Statistical analysis with MATLAB
Statistical analysis with MATLAB
Statistical analysis with MATLAB
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<strong>Statistical</strong> <strong>analysis</strong> <strong>with</strong> <strong>MATLAB</strong>A substantial amount of quantitative finance, in particular MFEcoursework, is statistical <strong>analysis</strong> of financial data.
Regression and hypothesis testsThe statistics toolbox has all manner of functionality related toregression. The most straightforward command, which will fit thefamiliar modelY = X β + εis regress. The regstats command is a bit less simple, but cancalculate many more important metrics.The chi2gof (“chi squared goodness of fit”), ttest, ttest2, andztest commands implement hypothesis tests that should befamiliar from introductory statistics.
Time series modelsThe System Identification Toolbox, offers simple functionalityfor fitting autoregressive moving average (ARMA) models.The Econometrics toolbox also has functionality for fittingGARCH (“generalized autoregressive conditionalheteroskedasticity”) models.Error correction models seem not to be natively implementedin <strong>MATLAB</strong>. Some high quality routines seem to beimplemented in the fairly well-regarded library athttp://www.spatial-econometrics.com/.
Descriptive statisticsBesides cov offered in basic <strong>MATLAB</strong>, the statistics toolboxofferes many functions to calculate sample moments:correlation <strong>with</strong> corr, kurtosis, skewness, and moregenerally, momentThe geometric and harmonic mean are implemented ingeomean and harmmean. The trimmed mean is trimmean.The quantile function calculates the quantiles of data
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