Option-Implied Volatility-Managed Asset Pricing Risk Factors and Resurrection of the Value Factor

2019 ◽  
Author(s):  
Klaus Grobys
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Implied Volatility ◽  
Pricing Risk

Risk Factors of Collateralized Loan Obligations and Corporate Bonds

2020 ◽  
Vol 32 (6) ◽  
pp. 347-355
Author(s):  
Mark Wahrenburg ◽  
Andreas Barth ◽  
Mohammad Izadi ◽  
Anas Rahhal
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Asset Pricing ◽  
Systemic Risk ◽  
Corporate Bonds ◽  
Risk Exposure ◽  
Expected Returns ◽  
Yield Spreads ◽  
Pricing Factors ◽  
Return Difference

AbstractStructured products like collateralized loan obligations (CLOs) tend to offer significantly higher yield spreads than corporate bonds (CBs) with the same rating. At the same time, empirical evidence does not indicate that this higher yield is reduced by higher default losses of CLOs. The evidence thus suggests that CLOs offer higher expected returns compared to CB with similar credit risk. This study aims to analyze whether this return difference is captured by asset pricing factors. We show that market risk is the predominant risk factor for both CBs and CLOs. CLO investors, however, additionally demand a premium for their risk exposure towards systemic risk. This premium is inversely related to the rating class of the CLO.

scholarly journals Industry Risk Factors and Stock Returns of Malaysian Oil and Gas Industry: A New Look with Mean Semi-Variance Asset Pricing Framework

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1732
Author(s):  
Mohammad Enamul Hoque ◽  
Soo-Wah Low
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Exchange Rate ◽  
Asset Pricing ◽  
Stock Returns ◽  
Oil And Gas ◽  
Negative Relationship ◽  
Downside Risk ◽  
Total Risk ◽  
Pricing Framework

This study employs a mean semi-variance asset pricing framework to examine the influence of risk factors on stock returns of oil and gas companies. This study also examines how downside risk is priced in stock performance. The time-series estimations expose that market, size, momentum, oil, gas, and exchange rate have significant impacts on oil and gas stock returns, but effects are heterogeneous depending on an individual stock. The two-stage cross-section estimations provide new insights about investors’ risk-return trade-off when facing downside risks. The results show that downside risk exposures to market, momentum, oil, and exchange rate factors are negatively priced in the Malaysian oil and gas stocks. This implies that investors are penalized for their downside exposure to these risk factors, and such inference is consistent with the risk preference explanation of prospect theory. Liquefied natural gas (LNG) is the only risk factor found to be positively priced in the returns of oil and gas stocks. Additionally, we find a negative relationship between LNG factor and total risk. This suggests that as the risk exposure to LNG increases, the total risk decreases, implying that the LNG risk factor is an idiosyncratic risk and not a systematic risk factor. Such interpretation is consistent with the correlation result, which shows no association between LNG and the market risk factor.

The diverging role of the systematic risk factors: evidence from real estate stock markets

2015 ◽  
Vol 33 (1) ◽  
pp. 81-106 ◽  
Author(s):  
Stephan Lang ◽  
Alexander Scholz
Keyword(s):  
Risk Factors ◽  
Time Series ◽  
Asset Pricing ◽  
Real Estate ◽  
Systematic Risk ◽  
Content Type ◽  
Risk Return ◽  
Systematic Risk Factors ◽  
Relationship Of

Purpose – The risk-return relationship of real estate equities is of particular interest for investors, practitioners and researchers. The purpose of this paper is to examine, in an asset pricing framework, whether the systematic risk factors play a significantly different role in explaining the returns of listed real estate companies, compared to general equities. Design/methodology/approach – Running the difference test of the Fama-French three-factor and the liquidity-augmented asset pricing model, the authors analyze the effect of the systematic risk factors related to market, size, BE/ME and liquidity in a time-series setting over the period July 1992 to June 2012. By applying the propensity score matching (PSM) algorithm, the authors bypass the “curse of dimensionality” of traditional matching techniques and identify a comparable control sample of general equities, in terms of the relevant firm characteristics of size, BE/ME and liquidity. Findings – The empirical results indicate that European real estate equity returns load significantly differently on the size, value and liquidity factor, while the influence of the market factor seems to be equivalent. In addition, the authors find an economically and statistically significant underperformance of European real estate equities, after accounting for the diverging role of systematic risk factors. Running the conditional time-series regression, the authors further reveal that these findings are predominately caused by the divergent risk-return behavior of real estate equities in economic downturns. Practical implications – Due to the diverging role of the systematic risk factors in pricing real estate equities, the authors provide evidence of potential diversification benefits for investors and portfolio managers. Originality/value – This is the first real estate asset pricing study to dissect the unique risk-return relationship of real estate equities by employing propensity score matching.

Liquidity and real estate asset pricing: a pan-European study

2014 ◽  
Vol 7 (1) ◽  
pp. 59-86 ◽  
Author(s):  
Alexander Scholz ◽  
Stephan Lang ◽  
Wolfgang Schaefers
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Asset Pricing ◽  
Real Estate ◽  
Stock Returns ◽  
Systematic Risk ◽  
Time Series Regression ◽  
Content Type ◽  
Market Factors ◽  
The Impact

Purpose – Understanding the pricing of real estate equities is a central objective of real estate research. This paper aims to investigate the impact of liquidity on European real estate equity returns, after accounting for well-documented systematic risk factors. Design/methodology/approach – Based on risk factors derived from general equity data, the authors extend the Fama-French time-series regression approach by a liquidity factor, using a pan-European sample of 272 real estate equities. Findings – The empirical results indicate that liquidity is a significant pricing factor in real estate stock returns, even after controlling for market, size and book-to-market factors. In addition, the authors detect that real estate stock returns load predominantly positively on the liquidity risk factor, suggesting that real estate equities tend to behave like illiquid common equities. These findings are underpinned by a series of robustness checks. Running a comparative analysis with alternative factor models, the authors further demonstrate that the liquidity-augmented asset-pricing model is most appropriate for explaining European real estate stock returns. Research limitations/implications – The inclusion of sentiment and downside risk factors could provide further insights into real estate asset pricing in European capital markets. Originality/value – This is the first study to examine the role of liquidity as a systematic risk factor in a pan-European setting.

Option-implied volatility spillover indices for FX risk factors

2017 ◽  
Vol 157 ◽  
pp. 83-87 ◽  
Author(s):  
Klaus Grobys ◽  
Jari-Pekka Heinonen
Keyword(s):  
Risk Factors ◽  
Implied Volatility ◽  
Volatility Spillover

scholarly journals Higher moments and US industry returns: realized skewness and kurtosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoyue Chen ◽  
Bin Li ◽  
Andrew C. Worthington
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Trading Strategies ◽  
Higher Moments ◽  
Pricing Models ◽  
Firm Level ◽  
Content Type ◽  
Industry Level ◽  
Asset Pricing Models ◽  
Common Risk Factors

Purpose The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies. Design/methodology/approach Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships. Findings Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns. Research limitations/implications The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor. Practical implications Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking. Originality/value While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

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