Expected Stock Returns and the Correlation Risk Premium

2018 ◽  
Author(s):  
Adrian Buss ◽  
Lorenzo Schoenleber ◽  
Grigory Vilkov
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Expected Stock Returns ◽  
Correlation Risk

Volatility in the Indian Stock Market: Evidence from S&P CNX 500

2020 ◽  
Vol 17 (4) ◽  
pp. 1826-1830
Author(s):  
V. Shanthaamani ◽  
V. B. Usha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Conditional Variance ◽  
Leverage Effect ◽  
Volatility Clustering ◽  
Expected Stock Returns ◽  
High Volatility ◽  
Indian Stock Market ◽  
Daily Returns

This paper uses the Generalized Autoregressive Conditional Heteroskedastic models to estimate volatility (conditional variance) in the daily returns of the S&P CNX 500 index over the period from April 2007 to March 2018. The models include both symmetric and asymmetric models that capture the most common stylized facts about index returns such as volatility clustering and leverage effect. The empirical results show that the conditional variance process is highly persistent and provide evidence on the existence of risk premium for the S&P CNX 500 index return series which support the positive correlation hypothesis between volatility and the expected stock returns. Our findings also show that the asymmetric models provide better fit than the symmetric models, which confirms the presence of leverage effect. These results, in general, explain that high volatility of index return series is present in Indian stock market over the sample period.

scholarly journals Firm returns and network centrality

2019 ◽  
Vol 9 (3) ◽  
pp. 74-82
Author(s):  
Zornitsa Todorova
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Predictive Power ◽  
Empirical Relationship ◽  
Investment Strategy ◽  
Network Centrality ◽  
Out Of Sample ◽  
Expected Stock Returns ◽  
Market Beta ◽  
Recent Financial Crisis

Using methods from graph theory and network analysis, this paper identifies, visualizes and analyzes a correlation network of residual stock returns for more than 5,000 US-based publicly traded firms. Building on prior work by Billio et al. (2012), the paper computes a systemic measure of network centrality using principal components analysis. Two main questions are addressed: 1) What is the empirical relationship between expected stock returns and network centrality? and 2) Does network centrality have predictive power to identify firms, which are most at risk during systemic events? First, the paper finds that network centrality has substantial predictive power in out-of-sample tests related to the recent financial crisis. Second, firms that are more central in the network earn higher returns than firms that are located in the periphery. The paper rationalizes this finding by arguing that central firms are characterized by higher market risk because they are more exposed to idiosyncratic shocks passing through the network. Finally, the paper develops a novel factor-mimicking portfolio, weighted by centrality scores. The investment strategy earns an annualized risk premium of 3.38 % controlling for market beta, size and book-to-market.

Commonality in the Determinants of Expected Stock Returns

CFA Digest ◽  
1997 ◽  
Vol 27 (1) ◽  
pp. 41-42
Author(s):  
Terence M. Lim
Keyword(s):  
Stock Returns ◽  
Expected Stock Returns

Consumption, Aggregate Wealth, and Expected Stock Returns

CFA Digest ◽  
2002 ◽  
Vol 32 (1) ◽  
pp. 52-53
Author(s):  
Johann U. de Villiers
Keyword(s):  
Stock Returns ◽  
Expected Stock Returns ◽  
Aggregate Wealth
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