Is There an Intertemporal Relation between Downside Risk and Expected Returns?

2009 ◽  
Vol 44 (4) ◽  
pp. 883-909 ◽  
Author(s):  
Turan G. Bali ◽  
K. Ozgur Demirtas ◽  
Haim Levy
Keyword(s):  
Stock Returns ◽  
Value At Risk ◽  
Risk Measures ◽  
Downside Risk ◽  
Expected Returns ◽  
Significant Relation ◽  
Macroeconomic Variables ◽  
Portfolio Returns ◽  
Risk Return ◽  
Expected Stock Returns

AbstractThis paper examines the intertemporal relation between downside risk and expected stock returns. Value at Risk (VaR), expected shortfall, and tail risk are used as measures of downside risk to determine the existence and significance of a risk-return tradeoff. We find a positive and significant relation between downside risk and the portfolio returns on NYSE/AMEX/Nasdaq stocks. VaR remains a superior measure of risk when compared with the traditional risk measures. These results are robust across different stock market indices, different measures of downside risk, loss probability levels, and after controlling for macroeconomic variables and volatility over different holding periods as originally proposed by Harrison and Zhang (1999).

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.

scholarly journals Value-at-Risk and Expected Stock Returns: Evidence from Pakistan

2014 ◽  
Vol 19 (2) ◽  
pp. 71-100 ◽  
Author(s):  
Javed Iqbal ◽  
Sara Azher
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Value At Risk ◽  
Emerging Market ◽  
Explanatory Power ◽  
Expected Returns ◽  
Empirical Performance ◽  
Expected Stock Returns ◽  
Time Series Approach ◽  
Market Factors

This study investigates whether exposure to downside risk, as measured by value-at-risk (VaR), explains expected returns in an emerging market, i.e., Pakistan. We find that portfolios with a higher VaR are associated with higher average returns. In order to explore the empirical performance of VaR at the portfolio level, we use a time series approach based on 25 size and book-to-market portfolios. Based on monthly portfolio data for October 1992 to June 2008, the results show that VaR has greater explanatory power than the market, size, and book-to-market factors.

The Impact of Economic Sentiment, Consumer, Producer and Investor's Confidence Indices on Stock Returns of the Listed Companies in FTSE-20 in Greece

2018 ◽  
pp. 162-209
Author(s):  
Constantine Cantzos ◽  
Petros Kalantonis ◽  
Aristidis Papagrigoriou ◽  
Stefanos Theotokas
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Macroeconomic Variables ◽  
Production Index ◽  
Behavioral Indicators ◽  
Portfolio Returns ◽  
Long Run ◽  
Short Run ◽  
The Impact ◽  
The Relationship

This chapter examines the relationship between stock returns of companies listed in the FTSE-20 on the Athens Exchange and behavioral indicators. The research is based on the behavioral APT model, which examines stock returns' risk factors through the involvement of macroeconomic variables and behavioral indicators. The data is the closing price of 17 shares listed in the FTSE-20 index, a number of macroeconomic variables, and a series of behavioral indicators for the period of January 2001-December 2014. Regressions were conducted with dependent variable stock returns of a portfolio invested equally in these 17 stocks. In addition, the research tests the existence of long-run and short-run equilibrium and causality. The change in the industrial production index along with the risk premium have a positive and significant impact on the portfolio returns. Johansen's test showed that there is a long-run equilibrium between stock returns, macroeconomic variables, and behavioral indicators. The VECM and VAR models showed that there is not long and short-run causality, not even Granger causality. No similar research has been conducted in Greece, thus it fills a literature gap.

scholarly journals The impact of downside risk on UK stock returns

2019 ◽  
Vol 18 (1) ◽  
pp. 53-70
Author(s):  
Fangzhou Huang
Keyword(s):  
Stock Returns ◽  
Financial Crises ◽  
Negative Impact ◽  
Positive Impact ◽  
Downside Risk ◽  
Content Type ◽  
Portfolio Returns ◽  
Time Sensitivity ◽  
Window Approach ◽  
The Impact

PurposeThis paper aims to investigate patterns in UK stock returns related to downside risk, with particular focus on stock returns during financial crises.Design/methodology/approachFirst, stocks are sorted into five quintile portfolios based on the relevant beta values (classic beta, downside beta and upside beta, calculated by the moving window approach). Second, patterns of portfolio returns are examined during various sub-periods. Finally, predictive powers of beta and downside beta are examined.FindingsThe downside risk is observed to have a significant positive impact on contemporaneous stock returns and a negative impact on future returns in general. In contrast, an inverse relationship between risk and return is observed when stocks are sorted by beta, contrary to the classic literature. UK stock returns exhibit clear time sensitivity, especially during financial crises.Originality/valueThis paper focuses on the impact of the downside risk on UK stock returns, assessed via a comprehensive sub-period analysis. This paper fills the gap in the existing literature, in which very few studies examine the time sensitivity in relation to the downside risk and the risk-return anomaly in the UK stock market using a long sample period.

Extreme Downside Risk and Expected Stock Returns

2009 ◽  
Author(s):  
Wei Huang ◽  
Qianqiu Liu ◽  
S. Ghon Rhee ◽  
Feng Wu
Keyword(s):  
Stock Returns ◽  
Downside Risk ◽  
Expected Stock Returns

On the Relation between EGARCH Idiosyncratic Volatility and Expected Stock Returns

2014 ◽  
Vol 49 (1) ◽  
pp. 271-296 ◽  
Author(s):  
Hui Guo ◽  
Haimanot Kassa ◽  
Michael F. Ferguson
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Return Time ◽  
Idiosyncratic Volatility ◽  
Model Parameters ◽  
Risk Return ◽  
Look Ahead ◽  
Monthly Return ◽  
Expected Stock Returns ◽  
Estimation Of Model Parameters

AbstractA spurious positive relation between exponential generalized autoregressive conditional heteroskedasticity (EGARCH) estimates of expected monthtidiosyncratic volatility and monthtstock returns arises when the monthtreturn is included in estimation of model parameters. We illustrate via simulations that this look-ahead bias is problematic for empirically observed degrees of stock return skewness and typical monthly return time series lengths. Moreover, the empirical idiosyncratic risk-return relation becomes negligible when expected monthtidiosyncratic volatility is estimated using returns only up to montht− 1.

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)

scholarly journals Variance Premium, Downside Risk, and Expected Stock Returns

2017 ◽  
Author(s):  
Bruno Feunou ◽  
Ricardo Lopez Aliouchkin ◽  
Roméo Tédongap ◽  
Lai Xu
Keyword(s):  
Stock Returns ◽  
Downside Risk ◽  
Expected Stock Returns

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.

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