This paper investigates adaptive hedging strategies for managing agricultural price risk in Karnataka, India. A Monte Carlo simulation framework is developed to model the relationship between input costs and output prices, combining stochastic volatility models with Hamilton filter-based regime detection to create an adaptive hedging strategy. The study shows that both vanilla delta-hedge and adaptive regime-hedge strategies achieve hedge effectiveness exceeding 99.6% in regime-switching environments.
Key findings
Both hedging strategies achieve 99.7% hedge effectiveness in regime-switching environments.
Adaptive approach reduces Value at Risk (95%) from -48.02 (unhedged) to -4.87, representing a 90% improvement in tail risk.
Stress regime occurs approximately 14.3% of the time on average.
Limitations & open questions
Synthetic price paths used instead of actual Karnataka price data.
Two-state model simplifies complex market dynamics.