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StuartGordonReid/Volatility.py

Created June 12, 2015 11:33
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Volatility Risk Metrics
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 import numpy import numpy.random as nrand def vol(returns): # Return the standard deviation of returns return numpy.std(returns) def beta(returns, market): # Create a matrix of [returns, market] m = numpy.matrix([returns, market]) # Return the covariance of m divided by the standard deviation of the market returns return numpy.cov(m)[0][1] / numpy.std(market) # Example usage r = nrand.uniform(-1, 1, 50) m = nrand.uniform(-1, 1, 50) print("vol =", vol(r)) print("beta =", beta(r, m))

WilliamJosephKessler commented Dec 16, 2019

Hi Stuart,

In line 14: `return numpy.cov(m)[0][1] / numpy.std(market)` you should replace the `.std` with `.var`. The division in Beta is done with the variance of the market and not standard deviation. Thus, `return numpy.cov(m)[0][1] / numpy.var(market)`

In the current form, Beta is significantly affecting the results of the Treynor ratio.

PS. It can also be easily tested in Excel e.g. `=COVARIANCE.P(range_of_the_fund,range_of_the_market)/VAR.P(range_of_the_market)`

Hope it helps. Thanks
William Klubinski

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