Author(s) :

Aarit Das.

Volume/Issue :

Abstract :

Prior literature on the low-volatility anomaly suggests that portfolios composed of lower-volatility assets often achieve superior risk-adjusted returns compared to their higher-volatility counterparts over long investment horizons. This phenomenon challenges the traditional risk-return trade-off implied by classical asset pricing models, which associate higher risk with higher expected returns. As a result, lowvolatility strategies have gained attention for their ability to deliver more efficient return profiles with reduced downside risk. This study investigates this claim by comparing alternative portfolio allocation strategies in optimizing the risk-return tradeoff over the 2021-2025 period. Using volatility forecasts generated through a GARCH (1,1) model and evaluating two allocation frameworks - volatility targeting and Sharpe ratio-constrained optimization, we examine performance across varying market conditions. The findings indicate that Sharpe ratio-constrained optimization produces higher returns during strong market recoveries; however, it is vulnerable to significant drawdowns and elevated tail risk during market downturns. In contrast, the volatility-targeting strategy demonstrates greater stability, lower maximum drawdowns, and more consistent risk-adjusted performance in adverse market environments. Overall, the results suggest that dynamic rebalancing and active risk management are critical determinants of long-term portfolio performance. High-volatility assets, such as Bitcoin, may enhance returns, but only when their exposure is carefully managed within a diversified portfolio framework.