Dynamic Hedging and Portfolio Risk Management Using Regime Switching and CVaR-Based Optimization

Abstract

The paper proposes Dynamic Hedging and Portfolio Risk Management to stabilize portfolios and decrease downside risk. We use regime identification, derivative-based hedging, and adaptive portfolio allocation. It uses tail-risk indicators including Value at Risk and Conditional Value at Risk, dynamic portfolio optimization, market state modeling, and delta-gamma adaptive hedging. Regime changes may cause market volatility, whereas stochastic processes cause asset price changes. Monte Carlo simulations using Geometric Brownian Motion-generated synthetic financial data test the proposed method. Experimental findings show considerable risk-adjusted performance gains. Hedging reduced volatility by 29%, increased Sharpe ratio from 0.46 to 0.59, and reduced VaR (95%) from -12.4% to -8.1%. Reduce tail-risk exposure (CVaR 95%) from -18.7% to -11.5% to cut catastrophic losses by 38%. The recommended dynamic hedging method reduces maximum loss from −28.3% to −16.4%, substantially improving market resilience. The results suggest regime-based optimization and derivative hedging may reduce portfolio risk in dynamic financial markets.