Volatility and Expected Option Returns

2019 ◽  
Vol 55 (3) ◽  
pp. 1025-1060 ◽  
Author(s):  
Guanglian Hu ◽  
Kris Jacobs
Keyword(s):  
Stock Returns ◽  
Expected Return ◽  
Cross Sectional ◽  
Stock Volatility ◽  
Option Returns ◽  
Put Option ◽  
Expected Stock Returns ◽  
Alternative Measures ◽  
Expected Option Returns ◽  
Equity Option

We analyze the relation between expected option returns and the volatility of the underlying securities. The expected return from holding a call (put) option is a decreasing (increasing) function of the volatility of the underlying. These predictions are supported by the data. In the cross section of equity option returns, returns on call (put) option portfolios decrease (increase) with underlying stock volatility. This finding is not due to cross-sectional variation in expected stock returns. It holds in various option samples with different maturities and moneyness, and is robust to alternative measures of underlying volatility and different weighting methods.

Can the Cross-Sectional Variation in Expected Stock Returns Explain Momentum?

2009 ◽  
Vol 44 (4) ◽  
pp. 777-794 ◽  
Author(s):  
George Bulkley ◽  
Vivekanand Nawosah
Keyword(s):  
Stock Returns ◽  
Expected Returns ◽  
Sample Period ◽  
Expected Return ◽  
Cross Sectional ◽  
Holding Period ◽  
Expected Stock Returns ◽  
The Cross

AbstractIt has been hypothesized that momentum might be rationally explained as a consequence of the cross-sectional variation of unconditional expected returns. Stocks with relatively high unconditional expected returns will on average outperform in both the portfolio formation period and in the subsequent holding period. We evaluate this explanation by first removing unconditional expected returns for each stock from raw returns and then testing for momentum in the resulting series. We measure the unconditional expected return on each stock as its mean return in the whole sample period. We find momentum effects vanish in demeaned returns.

Understanding the Determinants of Analyst Target Price Implied Returns

2020 ◽  
Vol 95 (6) ◽  
pp. 125-149
Author(s):  
Patricia M. Dechow ◽  
Haifeng You
Keyword(s):  
Stock Returns ◽  
Investment Decision ◽  
Optimistic Bias ◽  
Target Price ◽  
Expected Return ◽  
Cross Sectional ◽  
Empirical Risk ◽  
Investment Decision Making ◽  
The Cross ◽  
Jel Classifications

ABSTRACT We investigate the determinants of analysts' target price implied returns and the implication of our findings for investment decision-making. We identify four broad sets of factors that help explain the cross-sectional variation in target price implied returns: future realized stock returns, errors in forecasting fundamentals, errors in forecasting the expected return to risk, and biases relating to analysts' incentives. Our results suggest that all four sets help explain target price implied returns, with errors in forecasting the expected return to empirical risk proxies having the greatest impact. Collectively, these variables explain nearly a quarter of the cross-sectional variation in target price implied returns. We use our model to predict the optimistic bias in target price implied returns and evaluate whether investors correctly ignore the predictable bias. The results suggest that investors make similar valuation errors to analysts and/or do not perfectly back out the predicted bias in target prices. JEL Classifications: M40; M41; G14.

scholarly journals Previsibilidade de Retorno das Ações na Bovespa: Um Teste Envolvendo o Modelo de Fator de Retorno Esperado

2004 ◽  
Vol 2 (2) ◽  
pp. 183
Author(s):  
Luciano Martin Rostagno ◽  
Gilberto De Oliveira Kloeckner ◽  
João Luiz Becker
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Factor Model ◽  
Explanatory Power ◽  
Return Predictability ◽  
Expected Return ◽  
Cross Sectional ◽  
Theoretical Assumptions ◽  
History Of

This paper examines the hypothesis of asst return predictability in the Brazilian Stock Market (Bovespa). Evidence suggests that seven factors explain most of the monthly differential returns of the stocks included in the sample. Within the factors that present statistically significant mean, two are liquidity factors (market capitalization and trading volume trend), three refer to price level of stocks (dividend to price, dividend to price trend, and cash flow to price), and two relate to price history of stocks (3 and 12 months excess return). Contradicting theoretical assumptions, risk factors present no explanatory power on cross-sectional returns. Using an expected return factor model, it is contended that stock returns are quite predictable. An investment simulation shows that the model is able to assemble portfolios with statistically significant higher returns. Additional tests indicate that the winner portfolios are not fundamentally riskier suggesting mispricing of assets in the Brazilian stock Market.

Firm Characteristics, Cross-Sectional Regression Estimates, and Asset Pricing Tests

2019 ◽  
Vol 10 (2) ◽  
pp. 290-334 ◽  
Author(s):  
Chris Kirby
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Nonlinear Effects ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Cross Sectional ◽  
Portfolio Returns ◽  
Asset Pricing Models ◽  
Formidable Challenge ◽  
Expected Stock Returns

Abstract I test a number of well-known asset pricing models using regression-based managed portfolios that capture nonlinearity in the cross-sectional relation between firm characteristics and expected stock returns. Although the average portfolio returns point to substantial nonlinearity in the data, none of the asset pricing models successfully explain the estimated nonlinear effects. Indeed, the estimated expected returns produced by the models display almost no variation across portfolios. Because the tests soundly reject every model considered, it is apparent that nonlinearity in the relation between firm characteristics and expected stock returns poses a formidable challenge to asset pricing theory. (JEL G12, C58)

Idiosyncratic Volatility and the Cross Section of Expected Returns

2008 ◽  
Vol 43 (1) ◽  
pp. 29-58 ◽  
Author(s):  
Turan G. Bali ◽  
Nusret Cakici
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Idiosyncratic Volatility ◽  
Expected Returns ◽  
Cross Sectional ◽  
Monthly Data ◽  
Portfolio Returns ◽  
Expected Stock Returns ◽  
The Cross ◽  
Data Frequency

AbstractThis paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that i) the data frequency used to estimate idiosyncratic volatility, ii) the weighting scheme used to compute average portfolio returns, iii) the breakpoints utilized to sort stocks into quintile portfolios, and iv) using a screen for size, price, and liquidity play critical roles in determining the existence and significance of a relation between idiosyncratic risk and the cross section of expected returns. Portfoliolevel analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse volatility-weighted), three breakpoints (CRSP, NYSE, equal market share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that no robustly significant relation exists between idiosyncratic volatility and expected returns.

Measuring gambling activity in options market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bei Chen ◽  
Quan Gan
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Stock Return ◽  
Return Predictability ◽  
Capital Gains ◽  
Gambling Activity ◽  
Cross Sectional ◽  
Content Type ◽  
Option Returns ◽  
Model Free

PurposeThis paper investigates how the gambling measure captures market bubble events, and how it predicts stock return and option return.Design/methodology/approachThis paper proposes a gambling activity measure by jointly considering open interest and moneyness of out-of-the-money (OTM) individual equity call options.FindingsThe new measure, CallMoney, captures excessive optimism during the dot-com bubble, the oil price bubble and the pre-GFC stock market bubble. CallMoney robustly and negatively predicts both OTM and at-the-money call option returns cross-sectionally. The option return predictability of CallMoney is stronger when stock price is further from its 52-weeks high, capital gains overhang is lower, and when information uncertainty of the underlying stock is higher. CallMoney also robustly and negatively predicts cross-sectional stock returns.Originality/valueThe gambling measure has the advantages of being economically intuitive, model-free, easy to measure. The measure performs more robustly than existing lottery measures with respect to option and stock return predictability and more reliably captures the overpricing of options and stocks. The work helps understanding the gambling related anomalies in equity option returns and stock returns.

Sign in / Sign up

Export Citation Format

Share Document