scholarly journals Time-Varying Risk and the Relation between Idiosyncratic Risk and Stock Return

2021 ◽  
Vol 14 (9) ◽  
pp. 432
Author(s):  
Chengbo Fu
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Idiosyncratic Risk ◽  
Point Of View ◽  
Time Varying ◽  
Total Risk ◽  
Risk Return ◽  
Long Run ◽  
Book To Market Ratio ◽  
The 1960S

This paper studies the historical time-varying dynamics of risk for individual stocks in the U.S. market. Total risk of an individual stock is decomposed into two components, systematic risk and idiosyncratic risk, and both components are studied separately. We start from the historical trend in the magnitude of risk and then turn to the relation between idiosyncratic risk and stock returns. The result shows that both components of risk for individual stocks are changing over time. They increased from the 1960s to the 1990s/2000s and then declined until today. This paper also studies the risk-return tradeoff by investigating the relation between idiosyncratic risk and stock return in the long run. Stocks are sorted into portfolios for analysis and the whole sample period is further decomposed into decades for subgroup analysis. Multivariable regressions are used to study this relation as we control for beta, size, book-to-market ratio, momentum and liquidity. From a historical point of view, we show that the relation between idiosyncratic risk and stock return is time-varying, and it did not exist in certain decades. The results indicate that the risk-return tradeoff also varied in history.

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.

scholarly journals PENGARUH HUMAN CAPITAL (LABOR INCOME) TERHADAP EXPECTED STOCK RETURNS

2018 ◽  
Vol 12 (1) ◽  
pp. 58-79
Author(s):  
Caecilia Atmini Susilandari
Keyword(s):  
Human Capital ◽  
Asset Pricing ◽  
Stock Returns ◽  
Stock Return ◽  
Idiosyncratic Risk ◽  
Labor Income ◽  
Pricing Model ◽  
Asset Pricing Model ◽  
Industry Level ◽  
Expected Stock Returns

This research intended to analyse the use of premium as the proxy of human capital (labor income) in the industry level as one of the factors to measure the expected stock returns other than market, smb, hml, umdand liquidity variable that can be applied in Indonesia.The analysis coveres the human capital (labor income) in the industry level to cross section of stock return and the effect of human capital (labor income) to idiosyncratic risk in the asset pricing model. It usesincome percapita to measure the premium variabel in the period of 2001 – 2011 and 30 stocks portfolio chosen based on the biggest market capitalization value in six sector in the period of 2001 – 2011

Long-Run Inflation and Exchange Rates Hedge of Stocks in Brazil and Mexico

2006 ◽  
Vol 6 (3) ◽  
pp. 1850093 ◽  
Author(s):  
Edgar Ortiz ◽  
Alejandra Cabello ◽  
Raúl de Jesús
Keyword(s):  
Stock Market ◽  
Exchange Rates ◽  
Stock Returns ◽  
Latin American ◽  
Developed Countries ◽  
Point Of View ◽  
Long Run ◽  
Stock Market Performance ◽  
Fisher Model

A substantial body of evidence documents the relationship between macroeconomic variables and stock returns and risk from developed countries. The evidence for emerging markets is limited, particularly identifying risk premia compensations for inflation and exchange rates. This paper attempts to quantify the short and long term relationship between inflation and exchange rates with over all stock market performance for the case of the two largest Latin American capital markets, Mexico and Brazil. Extending the Fisher model, the aim is to determine whether or not these markets have failed to keep pace with movements in those two variables (the most unstable and economic growth hampering variables in these economies during the last three decades), and therefore to what extent the stock market succeeds or fails to test as inflation hedges. The empirical evidence is presented assuming positioning of a local investor in their own market, and from the point of view of a U.S. investor in each of these markets. Two unit root tests are also presented to stress long term relationships between stock returns, inflation, and foreign exchange.

scholarly journals Consumption-Based Asset Pricing When Consumers Make Mistakes

2021 ◽  
Vol 2021 (015) ◽  
pp. 1-71
Author(s):  
Chris Anderson ◽  
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Equity Premium ◽  
Expected Returns ◽  
Power Utility ◽  
Expected Return ◽  
Risk Return ◽  
Asset Pricing Models ◽  
Long Run ◽  
Wide Range

I analyze the implications of allowing consumers to make mistakes on the risk-return relationships predicted by consumption-based asset pricing models. I allow for consumption mistakes using a model in which a portfolio manager selects investments on a consumer's behalf. The consumer has an arbitrary consumption policy that could reflect a wide range of mistakes. For power utility, expected returns do not generally depend on exposure to single-period consumption shocks, but robustly depend on exposure to both long-run consumption and expected return shocks. I empirically show that separately accounting for both types of shocks helps explain the equity premium and cross section of stock returns.

scholarly journals A Multivariate Analysis of the Effects of Structural Breaks on Stock Return Volatility Persistence: The Case of the US and Japan

2019 ◽  
Vol 10 (3) ◽  
pp. 39
Author(s):  
Chikashi Tsuji
Keyword(s):  
Multivariate Analysis ◽  
Stock Returns ◽  
Stock Return ◽  
Structural Breaks ◽  
Structural Break ◽  
Return Volatility ◽  
Time Varying ◽  
Stock Return Volatility ◽  
Volatility Persistence ◽  
The Us

This paper quantitatively inspects the effects of structural breaks in stock returns on their volatility persistence by using the stock return data of the US and Japan. More concretely, applying the diagonal BEKK-MGARCH model with and without structural break dummies to the returns of S&P 500 and TOPIX, we reveal the following interesting findings. (1) First, we clarify that for both the US and Japanese stock returns, the values of the GARCH parameters, namely, the values of the volatility persistence parameters in the diagonal BEKK-MGARCH models decrease when we include the structural break dummies in the models. (2) Second, we further find that interestingly, during the Lehman crisis in 2008, the estimated time-varying volatilities from the diagonal BEKK-MGARCH model with structural break dummies are slightly higher than those from the no structural break dummy model. (3) Third, we furthermore reveal that also very interestingly, the estimated time-varying correlations from the diagonal BEKK-MGARCH model with no structural break dummy are slightly higher than those from the structural break dummy model.

