An ETF-Based Measure of Stock Price Fragility
Renato Lazo-Paz (University of Ottawa)
A growing literature employs equity mutual fund flows to measure a stock’s exposure to non-fundamental demand risk – stock price fragility. However, this approach may be biased by confounding fundamental information, potentially leading to underestimating risk exposure. We propose an alternative estimation procedure incorporating readily available primary market data from exchange-traded funds (ETFs). Our proposed procedure significantly enhances the predictive power of fragility in forecasting stock return volatility. Moreover, we find that our measure captures the influence of increased ETF activeness while partially capturing the effect of institutional investors’ demand on price return volatility. Additionally, our analysis reveals a decrease in the explanatory power of mutual fund-based fragility. These results highlight the advantages of employing an ETF-based fragility measure that takes into account recent developments in the asset management industry, particularly the rise of passive investing.
Geopolitical Threat, Market Capitalization, and Portfolio Return
Syed Riaz Mahmood Ali
University of Turku
In this paper, we validate that in the US equity market, the large and prime cap portfolios can generate significantly positive returns against geopolitical threats, whereas other medium and small cap portfolios fail to exhibit such results. The results of our investigation are equally supported by the Markov regime-switching model where we find that portfolio returns perform better against geopolitical threats during high-volatility regimes. Additionally, we demonstrate that geopolitical threat has a significant impact on the conditional volatility of large and prime cap portfolios. However, the monthly impact and lag effect of geopolitical threat is not visible in our results indicating that investors adjust their portfolios instantly against geopolitical threat. Our findings are robust in the presence of various alternative measures of market uncertainties, for example, economic policy uncertainty, economic uncertainty, VIX, etc. We also conduct a series of out-of-sample regressions to confirm our results. Finally, we report a few trading strategies using geopolitical threats.
Milos Bozovic (University of Belgrade)
We propose a simple portfolio management strategy that gauges the leverage based on the observed implied volatility index (VIX). The strategy involves taking less risk when the cumulative previous-month VIX is high and more when it is low. We show that the strategy yields more stable weights and thus requires less rebalancing than comparable strategies based on realized volatility. As a result, it produces substantially higher spanning regression alphas when transaction costs are taken into account. We document this for ten equity factors, six classes of mean-variance efficient portfolios and 176 anomaly portfolios. We argue that the superior performance of the VIX-based strategy is driven by its ability to time volatility and tail risk simultaneously, resulting from the forward-looking nature of the information entailed in the index and the higher-order return moments embedded in the implied volatility smile.
News-Driven Uncertainty and Volatility Feedback
Ankush Agarwal (University of Glasgow), et al.
This paper presents a two-factor model of asset returns that depends on uncertainty and volatility. To infer the uncertainty from news data, we propose a machine learning-basedmeasure that uses the dispersion of cross-sectional firm-level sentiment. We then theoretically illustrate the potential biases that could arise if the instantaneous feedback and leverage effects of uncertainty and volatility factors are ignored. The newly introduced news-driven uncertainty measure facilitates an empirical investigation of the significant feedback effects, with and without the inclusion of the leverage effect.
Is VIX a Contrarian Indicator? On the Positivity of the Conditional Sharpe Ratio
Ehud I. Ronn (U. of Texas at Austin) and Liying Xu (Spears School of Business)
The notion of compensation for systematic risk is well-ingrained in the finance literature. Whether explicitly considering the security market line’s Capital Asset Pricing Model (CAPM), this notion is the basis for numerous empirical tests. The concept that an increase in the level of systematic risk might be accompanied by an increase in the required risk premium has strong intuitive content: The more risk there is to be borne, the greater the compensation therefor.
The thrust of this paper is to augment previous tests of expected and realized returns by providing simple yet straightforward empirical tests of the proposition the market rewards the undertaking of equity risk. Specifically, we seek to answer the question, Using observed realized returns, is an increase in systematic risk accompanied by an increase in the equity risk premium? Our tentative answer is in the affirmative.
Time-varying Equity Premia with a High-VIX Threshold and Sentiment
Naresh Bansal and Chris T. Stivers (U. of Louisville)
Over the 1990 to 2022 period, we show that time-variation in the return earned from equity-market exposure can be explained well with a simple specification, which predicts: (1) much higher excess returns after the implied volatility from equity-index options exceeds a threshold at around its 80th to 85th percentile; and (2) lower excess returns following a high market sentiment. Our results are robustly evident for 1-, 3-, 6-, and 12-month return horizons, in subperiod analysis, and for both in-sample and out-of-sample evaluations. The predictive R-squared values are substantial at about 20% and 30% for 6-month and 12-month returns, respectively. Furthermore, we find that the VIX-threshold in our specification outperforms other risk explanatory terms suggested by the literature; including the recent high-frequency realized volatility, the equity volatility risk premium, a risk-aversion index measure, stock-market illiquidity, and macroeconomic uncertainty. Our evidence indicate that the VIX-threshold and sentiment capture complementary risk aspects, suggesting an interpretation where VIX largely indicates the level of risk and sentiment is informative about the market’s risk appetite or price of risk.
Using the Volatility Index (VIX) as a Trading Indicator
Steven D. Dolvin and Bryan Foltice (Butler University)
Most technical trading strategies primarily focus on price trends to guide investment decisions. We consider an alternative approach that employs changes in the volatility index (i.e., VIX) as a trading indicator and test a variety of thresholds in an easy to implement trading strategy that even unsophisticated retail investors could follow. Hereby, we explore the effectiveness of rotating out of stocks and into bonds when the VIX rises above certain levels (or thresholds), and vice versa when the VIX declines below that threshold. Using data from 2000-2021, we find that this approach generates excess risk-adjusted returns in a wide variety of thresholds, ranging from 15 up to 59. We also find excess raw returns when using high thresholds ranging from 40 to 59 and also at lower threshold levels (17 to 24), although these latter outcomes are dependent on the use of stock portfolio leverage to increase portfolio efficiency.
Interest Rate Volatility Risk and the Cross-Section of Expected Corporate Bond Returns
Junbo Wang City U. of Hong Kong), et al.
This paper finds that interest rate volatility (IRV) is an important systematic risk factor priced in the corporate bond market. Bonds with high sensitivity to IRV innovations have low expected returns, and this relationship is robust to controlling for aggregate equity volatility risk, conventional risk factors and bond characteristics. The negative IRV risk premium is larger for low-grade bonds and during periods of high economic and monetary policy uncertainty. IRV innovations carry a more negative price of risk than VIX innovations. Interest rate volatility risk effects work through the channels of deteriorations in firm fundamentals, business conditions and policy uncertainty.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno