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...

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

Path integrals are useful and general. Here we show how to calculate Greeks using path integrals. In particular we exhibit the solution to an otherwise troublesome calculation – gamma for a digital option. A subsequent paper will present more detail

We construct “Hybrid Value at Risk” (HYVAR) that is an arbitrary mixture of Historical VAR and Monte Carlo VAR. The procedure is capable of retaining both the correlation matrix of the original time series and also jumps/‘fat tails'. For this reason HYVAR provides more realistic scenarios,...

Previously we introduced Singular Spectrum Analysis SSA and its multivariate extension MSSA as a powerful tool for cleaning data. Here we compare MSSA with the data filling algorithm M-REM (Multivariate Regularized Expectation Maximization). We compare theoretical methodology, numerical...

Data cleaning in the real world has to cope with new data arriving (or failing to arrive) as time passes, and which may be bad data. We illustrate MSSA data cleaning with a real-time historical simulation on some problematic data. The example also serves to determine some MSSA algorithm...

This paper introduces a powerful method for detecting and fixing unphysical spikes in time series. The method utilizes Multiple Singular Spectrum Analysis (MSSA) to define local market trends used to identify outlier data spikes that are not caused by market movements, and then effectively...

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...