Volatility Risk Metrics
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)) |
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