When Strategies Work Too Well: Volatility-Based Investing, Overfitting and the Limits of Empirical Finance

Abstract

This study examines the performance and robustness of an index volatility portfolio allocation strategy applied to the S&P 500, DAX, and Nikkei 225 indices for the period 1996-2025. The volatility portfolio allocation strategy is based on a dynamic market allocation that uses discrete volatility regimes estimated according to historical data, so that the portfolio enters or exits the market entirely depending on the level of observed volatility. Empirical results show that volatility portfolio allocation rules can achieve substantial improvements in cumulative returns and risk-adjusted performance compared to a passive Buy-and-Hold strategy in various financial markets. The strategy often achieves superior performance despite being invested for a smaller percentage of time. However, subsequent analysis reveals significant methodological limitations that affect the interpretation of the results. The strategy performance is highly sensitive to parameter selection, exhibiting instability between subperiods and a high dependence on model specification. The study shows the existence of various methodological problems, such as look-ahead bias in the contemporaneous (t-0) specification, extensive in-sample parameter optimization, and the absence of out-of-sample validation. All of this creates concerns related to data mining, backtest overfitting, and the economic robustness of the strategy. Overall, the results suggest that the improvements observed in performance do not constitute strong evidence of a stable relationship between volatility and future returns. The results highlight the importance of rigorous methodology and the evaluation of robustness in empirical financial research. The study contributes to the literature by exposing the risks involved in the selection of data-driven models.