hedging-strategy

Calculate hedge ratios for an equity portfolio using OLS regression, minimum variance, or EWMA methods to determine the optimal quantity of futures or options needed to offset beta exposure.

Minimum variance hedging is a statistical method to optimize hedge ratios by minimizing portfolio variance. Use it for risk management when mitigating drawdowns without sacrificing long-term alpha is the primary objective.

Yes, you can use numpy and scipy to estimate beta exposure for cross-asset hedging. These dependencies support the statistical beta estimation and variance-based hedge ratio optimization required.

Design a protective put hedge by calculating the optimal number of put options required based on your portfolio's specific beta exposure, while simultaneously evaluating the associated hedging costs.

Linear hedging with futures manages directional beta exposure, while non-linear options are better suited for tail-risk scenarios. Evaluate both instruments to mitigate drawdowns across various market conditions.

EWMA hedge ratio calculation relies on historical volatility weighting and may lag during sudden regime shifts. Combine it with OLS regression or minimum variance methods to validate cross-asset hedging effectiveness.