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 introduce advanced idiosyncratic risk (“AI-Risk”), a parsimonious correlated residual correction to a predictive stress CAPM-like factor model, aimed to get more accurate stock-stock correlations. We find that AI-Risk can be significant for stock portfolios. Inclusion of AI-Risk gives a...

In two previous papers we introduced Smart Monte Carlo SMC, more accurate and faster than traditional MC. Here we apply SMC to American Monte Carlo AMC. The main tool is the Feynman-Wiener path integral with a useful binning procedure. We also suggest Prony interpolating functions with...

“Smart Monte Carlo” (SMC) improves accuracy and speed. We extend results in an earlier paper, applying SMC to path-dependent deals and multifactor models. Auxiliary results are a path-to-path distance, an analytic approximation for N-dimensional Gaussian integrals, and time interpolation...

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 Macro Micro (MM) model contains different time scales and deals with risk as it occurs in the real world, especially trend risk. A new methodology - Singular Spectrum Analysis (SSA) – is introduced to identify historical trends, trend volatilities, and noise-reduced trend-trend...

We describe some details of extensions of the Macro Micro (MM) model. Applications include a long-term real-world PFE risk simulation, including realistic quasi-random Macro trends. The details elaborated here include the use of the 3rd order skew Green function to obtain micro mean reversion, a...

In order to characterize a statistical probability distribution p(x) of a variable x, the moments of the distribution are used; the first two of which are the mean and standard deviation. The z-score is often used to characterize data points of x (e.g. outliers with large z-scores). Polynomials...

We introduce a powerful method for cleaning time series - Multi-Channel Singular Spectrum Analysis (MSSA). “Cleaning” means filling data gaps and removing unphysical spikes, which are chronic problems. MSSA utilizes all available information in “time” and “space” with...

We present the framework for a distressed bond model. The utility is as a proxy for calculating the risk of a distressed bond portfolio. We elaborate several possible implementations and give an example