The portfolio standard deviation or variance, which is simply the square of the standard deviation, comprises two key parts: the variance of the underlying assets plus the covariance of each underlying asset pair. Due to the correlation between securities, the computation of portfolio risk must incorporate this correlation relationship. This approach ignores autocorrelation in x.The standard deviation of a portfolio of assets, or portfolio risk, is not simply the sum of the risk of the underlying securities.A softmax function might make ewm_weights safer.While a bootstrapped b with alpha=0.03 looks like: b = bootstrap(x, alpha=0.03, size=int(1e6))Īny continuous distribution from scipy.stats._continuous_distns._distn_names can then be fit to b. The histogram of x looks like this: plt.hist(x, bins=25) Example: # Create a nonstationary `x` variable with larger mean and stdev in period 2 Return np.random.choice(a=a, size=size, p=p) fit method of any distribution can then be called on that bootstrapped data. The weights are the p parameter to np.random.choice. Use these weights to bootstrap sample a new distribution.Given an empirical distribution x, assign exponential weights to each x.How could I extend this process to distributions that have parameters besides loc and scale?Ĭombined snippets from above: import scipy.stats as scs johnsonsu has parameters a, b, loc, scale.Print((q=0.05, loc=loc, scale=scale))īut I would like to be able to extend this to the broader family of continuous distributions where other parameters ( shape) are often involved. Standard normal distribution the location is the mean and the scale is To adjust the location and scale of the distribution, e.g.
ppf (percent point function), while incorporating exponential weights into the building of the distribution.įor example, with a normal distribution, this would just entail estimating an exponentially-weighted mean and standard deviation:Īll continuous distributions take loc and scale as keyword parameters
I would like to be able to establish a new distribution, and then call it's. Index=pd.date_range('2013', periods=1000))Īnd the distributions within scipy.stats._continuous_distns._distn_names: import scipy.stats as scsĭists = scs._continuous_distns._distn_names Make a norm object by passing the computed mean and standard deviation to norm(). Compute the mean and standard deviation of the result. Extract realinc from gss and compute its logarithm using np.log10(). R = pd.Series(np.random.beta(a=0.5, b=0.5, size=1000), Then, you’ll use the computed mean and standard deviation to make a normobject using the () function.
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