The following Matlab project contains the source code and Matlab examples used for nonlinear regression shapes. The art of fitting a nonlinear regression model often starts with choosing a model form.

The following Matlab project contains the source code and Matlab examples used for approximate the distribution of a compound random variable by panjer recursion. . Example: plot distribution of compound Poisson/Lognormal n = 1e4; xmax = 100; lam = 10; mu = 0; sig = 1; [gl,gu,xp,GL,GU]=panjer(@(x)logncdf(x,mu,sig),0,lam,xmax/n,n); plot(xp,GL,'b-',xp,GU,'r-'); See help for more details.

The following Matlab project contains the source code and Matlab examples used for var for portfolio stocks . Value-at-Risk calculation for portfolio stocks using variance-covariance, historical and MonteCarlo methods. Portfolio can be larger as you want including either the risk factor (stock index, currency, etc.)

The following Matlab project contains the source code and Matlab examples used for goodness of fit (modified).  GFIT2 Computes goodness of fit for regression model  USAGE:        [gf] = gfit2(t,y)        [gf] = gfit2(t,y,gFitMeasure)        [gf] = gfit2(t,y,gFitMeasure,options)  INPUT:            t: matrix or vector of target values for regression model            y: matrix or vector of output from regression model.

The following Matlab project contains the source code and Matlab examples used for modeling variable annuities with matlab. Highlights include: • Integrating data sources • Valuing and creating a variable annuity product • Application development and deployment This webinar is relevant to practitioners or academics in finance whose focus is quantitative analysis, modeling, risk analysis, and valuation—particularly but not exclusively actuaries and professionals in the insurance industry.

The following Matlab project contains the source code and Matlab examples used for non parametric regression using kernels to estimate density function of residuals. . It will first to a simple regression using least squares to get a good start value.

The following Matlab project contains the source code and Matlab examples used for ranged major axis regression. . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

The following Matlab project contains the source code and Matlab examples used for major axis regression (principal axis regression). . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

The following Matlab project contains the source code and Matlab examples used for fit t copula fast using method of moments. Fitting a t-copula using the method of moments described in Quantitative Risk Management by McNeil, Frey and Embrechts.

The following Matlab project contains the source code and Matlab examples used for estimation value at risk by using conditional copula garch. the copula111cGarch111VaR function estimate VaR (Value at Risk) of portfolio composed of two stocks return and extract number of violation of VaR The method of estimation is conditional copula- GARCH model.

The following Matlab project contains the source code and Matlab examples used for copula marginal algorithm (cma). To walk through the code and for a thorough description, refer to A. Meucci, "A New Breed of Copulas for Risk and Portfolio Managemen", Risk (September 2011). Latest version of article and code available at http://symmys.com/node/335

The following Matlab project contains the source code and Matlab examples used for linear deming regression. [ b sigma2_x x_est y_est stats] = deming(x,y,lambda,alpha) deming() performs a linear Deming regression to find the linear coefficients:                     y = b(1) + b(2)*x under the assumptions that x and y *both* contain measurement error with measurement error variance related as lambda = sigma2_y/sigma2_x (sigma2_x and sigma2_y is the measurement error variance of the x and y variables, respectively).

The following Matlab project contains the source code and Matlab examples used for pls regression or discriminant analysis, with leave one out cross validation and prediction.. Leave-one-out cross-validation for PLS regression or discriminant analysis pls_cv = plscv(x,y,vl,'da') input: x (samples x descriptors) for cross-validation y (samples x variables) for regression or   (samples x classes) for discriminant analysis.

The following Matlab project contains the source code and Matlab examples used for plot the kaplan-meier estimation of the survival function . Survival times are data that measure follow-up time from a defined starting point to the occurrence of a given event, for example the time from the beginning to the end of a remission period or the time from the diagnosis of a disease to death.

The following Matlab project contains the source code and Matlab examples used for regression outliers. This function accepts two (vector of) variables for which a bivariate linear regression analysis is meant to be performed, and removes the outliers from both variables.

The following Matlab project contains the source code and Matlab examples used for kmv credit risk model probability of default default risk. KMV-Merton model Probability of Default represented by Jin-Chuan Duan, Genevi`eve Gauthier and Jean-Guy Simonato (2005).

The following Matlab project contains the source code and Matlab examples used for robust multivariate regression using the student-t distribution . The function mvsregress performs regression on multivariate data using the Student-t distribution. Its usage syntax is similar to that of the Statistics Toolbox function mvregress that does regression with the normal distribution. The contribution includes a user manual.

The following Matlab project contains the source code and Matlab examples used for student var cvar. It is part of chapter 1 VaR and CVaR illustration

The following Matlab project contains the source code and Matlab examples used for estimation value at risk by using conditional copula garch. Estimating VaR of portfoilio by using Conditional copula GARCH(1,1) model.

The following Matlab project contains the source code and Matlab examples used for analyzing investment strategies with cvar portfolio optimization. A .

The following Matlab project contains the source code and Matlab examples used for negative binomial regression. Performs Negative-Binomial regression. Regression coefficients are updated using IRLS, and the dispersion parameter is estimated via Chi^2 dampening. See Hardin, J.W. and Hilbe, J.M. Generalized Linear Models and Extensions. 3rd Ed. p. 251-254. for more information.

The following Matlab project contains the source code and Matlab examples used for function for multivariate robust linear regression with missing data. . Function for multivariate robust linear regression with missing data.

The following Matlab project contains the source code and Matlab examples used for geometric mean regression (reduced major axis regression). . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

The following Matlab project contains the source code and Matlab examples used for interval prediction of a single value for a geometric mean regression-reduced major axis regression. . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

The following Matlab project contains the source code and Matlab examples used for markov copula code. Log-likelihood functions for Markov Switching Copula model presented in "Modelling Dependence Dynamics through Copulas with Regime Switching" with Flávio A. Ziegelmann and Michael J. Dueker, Insurance: Mathematics and Economics, Volume 50, Issue 3, May 2012, Pages 346-356.

The following Matlab project contains the source code and Matlab examples used for find the p value and coefficients for linear regression . Input is x and y output is regression coefficients, degrees og freedom and p-value for slope

The following Matlab project contains the source code and Matlab examples used for copula generation and estimation. Functions written in 2007 for Master Thesis: "Simulating dependent random variables using copulas.

The following Matlab project contains the source code and Matlab examples used for support vector regression. Support Vector Regression is a powerful function approximation technique based on statistical learning theory.

The following Matlab project contains the source code and Matlab examples used for regression with gradient descent. Regression with Gradient Descent; A coefficient finding technique for the desired system model I included different functions to model the data using descent gradient technique performed Linear Regression of randomly generated data In Arbitary.