SSRN Author: Xipei YangXipei Yang SSRN Content
http://www.ssrn.com/author=1832390
http://www.ssrn.com/rss/en-usThu, 31 Aug 2017 01:51:14 GMTeditor@ssrn.com (Editor)Thu, 31 Aug 2017 01:51:14 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Introduction to Noise-Reduced Correlations Using Singular Spectrum AnalysisWe 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 correlations based on these smoothed series. We demonstrate that SSA-based correlation estimates have less noise than standard correlation estimates between unsmoothed series using: the signal-to-noise ratio, and distances from noise using polynomials generalizing the z-score and random matrix theory constructs. New useful analytic estimates for all eigenvalues of a random matrix are described. SSA-based correlations also enjoy superior time stability. Technical aspects are given in four accompanying papers, including extensive analyses of time stability and the noise-reduction tests described in this short paper.
http://www.ssrn.com/abstract=3028236
http://www.ssrn.com/1621543.htmlWed, 30 Aug 2017 09:33:22 GMTREVISION: SSA, Random Matrix Theory, and Noise-Reduced CorrelationsThis 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 Matrix Theory (RMT) constructs. In each case, the correlations obtained using SSA-smoothed time series are further from noise than are conventional correlations. “Noise” here is defined by a zero-correlation Wishart random matrix WRM composed of correlations between series filled with independent Gaussian random numbers.
http://www.ssrn.com/abstract=2808027
http://www.ssrn.com/1529470.htmlTue, 20 Sep 2016 18:31:44 GMTREVISION: Non-Leading Eigenvalue Distributions, RMT, and CorrelationsWe 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 present tractable analytic approximate WRM results that we used in the analysis: (1) leading and non-leading eigenvalue distributions of a WRM, (2) eigenvalue spacing of WRMs, and (3) eigenvector components of WRMs.
http://www.ssrn.com/abstract=2808055
http://www.ssrn.com/1529444.htmlTue, 20 Sep 2016 17:38:27 GMTREVISION: Smart Monte Carlo, Path Integrals, and American OptionsIn 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 regression advantages. We present American put option results. We obtain a smaller price error and a smoother optimal exercise boundary than with standard methods. We comment on multidimensional SMC.
http://www.ssrn.com/abstract=2808186
http://www.ssrn.com/1528794.htmlSat, 17 Sep 2016 16:23:21 GMTREVISION: Predicting Equity Crises, Critical Exponents, and Earthquakes - IIWe present further encouraging evidence for the Critical Exponent Earthquake Crisis (CEEC) Model that gives the probabilities of equity crises one year in advance. The CEEC model uses suitable precursor signals and is agnostic regarding dynamical origins of crises. The precursors accumulate in time between crises, like precursors to some earthquakes. The model uses a sophisticated noise filter to separate out crisis signals. The main metric is the anomalous exponent of an equity series describing the difference of the data variance scaling exponent from the Brownian variance scaling exponent of 1. No extra non-equity variables are used. The CEEC Model results for predicting crises are not perfect, but are much better than chance, including out-of-sample tests. The details here supplement our previous CEEC crisis paper (2013).
In another paper (2016) we give details showing that various markets - not just equities - that are already in crisis are on the average described by a ...
http://www.ssrn.com/abstract=2811719
http://www.ssrn.com/1528777.htmlSat, 17 Sep 2016 12:47:01 GMTREVISION: Macro-Micro, Trends vs. Noise, and SSA - IIWe 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 random time distribution for the Macro component, sliding down the yield curve, and approximate no-arbitrage in the MM model.
http://www.ssrn.com/abstract=2808170
http://www.ssrn.com/1528774.htmlSat, 17 Sep 2016 12:26:50 GMTREVISION: Describing Crises with a Critical Exponent of the Reggeon Field TheoryWe present evidence that markets in crisis can be described by a critical exponent of the nonlinear-diffusion Reggeon Field Theory, calculated 40 years ago, with no free parameters, translated to finance. We propose this as a benchmark for average crisis behavior, to which individual crises can be rich or cheap. In another paper we present a quantitative model for the probability of equity crises in advance. An earlier paper contained a summary.
http://www.ssrn.com/abstract=2808149
http://www.ssrn.com/1528772.htmlSat, 17 Sep 2016 12:24:39 GMT