Expected Returns, Risk Premia, and Volatility Surfaces Implicit in Option Market Prices

2008 ◽  
Author(s):  
Antonio Camara ◽  
Timothy L. Krehbiel ◽  
Weiping Li
Keyword(s):  
Risk Premia ◽  
Expected Returns ◽  
Market Prices ◽  
Option Market

Expected returns, risk premia, and volatility surfaces implicit in option market prices

2011 ◽  
Vol 35 (1) ◽  
pp. 215-230 ◽  
Author(s):  
António Câmara ◽  
Tim Krehbiel ◽  
Weiping Li
Keyword(s):  
Risk Premia ◽  
Expected Returns ◽  
Market Prices ◽  
Option Market

scholarly journals Value Return Predictability across Asset Classes and Commonalities in Risk Premia*

2020 ◽  
Author(s):  
Fahiz Baba Yara ◽  
Martijn Boons ◽  
Andrea Tamoni
Keyword(s):  
Risk Premia ◽  
Return Predictability ◽  
Expected Returns ◽  
Government Bonds ◽  
Common Component ◽  
Common Value ◽  
Asset Class ◽  
Asset Classes ◽  
Global Government ◽  
Stock Indexes

Abstract We show that returns to value strategies in individual equities, industries, commodities, currencies, global government bonds, and global stock indexes are predictable in the time series by their respective value spreads. In all these asset classes, expected value returns vary by at least as much as their unconditional level. A single common component of the value spreads captures about two-thirds of value return predictability and the remainder is asset class specific. We argue that common variation in value premia is consistent with rationally time-varying expected returns, because (i) common value is closely associated with standard proxies for risk premia, such as the dividend yield, intermediary leverage, and illiquidity, and (ii) value premia are globally high in bad times.

scholarly journals Testing Beta-Pricing Models Using Large Cross-Sections

2019 ◽  
Vol 33 (6) ◽  
pp. 2796-2842 ◽  
Author(s):  
Valentina Raponi ◽  
Cesare Robotti ◽  
Paolo Zaffaroni
Keyword(s):  
Cross Sections ◽  
Risk Premia ◽  
Expected Returns ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Cross Sectional ◽  
Misspecified Models ◽  
Oxford University ◽  
Fama French Factors ◽  
Published Paper

Abstract We propose a methodology for estimating and testing beta-pricing models when a large number of assets is available for investment but the number of time-series observations is fixed. We first consider the case of correctly specified models with constant risk premia, and then extend our framework to deal with time-varying risk premia, potentially misspecified models, firm characteristics, and unbalanced panels. We show that our large cross-sectional framework poses a serious challenge to common empirical findings regarding the validity of beta-pricing models. In the context of pricing models with Fama-French factors, firm characteristics are found to explain a much larger proportion of variation in estimated expected returns than betas. 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.

The Information Content of Extreme Events Implied in the Non-complete Market

2011 ◽  
Vol 19 (2) ◽  
pp. 121-148
Author(s):  
Moo Sung Kim ◽  
Tae Hun Kang
Keyword(s):  
Extreme Events ◽  
Value At Risk ◽  
Calibration Function ◽  
Market Prices ◽  
Back Testing ◽  
Complete Market ◽  
Testing Performance ◽  
Low Probability ◽  
First Moment ◽  
Option Market

This paper empirically investigates the usefulness of extreme events implied into the non-complete option market in which return generating process of underlying asset is different from that of options. The empirical results find that the information about the extreme events implied in the option market prices has more accurate forecasting power within the tail than near the first moment of realized distribution. So, we expect that the implied information of extreme jump can help to improve the back-testing performance of value at risk where it is primarily important to take account of low-probability events. Regardless of whether calibration function for density transformation is the beta-distribution or non-parametric kernel density, extreme jump provides consistently satisfactory predictions.

Liquidity Risk Premia in Corporate Bond Markets

2012 ◽  
Vol 02 (02) ◽  
pp. 1250006 ◽  
Author(s):  
Frank de Jong ◽  
Joost Driessen
Keyword(s):  
Risk Premium ◽  
Corporate Bonds ◽  
Liquidity Risk ◽  
Risk Premia ◽  
Market Liquidity ◽  
Corporate Bond ◽  
Expected Returns ◽  
Bond Returns ◽  
Treasury Bond ◽  
Investment Grade

This paper explores the role of liquidity risk in the pricing of corporate bonds. We show that corporate bond returns have significant exposures to fluctuations in treasury bond liquidity and equity market liquidity. Further, this liquidity risk is a priced factor for the expected returns on corporate bonds, and the associated liquidity risk premia help to explain the credit spread puzzle. In terms of expected returns, the total estimated liquidity risk premium is around 0.6% per annum for US long-maturity investment grade bonds. For speculative grade bonds, which have higher exposures to the liquidity factors, the liquidity risk premium is around 1.5% per annum. We find very similar evidence for the liquidity risk exposure of corporate bonds for a sample of European corporate bond prices.

