The Cross-Section of Volatility and Expected Returns: Then and Now

Detzel, Andrew L.; Duarte, Jefferson; Kamara, Avraham; Siegel, Stephan; Sun, Celine

doi:10.2139/ssrn.3455609

Cited by 4 publications

(2 citation statements)

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“…They investigated how the stochastic volatility of the market is priced in the cross section of expected stock returns and empirically tested the hypothesis that stocks with different sensitivities to innovations in aggregate volatility should have different expected returns. They maintain that volatility has to be priced in the cross section of stocks as well, should the volatility of market return be a systematic risk factor [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

The Cross-Sectional Intrinsic Entropy—A Comprehensive Stock Market Volatility Estimator

Vințe

Ausloos

2022

Entropy

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To take into account the temporal dimension of uncertainty in stock markets, this paper introduces a cross-sectional estimation of stock market volatility based on the intrinsic entropy model. The proposed cross-sectional intrinsic entropy (CSIE) is defined and computed as a daily volatility estimate for the entire market, grounded on the daily traded prices—open, high, low, and close prices (OHLC)—along with the daily traded volume for all symbols listed on The New York Stock Exchange (NYSE) and The National Association of Securities Dealers Automated Quotations (NASDAQ). We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman–Klass, Rogers–Satchell, Yang–Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor’s 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. Our study uses an approximate 6000-day reference point, starting 1 January 2001, until 23 January 2022, for both the NYSE and the NASDAQ. We found that the CSIE market volatility estimator is consistently at least 10 times more sensitive to market changes, compared to the volatility estimate captured through the market indices. Furthermore, beta values confirm a consistently lower volatility risk for market indices overall, between 50% and 90% lower, compared to the volatility risk of the entire market in various time intervals and rolling windows.

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Section: Introductionmentioning

confidence: 99%

The Cross-Sectional Intrinsic Entropy—A Comprehensive Stock Market Volatility Estimator

Vințe

Ausloos

2022

Entropy

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“…On the other hand, the realized idiosyncratic volatility is believed to exist due to a risk factor that is neglected in the Fama and French [15] three factors model [16]. Additionally, the stock volatility and the macroeconomy are mentioned to be strongly related.…”

Section: Introductionmentioning

confidence: 99%

Firm-specific News and Anomalies

Hoang¹,

Tuan²

2021

Investment Strategies in Emerging New Trends in Finance

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This study investigates the relation between idiosyncratic volatility and future returns around the firm-specific news announcements in the Korean stock market from July 1995 to June 2018. The excess returns of decile portfolios that are formed by sorting the stocks based on news and non-news idiosyncratic volatility measures. The Fama and French three-factor model is also examined to see whether systematic risk affects news and non-news idiosyncratic volatility profits. The pricing of our news and non-news idiosyncratic volatility are confirmed in the cross-sectional regression using the Fama and MacBeth method. Market beta, size, book to market, momentum, liquidity, and maximum return are controlled to determine robustness. Our empirical evidence suggests that the pricing of the non-news idiosyncratic volatility is more strongly negative compared to the news idiosyncratic volatility, which is contrary to the limited arbitrage explanation for the negative price of the idiosyncratic volatility. We find that the non-news idiosyncratic volatility has a robust negative relation to returns in non-January months. Macro-finance factors drive the conditioned on the missing risk factor hypothesis, the pricing of idiosyncratic volatility. This study contributes to a better understanding of the role of the conditional idiosyncratic volatility in asset pricing. As the Korean stocks provide a fresh sample, our non-U.S. investigation delivers a useful out-of-sample test on the pervasiveness of the non-news volatility effect across the emerging markets.

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Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns

Barinov

2011

SSRN Journal

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The Cross-Section of Volatility and Expected Returns: Then and Now

Cited by 4 publications

References 0 publications

The Cross-Sectional Intrinsic Entropy—A Comprehensive Stock Market Volatility Estimator

The Cross-Sectional Intrinsic Entropy—A Comprehensive Stock Market Volatility Estimator

Firm-specific News and Anomalies

Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns

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