Consider a set of alternative forecasts. The literature on forecast combinations* shows that an average forecast often tends to beat the estimated (ex ante) best forecast from the set. This is because of two reasons. First, different forecasts make different mistakes, and averaging across forecasts tends to make them less influential. The analogy in finance is portfolio diversification. A well diversified portfolio will have lower risk than the average risk of its constituents. Second, identifying the ex ante best forecast is difficult due to issues with estimation precision and with the changing nature of the data generating process. Perhaps stock A gave you great returns for the last 5 years, but there is no guarantee it will continue doing that the next year.
The literature on forecast combinations further tells us that if we are looking for an optimal combination, a simple average is hard to beat. This was known as the "forecast combination puzzle", but it is not a puzzle anymore. A simple average is hard to beat not because better combinations do not exist but because it is difficult to estimate them with good precision. The portfolio analogy is, an equally-weighted portfolio may be your best bet unless you can estimate the expected returns and (especially) the covariance matrix precisely enough to build one that is truly superior. Try that with a time horizon of 100 periods and 1000 stocks to choose from... (The "forecast combination puzzle" has also been studied in the context of volatility forecasting; see Clements & Vasnev (2021).)
Now the caveats. First, some future shocks will be missed by all forecasts. This is the systematic risk of your portfolio; you cannot diversify it away. This is not a drawback of the forecast combination approach, though, as it applies to each and every candidate forecast. Second, if one** of the candidate forecasts is vastly superior than the rest, combining forecasts may be detrimental. (In terms of portfolios, there is probably no alternative in a well functioning market. In an inefficient market, you have a single stock that has an alpha way above any other alphas, so you want to invest in it alone without building a portfolio.) Or if one** of the candidate forecasts is vastly inferior, you do not want it in the combination. The individual performance can be estimated using rolling or expanding windows the way @phdstudent suggests in their answer. The vastly inferior forecast(s) may well be kicked out of the forecast combination, provided that estimation precision is sufficient.
*Summarized in textbooks such as Diebold (2017) and Claeskens & Hjort (2012).
**Or a few.
- Claeskens, G., & Hjort, N. L. (2008). Model selection and model averaging. Cambridge Books.
- Clements, A., & Vasnev, A. L. (2021). Forecast combination puzzle in the HAR model. Available at SSRN 3875026.
- Diebold, F. X. (2017). Forecasting in Economics, Business, Finance and Beyond. Department of Economics, University of Pennsylvania.