Financial Crises and Risk Premia*

2017 ◽  
Vol 132 (2) ◽  
pp. 765-809 ◽  
Author(s):  
Tyler Muir
Keyword(s):  
Financial Crises ◽  
Asset Prices ◽  
Risk Premia ◽  
Expected Returns ◽  
Behavioral Models ◽  
Pricing Models ◽  
Aggregate Consumption ◽  
Consumption Volatility ◽  
International Panel ◽  
Asset Pricing Models

Abstract I analyze the behavior of risk premia in financial crises, wars, and recessions in an international panel spanning over 140 years and 14 countries. I document that expected returns, or risk premia, increase substantially in financial crises, but not in the other episodes. Asset prices decline in all episodes, but the decline in financial crises is substantially larger than the decline in fundamentals so that expected returns going forward are large. However, drops in consumption and consumption volatility are fairly similar across financial crises and recessions and are largest during wars, so asset pricing models based on aggregate consumption have trouble matching these facts. Comparing crises to “deep” recessions strengthens these findings further. By disentangling financial crises from other bad macroeconomic times, the results suggest that financial crises are particularly important to understanding why risk premia vary. I discuss implications for theory more broadly and discuss both rational and behavioral models that are consistent with the facts. Theories where asset prices are related to the health of the financial sector appear particularly promising.

scholarly journals The Cross Section of Foreign Currency Risk Premia and Consumption Growth Risk: Comment

2011 ◽  
Vol 101 (7) ◽  
pp. 3456-3476 ◽  
Author(s):  
Craig Burnside
Keyword(s):  
Foreign Currency ◽  
Risk Premia ◽  
Expected Returns ◽  
Stochastic Discount Factor ◽  
Forward Premium Puzzle ◽  
Cross Sectional ◽  
Consumption Growth ◽  
Forward Premium ◽  
The Cross ◽  
Premium Puzzle

Lustig and Verdelhan (2007) argue that the excess returns to borrowing US dollars and lending in foreign currency “compensate US investors for taking on more US consumption growth risk,” yet the stochastic discount factor corresponding to their benchmark model is approximately uncorrelated with the returns they study. Hence, one cannot reject the null hypothesis that their model explains none of the cross sectional variation of the expected returns. Given this finding, and other evidence, I argue that the forward premium puzzle remains a puzzle. JEL: C58, E21, F31, G11, G12

The Predictability of Future Aggregate Earnings Growth and the Relation between Aggregate Analyst Recommendation Changes and Future Returns

2020 ◽  
pp. 0000-0000
Author(s):  
Bruce K. Billings ◽  
Sami Keskek ◽  
Spencer R. Pierce
Keyword(s):  
Risk Premia ◽  
Expected Returns ◽  
Positive Relation ◽  
Negative Relation ◽  
Earnings Growth ◽  
Future Earnings ◽  
Industry Level ◽  
Aggregate Information ◽  
Expected Earnings ◽  
Over Time

We extend prior research examining the relation between aggregate recommendation changes and future returns by documenting that this relation varies over time as a function of the predictability of future earnings growth. When industry-level earnings growth is more predictable, we find that recommendation changes relate negatively to future returns. Our evidence suggests that this negative relation results from analysts revising recommendations upward for higher expected earnings growth but failing to adjust downward for a related decrease in investor risk aversion and demand for risk premia leading to lower expected returns. In contrast, when industry-level earnings growth is less predictable, we find that recommendation changes relate positively to future returns. However, this positive relation results from analysts and investors similarly underestimating earnings growth persistence. Overall, the evidence fails to support the claim that analysts' recommendation changes incorporate aggregate information in a manner that adds value to investors by predicting future returns.

scholarly journals Estimating Market Expectations for Portfolio Selection Using Penalized Statistical Models

2020 ◽  
Vol 38 (2) ◽  
pp. 133-146
Author(s):  
Carlos Felipe Valencia-Arboleda ◽  
Diego Hernan Segura-Acosta
Keyword(s):  
Portfolio Selection ◽  
Historical Data ◽  
Selection Problem ◽  
Expected Returns ◽  
Portfolio Performance ◽  
Market Prices ◽  
Portfolio Selection Problem ◽  
Implicit Information ◽  
Decision Variables ◽  
Out Of Sample

The portfolio selection problem can be viewed as an optimization problem that maximizes the risk–return relationship. It consists of a number of elements, such as an objective function, decision variables and input parameters, which are used to predict expected returns and the covariance between the said returns. However, the real values of these parameters cannot be directly observed; thus, estimations based on historical data are required. Historical data, however, can often result in modelling errors when the parameters are replaced by their estimations. We propose to address this by using some regularization mechanisms in the optimization.  In addition, we explore the use of implicit information to improve the portfolio performance, such as options market prices, which are a rich source of investor expectations. Accordingly, we propose a new estimator for risk and return that combines historical and implicit information in the portfolio selection problem. We implement the new estimators for the mean-VAR and mean-VaR2 problems using an elastic-net model that reduces the risk of all estimations performed. The results suggest that the model has a good out-of-sample performance that is superior to models with pure historical estimations.

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