Decomposed Higher-Moment Risk Premiums and Market Return Predictability

2020 ◽  
Author(s):  
Julian Dörries
Keyword(s):  
Return Predictability ◽  
Risk Premiums ◽  
Market Return

Bond Risk Premiums with Machine Learning

2020 ◽  
Author(s):  
Daniele Bianchi ◽  
Matthias Büchner ◽  
Andrea Tamoni
Keyword(s):  
Machine Learning ◽  
Yield Curve ◽  
Return Predictability ◽  
Time Varying ◽  
Short Term ◽  
Risk Premiums ◽  
Machine Learning Methods ◽  
Yield Information ◽  
Bond Return ◽  
Bond Risk

Abstract We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.

Macroeconomic Tail Risks and Asset Prices

2019 ◽  
Vol 33 (8) ◽  
pp. 3541-3582 ◽  
Author(s):  
David Schreindorfer
Keyword(s):  
Stock Market ◽  
Asset Prices ◽  
Equity Premium ◽  
Return Predictability ◽  
Risk Premiums ◽  
Asset Pricing Model ◽  
Oxford University ◽  
Excess Volatility ◽  
Equity Index ◽  
Published Paper

Abstract I document that dividend growth and returns on the aggregate U.S. stock market are more correlated with consumption growth in bad economic times. In a consumption-based asset pricing model with a generalized disappointment-averse investor and small, IID consumption shocks, this feature results in a realistic equity premium despite low risk aversion. The model is consistent with the main facts about stock market risk premiums inferred from equity index options, remains tightly parameterized, and allows for analytical solutions for asset prices. An extension with non-IID dynamics accounts for excess volatility and return predictability, while preserving the model’s consistency with option moments. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Stock market return predictability: Google pessimistic sentiments versus fear gauge

2017 ◽  
Vol 5 (1) ◽  
pp. 1390897 ◽  
Author(s):  
Ume Habibah ◽  
Suresh Rajput ◽  
Ranjeeta Sadhwani ◽  
David McMillan
Keyword(s):  
Stock Market ◽  
Return Predictability ◽  
Market Return ◽  
Stock Market Return

scholarly journals Non-Diversifiable Volatility Risk and Risk Premiums at Earnings Announcements

2014 ◽  
Vol 89 (5) ◽  
pp. 1579-1607 ◽  
Author(s):  
Mary E. Barth ◽  
Eric C. So
Keyword(s):  
Risk Premium ◽  
Earnings Announcements ◽  
Return Volatility ◽  
Volatility Risk ◽  
Risk Premiums ◽  
Market Return ◽  
Predictable Variation ◽  
Jel Classifications

ABSTRACT This study seeks to determine whether earnings announcements pose non-diversifiable volatility risk that commands a risk premium. We find that investors anticipate some earnings announcements to convey news that increases market return volatility and pay a premium to hedge this non-diversifiable risk. In particular, we find evidence of risk premiums embedded in prices of firms' traded options that are significantly positively associated with the extent to which the firms' earnings announcements pose non-diversifiable volatility risk. In addition, we find that volatility risk premiums are concentrated among bellwether firms and result in predictable variation in option straddle returns around earnings announcements. Taken together, our findings show that some earnings announcements pose non-diversifiable volatility risk that commands a risk premium. JEL Classifications: M41; G12; G13; G14

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