When to Hedge Downside Risk?

Principal Investigator:
Andreas Kakolyris

Co-PIs:
Tin Shan Suen, Hany Guirguis

Abstract:
Hedging downside risk before substantial price corrections is vital for risk management and long-only active equity manager performance. This study proposes a novel methodology for crafting timing signals to hedge sectors’ downside risk. These signals can be integrated into existing strategies simply by purchasing sector index put options. Our methodology generates successful signals for price corrections in 2000 (dot-com bubble) and 2008 (global financial crisis). A key innovation involves utilizing sector correlations. Major price swings within six months are signaled when a sector exhibits high valuation alongside abnormal correlations with others. Our signals are also more efficient than those of standard technical analyses.

Description of Research:
Our proposed methodology provides timely alerts for the two most significant market crashes in recent market history. Many studies emphasize the importance of the early forecasting of a financial bubble for investors. One way to take advantage of an early warning is to buy sector index put options when the signal is observed. The signal can overlay easily with a portfolio manager’s existing strategy. Therefore, an equity portfolio manager can use it to hedge potential downside risk due to a portfolio’s exposure to a certain sector. In addition to these crashes, our timing signal has also been effective in identifying notable price corrections across major S&P 500 sectors over the past few decades.
We evaluate our signals using bootstrapped pseudo p-values and finally compare our results with signals derived from commonly used technical analyses among practitioners, demonstrating their efficiency. Previous research on predicting market crashes using a single-variable approach suggests that the bond–stock earnings yield differential (BSEYD) model may offer better predictions for equity market corrections compared to price-earnings ratio (PE) models. However, the results of our generalized methodology challenge these findings. In the future, our suggested approach could be extended by utilizing more valuation metrics.