This paper contains new results for helping to understand financial crises. First, we present a new model for obtaining the probability of equity crises within one year in advance, and we test it. Second and separately, various markets already in crises appear quantitatively related to a theory...

“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...

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

We showed that Singular Spectrum Analysis (SSA) applied to time series yields better correlations for risk simulations. This involved comparing SSA-based correlations with standard correlations and to noise, a zero correlation Wishart random matrix (WRM). We complete this testing here. We also...

This is the second paper presenting noise-reduced, stable correlations for long-term risk measurement. We smooth time series using Singular Spectrum Analysis (SSA) and then form the correlations from these smoothed time series. These correlations have superior time stability and are cleaned of...

This is the third paper in a series devoted to obtaining noise-reduced, stable correlations by smoothing time series using Singular Spectrum Analysis, or SSA. Here we show that the SSA-based correlations are superior in terms of noise reduction, employing a number of simple tests using Random...

We introduce a methodology from geophysics, Singular Spectrum Analysis (SSA), to obtain stable, noise-cleaned correlations for long term risk (e.g. counterparty risk). SSA is applied to time series to smooth them in a robust manner. The SSA-smoothed time series are then used to obtain the...

We summarize new results for estimating correlations for use in risk management. These estimates have better behavior than traditional estimation approaches from both a business standpoint and a technical standpoint. We smooth time series using Singular Spectrum Analysis (SSA) and compute...

The financial crisis of 2008 had many putative causes. Psychology was an important driver for human decisions underlying these causes. However, quantitative financial models have no “knobs” to dial psychology parameters, and so arguably cannot possibly cope with financial crises. We have no...