- Is there a benefit of having lower gap between 'in-sample' variance of portfolio daily returns and 'out-of-sample' variance of portfolio daily returns? (= better estimates the out-of-sample variance)
Question in more detail
I have developed a way of optimizing a portfolio, based on Global Minimum Variance portfolio optimization.
There are upside and downside of my portfolio optimization method.
cons : It cannot lower the `out-of-sample variance' of portfolio daily returns than the GMV portfolio. In other words, my portfolio optimization method fails to achieve better portfolio performance such as Sharpe ratio.
pros : However, my portfolio optimization method has lower gap between 'in-sample variance' and 'out-of-sample' variance than the GMV portfolio.
For illustration, let me give you an example. During training period to come up with how much weight to put on each stock, GMV portfolio optimization calculates the stock weight with variance 100 of daily returns. However, during the investment period (test period), it gives me 125 for 'out-of-sample' variance.
My portfolio method gives me 150 variance for 'in-sample variance' and 130 for 'out-of-sample' variance. As you can see, the actual variance is still low with the result of GMV portfolio optimization method. However, my method expects the 'out-of-sample' variance better than GMV method. GMV method is wrong by 25 percent, while my method is wrong by 13 percent.
As such, I am curious to know if my portfolio optimization method would be useful in any case of trading in stock market nowadays.