Dynamically hedging systematic financial risks embedded in life insurance retirement products like Variable Annuities (VA) requires intraday rebalancing of derivative asset positions. The complex structure of VA products and their embedded long-dated options pose significant risk management challenges. Generally, changes in liability values due to equity and interest rate movements are compensated by holding an asset portfolio with matching and opposite sensitivity due to the same equity and interest rate movements. The sensitivity of the liability and asset portfolio changes with the passage of time and market movements themselves, so asset positions need to be rebalanced periodically, or a sensitivity mismatch will occur. Simplistic assumptions made about the volatility of equity and interest rate movements do not fully capture the actual market dynamics that drive changes in liability values. In addition, there are no hard set rules about how and when to rebalance positions in the presence of explicit and implicit transaction costs, adding even more challenges for dynamically hedging programs.

This paper explores the use of reinforcement learning techniques to improve the effectiveness of dynamic hedging programs of short dated exchange traded put options, as well as long dated VA products. We apply machine learning techniques to develop strategies that are able to react to market movements, transaction costs and volatility misspecification, versus some standard industry approaches used to hedge these such risks. Our numerical results using reinforcement learning over an 18-month period on live market data show that compared to traditional hedging strategies, reinforcement learning approaches produced higher P&L and lower transaction costs.

Find the Q&A here: Q&A on 'Annuities from Several Perspectives'