2004;...

Describes the behavior of financial markets as functions of the variables 'price return' and 'time' based on the net difference between ask and bid volumes over a unit period, thereby suggesting that at least a negative non-trivial price return extreme exists for a unit period. This admittedly...

Bayesian inference for DSGE models is typically carried out by single block random walk Metropolis, involving very high computing costs. This paper combines two features, adaptive independent Metropolis-Hastings and parallelisation, to achieve large computational gains in DSGE model estimation....