Is it always better to use the entire distribution of a financial returns series, not just $mu$ and $sigma$?

Question

In finance models that use historical returns for inputs, including option pricing models, forecasting and portfolio optimization, only the statistical moments of the returns distribution, $mu$ and $sigma$ (expected value, or mean, and standard deviation), are used as inputs because the moments summarize a return series' probability distribution (pdf). How strong is the argument that the user would be better off, and would get more accurate results, in using the data's entire pdf, instead of only $mu$ and $sigma$?
And would using the entire pdf also be better than models that try to extend to the third and fourth moments (skewness, kurtosis)? given that you could even create a distribution of the rolling skewness and rolling kurtosis of a return series, i.e. the distribution of each moment

Bob Jansen · Answer

It depends.
For example, if you're doing option pricing in the log normal world returns are completely described by the mean and standard deviation. If you add jumps, you would also need to parametrize the underlying Poisson process which is fully described by one parameter and the jump size. In other words, if you have a (log)normal distribution and the mean and standard deviation you're using the complete distribution. Of course, there is no need to keep the parameters fixed over the whole time horizon. This is what local volatility and stochastic volatility models are all about.
If you're doing portfolio optimization taking into account more than mean and variance, you should definitely use higher moments and do so almost by definition.
For risk management taking other moments into account is common place. In the Gaussian world, the number of extreme events is heavily underestimated after all. Performing scenario analysis can be seen as specifying the return distribution and you're not really considering the statistical mean or standard deviation in any case.
If you want to use the empirical distribution of past returns, you can use filtered historical simulation.

Is it always better to use the entire distribution of a financial returns series, not just $mu$ and $sigma$?

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