Garrett M. writes:
I had two (hopefully straightforward) questions related to time series analysis that I was hoping I could get your thoughts on:
First, much of the work I do involves “backtesting” investment strategies, where I simulate the performance of an investment portfolio using historical data on returns. The primary summary statistics I generate from this sort of analysis are mean return (both arithmetic and geometric) and standard deviation (called “volatility” in my industry). Basically the idea is to select strategies that are likely to generate high returns given the amount of volatility they experience.
However, historical market data are very noisy, with stock portfolios generating an average monthly return of around 0.8% with a monthly standard deviation of around 4%. Even samples containing 300 months of data then have standard errors of about 0.2% (4%/sqrt(300)).
My first question is, suppose I have two time series. One has a mean return of 0.8% and the second has a mean return of 1.1%, both with a standard error of 0.4%. Assuming the future will look like the past, is it reasonable to expect the second series to have a higher future mean than the first out of sample, given that it has a mean 0.3% greater in the sample? The answer might be obvious to you, but I commonly see researchers make this sort of determination, when it appears to me that the data are too noisy to draw any sort of conclusion between series with means within at least two standard errors of each other (ignoring for now any additional issues with multiple comparisons).
My second question involves forecasting standard deviation. There are many models and products used by traders to determine the future volatility of a portfolio. The way I have tested these products has been to record the percentage of the time future returns (so out of sample) fall within one, two, or three standard deviations, as forecasted by the model. If future returns fall within those buckets around 68%/95%/99% of the time, I conclude that the model adequately predicts future volatility. Does this method make sense?
Regarding your first question about the two time series, I’d recommend doing a multilevel model. I bet you have more than two of these series. Model a whole bunch at once, and then estimate the levels and trends of each series. Move away from a deterministic rule of which series will be higher, and just create forecasts that acknowledge uncertainty.
Regarding your second question about standard deviation, your method might work but it also discards some information. For example, the number of cases greater than 3sd must be so low that your estimate of these tails will be noisy, so you have to be careful that you’re not in the position of those climatologists who are surprised when so-called hundred-year floods happen every 10 years. At a deeper level, it’s not clear to me that you should want to be looking at sd; perhaps there are summaries that map more closely to decisions of interest.
But I say these things all pretty generically as I don’t know anything about stock trading (except that I lost something like 40% of my life savings back in 2008, and that was a good thing for me).