Newsletter: Volatility Targeting Across Asset Pricing Factors and Industry Portfolios

A Study of Position Sizing and Dynamic Risk Management

Position sizing is an important aspect of portfolio management, as it directly influences both risk and return. While investors can choose from a number of position sizing techniques, one approach that has gained traction is volatility targeting. In this newsletter, I explore how volatility targeting can be applied to manage portfolio exposure and improve risk-adjusted returns.

Web-only posts Recap

Below is a summary of the web-only posts I published during last week.

Lead-Lag Relationships Between Industries of Developed Countries

An Option Pricing Model Based on Market Factors

Comparative Analysis of Gold Forecasting Models: Statistical vs. Machine Learning Approaches

Gold and Low-Volatility Stocks as Diversifiers

Validity of Research in Asset Management

Volatility, Correlations, and Causal Links in Cryptocurrency Markets

Volatility Timing in Portfolio Management

Volatility of an asset is the measure of how much its price changes over time. The higher the volatility, the greater the price swings. Volatility is important because it can have a big impact on the value of your investments. For example, if you’re holding an asset that has high volatility, the value of your investment will be more volatile as well.

Reference [1] proposed a volatility timing technique to manage an investment portfolio.

Findings

-The study shows that volatility-managed portfolios generate large alphas, higher factor Sharpe ratios, and significant utility gains for mean-variance investors.

-Evidence is provided across equity factors (market, value, momentum, profitability, return on equity, and investment) as well as the currency carry trade.

-Volatility timing enhances Sharpe ratios because factor volatilities change more than expected returns, creating inefficiencies to exploit.

-The strategy runs contrary to conventional wisdom: it reduces risk in recessions and crises but still delivers high average returns.

-These findings challenge traditional risk-based explanations and structural models of time-varying expected returns.

-Volatility-managed portfolios are straightforward to implement in real time and provide consistently high risk-adjusted returns.

-Because volatility does not strongly predict future returns, reducing exposure when volatility is high and increasing it when volatility is low improves performance.

-Utility gains from volatility timing for mean-variance investors are estimated at around 65%, which far exceeds the gains from timing expected returns.

-The strategy also sheds light on the dynamics of effective risk aversion, which is central to theories of time-varying risk premia.

In short, the authors advocated lowering risk exposure when volatility is high and increasing risk exposure when volatility is low. The technique relies on the idea that volatility is autocorrelated but only weakly correlated with future returns. It has been widely adopted by industry practitioners.

Reference

[1] Moreira, Alan and Muir, Tyler, Volatility-Managed Portfolios, Journal of Finance, 72(4), 1611–1644

Applying Volatility Management Across Industries

Based on the previous paper, Reference [2] continues this line of research by applying volatility-managed techniques to U.S. industry portfolios. It uses four measures of volatility: one-month realized variance, one-month realized volatility, six-month exponentially weighted moving average (EWMA) of realized volatility, and GARCH-forecasted one-month volatility.

Findings

-Four volatility-management techniques are tested: one-month realized variance, one-month realized volatility, six-month EWMA volatility, and GARCH-forecasted one-month volatility.

-Volatility-managed portfolios show statistically and economically significant improvements in Sharpe and Sortino ratios compared to unmanaged portfolios.

-The EWMA-based strategy is the most robust after accounting for transaction costs and leverage constraints.

-Technology, telecom, and utilities benefit the most, with Sharpe ratio improvements of 27.6%, 30.5%, and 25.5%, respectively.

-Results show that volatility management is practical and enhances investor welfare for both mean-variance and benchmark-aware investors.

-The technology sector emerges as the most favorable for implementing volatility-management strategies due to consistent performance gains.

-Strategy effectiveness varies across subperiods, with negative skewness and kurtosis disrupting traditional volatility patterns.

-Statistical significance weakens during recessionary periods, suggesting caution when applying strategies in stressed market environments.

In short, the article concluded that,

-Volatility management using a six-month EWMA volatility measure is the most consistent,

-The strategy improves Sharpe ratios in the technology, telecom, and utilities sectors, though not all sectors benefit equally. Technology performs best due to the persistence of its volatility,

-The statistical significance of volatility-managed strategies weakens when tested over selected subperiods and recessionary periods.

Reference

[2] Ryan Enney, Sector-Specific Volatility Management: Evidence from U.S. Equity Industry Portfolios, Claremont McKenna College, 2025

Closing Thoughts

These two studies highlight the effectiveness of volatility management across both factor-based and industry-specific portfolios. Evidence shows that scaling risk exposure inversely with volatility can significantly enhance Sharpe ratios, utility gains, and investor welfare. While factor-level strategies demonstrate robustness across market regimes, sector-level analysis points to particularly strong improvements in technology, telecom, and utilities. Collectively, the findings confirm that volatility management is not only theoretically sound but also practically implementable, offering investors a disciplined framework to improve risk-adjusted returns across diverse applications.

Educational Video

New Developments in Long-Term Asset Management by Alan Moreira

In this video, Alan Moreira outlines a straightforward approach to portfolio management: scale exposure to asset pricing factors inversely with realized volatility. The method takes more risk when volatility is low and reduces risk when volatility is high. Despite its simplicity, the strategy consistently produces higher Sharpe ratios, large positive alphas, and meaningful utility gains for mean-variance investors. Importantly, the approach survives transaction costs, applies across multiple factors and geographies, and does not rely on parameter estimation.

The results run counter to conventional wisdom. Volatility-managed portfolios cut risk in recessions and crises—periods usually associated with high expected returns—yet still deliver superior outcomes. Because volatility does not strongly predict returns, reducing exposure in turbulent times avoids unnecessary risk without sacrificing performance. The strategy is easy to implement, challenges standard risk–return models, and provides a practical tool for both institutional and academic audiences.

Around the Quantosphere

-A Macro Hedge Fund Plays Index Versus Index in Niche Option Trades (tradealgo)

-Hedge Fund Short Positions on VIX at Three-Year High, But It's Not Necessarily a Bet Against Volatility (marketwatch)

-Memes Can Be Good for Business (bloomberg)

-Quant Investment Management Firm Charged with Securities and Wire Fraud (justice)

-Regulatory Scrutiny Intensifies in Quant Trading: Sigma Setback, Broader Implications for Hedge Fund Resilience (ainvest)

-Hedge Funds Hunt Commodity Volatility Traders Amid Global Market Swings (bloomberg)

-Goldman Sachs Interview Questions (efinancialcareers)

-How to Get a Job in Asset Management (efinancialcareers)

- Hedge funds extend winning streak in August (opalesque)

- This $2 Billion Hedge Fund Led By a Former OpenAI Researcher Is Betting Against All Semiconductor Stocks Except These 2 Industry Giants (yahoo)

Recent Newsletters

Below is a summary of the weekly newsletters I sent out recently

-Tail Risk Hedging Using Option Signals and Bond ETFs (12 min)

-Stochastic Volatility Models for Capturing ETF Dynamics and Option Term Structures (11 min)

-Cross-Sectional Momentum: Results from Commodities and Equities (11 min)

-Predictive Information of Options Volume in Equity Markets (11 min)

-The Impact of Market Regimes on Stop Loss Performance (12 min)

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