Portfolio optimisation algorithm using implied volatility based on users' risk appetite

By: Contributor(s): Material type: Computer fileComputer filePublication details: Ahmedabad Indian Institute of Management 2023Description: 17 p. : illSubject(s): DDC classification:
  • SP2023/3696
Summary: In the evolving landscape of financial management, portfolio optimization remains a cornerstone for achieving desirable investment outcomes. Traditional approaches, largely rooted in Modern Portfolio Theory (MPT), emphasize diversification and the trade-off between risk and return. However, with the advent of more dynamic and forward-looking market indicators, there is growing interest in integrating Implied Volatility (IV) into optimization frameworks to better anticipate market movements and align portfolios with future conditions. This project presents a customized portfolio optimization model that accounts for individual financial goals, risk tolerance, and prevailing market conditions, with the primary objective of maximizing the Sharpe Ratio—a measure that effectively balances returns against risk. By leveraging the concept of the efficient frontier, the model identifies optimal portfolios that deliver the highest expected return for a given level of risk. What sets this study apart is the incorporation of implied volatility—derived from option pricing models—as a proxy for market expectations, complementing traditional measures based on historical volatility (HV). Utilizing a sample of the top 20 NSE-listed stocks with liquid options, the model compares the performance of portfolios built using both HV and IV. Near-month options were specifically selected to ensure better liquidity and real-time responsiveness. Using Python-based simulations, a large number of random portfolios were generated and analyzed. The comparative analysis reveals that IV-based portfolios slightly outperform HV-based portfolios in 46% of the test cases over a one-month holding period. This marginal but consistent outperformance is attributed to market stability, efficient asset pricing, and the predictive accuracy of option-derived volatilities. The study concludes that incorporating implied volatility into portfolio design enhances responsiveness to future market trends and improves the alignment of asset allocation with investor expectations, thereby offering a valuable augmentation to traditional portfolio optimization techniques.
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Student Project Vikram Sarabhai Library Reference Students Project SP2023/3696 (Browse shelf(Opens below)) e-Book - Digital Access SP003696

Submitted to: Prof. Anirban Banerjee

Submitted by: Priyanshu Raj, Aditya Katara

In the evolving landscape of financial management, portfolio optimization remains a cornerstone for achieving desirable investment outcomes. Traditional approaches, largely rooted in Modern Portfolio Theory (MPT), emphasize diversification and the trade-off between risk and return. However, with the advent of more dynamic and forward-looking market indicators, there is growing interest in integrating Implied Volatility (IV) into optimization frameworks to better anticipate market movements and align portfolios with future conditions.

This project presents a customized portfolio optimization model that accounts for individual financial goals, risk tolerance, and prevailing market conditions, with the primary objective of maximizing the Sharpe Ratio—a measure that effectively balances returns against risk. By leveraging the concept of the efficient frontier, the model identifies optimal portfolios that deliver the highest expected return for a given level of risk.

What sets this study apart is the incorporation of implied volatility—derived from option pricing models—as a proxy for market expectations, complementing traditional measures based on historical volatility (HV). Utilizing a sample of the top 20 NSE-listed stocks with liquid options, the model compares the performance of portfolios built using both HV and IV. Near-month options were specifically selected to ensure better liquidity and real-time responsiveness.

Using Python-based simulations, a large number of random portfolios were generated and analyzed. The comparative analysis reveals that IV-based portfolios slightly outperform HV-based portfolios in 46% of the test cases over a one-month holding period. This marginal but consistent outperformance is attributed to market stability, efficient asset pricing, and the predictive accuracy of option-derived volatilities.

The study concludes that incorporating implied volatility into portfolio design enhances responsiveness to future market trends and improves the alignment of asset allocation with investor expectations, thereby offering a valuable augmentation to traditional portfolio optimization techniques.

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