When you generate a set of correlated random number and then calculate the correlation of those samples, you’ll notice that the calculated “sample correlation” can deviate from the correlation value use to generate the samples. This is because of the randomness of the samples, and the limited number of samples you use
Category Archives: Technical
An Internally Consistent Interpolation Method for Yield Curves
Here we present a new yield curve interpolation method, one that’s based on conditioning a stochastic model on a set of market yields. The concept is closely related to a Brownian bridge where you generate scenario according to an SDE, but with the extra condition that the start and end of the scenario’s must have [...]
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Calculating Correlation and Means with Missing Data
A great deal has been written about fixing ‘invalid correlation matrices’ for risk management purposes. Correlation matrices are invalid when it’s mathematically impossible to generate random numbers with those mutual correlations. The most common cause of this problem -as seen in finance- is that the correlation matrices numbers are made up, or are based on [...]
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Calibrating the Ornstein-Uhlenbeck (Vasicek) model
Two methods for calibrating the Ornstein Uhlenbeck process on historical data are given. The least square fit, and the maximum likelihood estimates. Matlab source code for methods in Matlab is given.
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