Macroeconomic Sensitivity, Risk-Return Trade-Off and Volatility Dynamics Evidence From Developed and Developing Markets

2019 ◽  
Vol 6 (1) ◽  
pp. 1-16
Author(s):  
Faisal Khan ◽  
Hashim Khan ◽  
Saif Ur-Rehman Khan ◽  
Muhammad Jumaa ◽  
Sharif Ullah Jan
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Stock Exchange ◽  
Three Dimensions ◽  
Leverage Effect ◽  
Return Volatility ◽  
Macroeconomic Factors ◽  
Stock Return Volatility ◽  
Risk Return ◽  
The Impact

This study aims to examine the impact of macroeconomic factors on the stock return volatility along with the pricing of risk, and asymmetry and leverage effect on a comparative basis for the USA and UAE markets. Further, these three dimensions are also investigated with regard to various firm's features (such as firm's size and age). The daily data for the period 4th January 2010 to 29th December 2017 of firm stock returns from the New York Stock Exchange (NYSE), the Abu Dhabi Securities Exchange (ADSE), and the Dubai Financial Market (DFM) is considered and three time-series models were applied. The results from GARCH (1. 1) indicated that all the economic factors have significant impact on the stock return volatility in both the markets. Similarly, the study also found evidence of asymmetry & leverage effect using EGARCH in the NYSE (for all firms) and the UAE (partially). Finally, for a majority of the firms, a positive risk-return relationship is found in the UAE and a negative risk-return relationship is found in the NYSE using GARCH-in the mean. Interestingly, these results in context of both markets were different with respect to various firm features such as firm size and age. In light of these results, it is concluded that both the markets have different dynamics with regard to all three dimensions. Hence, the investors have a clear opportunity to diversify their risk and investments across developed and emerging markets.

Empirical Investigation of the Causal Relationships Among Herding, Stock Market Returns, and Illiquidity: Evidence from Major Asian Markets

2014 ◽  
Vol 17 (03) ◽  
pp. 1450018 ◽  
Author(s):  
Zhuo Qiao ◽  
Thomas C. Chiang ◽  
Lin Tan
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Empirical Investigation ◽  
Filter Method ◽  
Time Varying ◽  
Herding Behavior ◽  
Market Returns ◽  
Stock Market Returns ◽  
Causality Tests ◽  
Kalman Filter Method

We apply the Kalman filter method to estimate nine Asian markets and find evidence that stock return dispersions decline as markets experience stress conditions, supporting the existence of herding. This paper finds that herding behavior is time-varying and comoving across markets. Both linear and nonlinear Granger causality tests conclude that there is strong bilateral causality between herding and returns for all nine Asian markets. For markets in Japan, South Korea, and Thailand, we consistently find strong two-way causality exists in pairwise variables among herding, stock returns, and illiquidity. No consistent evidence can be drawn from other markets for other pairwise variables.

Default Risk and Stock Returns: Evidence from Indian Corporate Sector

2021 ◽  
pp. 097226292110033
Author(s):  
Gurmeet Singh ◽  
Ravi Singla
Keyword(s):  
Stock Returns ◽  
Default Risk ◽  
Stock Return ◽  
Linear Regression Analysis ◽  
Negative Relationship ◽  
Point Of View ◽  
Z Score ◽  
Score Model ◽  
Financial Obligation ◽  
The Relationship

Default risk is associated with the probability that a leveraged firm is not able to pay its financial obligation on time. Relationship between default risk and stock returns is very important from investor’s point of view because it has important implication for risk and return trade off. Relationship between default risk and returns is debatable issue and contradictory results are found in the literature regarding the relationship between default risk and stock returns. Default risk assessment helps the investors and lenders to accurately assess the risks to which investors or lenders are exposed. There are several models which can be used to assess the probability of default. In the present study, the widely used Altman’s Z-score model is used as a measure of default risk to find out the relationship between default risk and stock returns using simple linear regression analysis. It is found that Altman’s Z-score can be used as a measure of default risk and results indicate the existence of positive relationship between Z-score and stock return and hence a negative relationship between default risk and stock return.

Predicting short-to-medium horizon returns

2021 ◽  
pp. 251-272
Author(s):  
Jesper Rangvid
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Return ◽  
Return Predictability ◽  
Yield Spread ◽  
Dividend Yield ◽  
Important Lesson ◽  
Stock Return Predictability ◽  
Long Run ◽  
Short Run

This chapter lays out what we know about stock return predictability on the short-to-medium horizon. It recognizes that most of the fluctuations in the stock market are unpredictable, but characterizes those that are. Another important lesson of this chapter is that stock markets are very volatile in the short run but appears to be less so in the long run. Paradoxically, this implies that it looks as if we can say a little more about the future movements in the stock market when dealing with the longer run (several years). From today until tomorrow, or next week, we can say very little. The chapter illustrates how stock returns are somewhat predictable by indicators such as the yield spread and the dividend yield.

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