The Time Variation in Risk Appetite and Uncertainty

Bekaert, Geert; Engström, Eric; Xu, Nancy R.

doi:10.2139/ssrn.3069078

Cited by 25 publications

(40 citation statements)

References 104 publications

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“…An international extension of this model is used in Londono (2015) and Bollerslev, Marrone, Xu, and Zhou (2014) to understand the predictive power of U.S. and global VP for international stock returns. A recent study by Bekaert, Engstrom, and Xu (2017) formally justifies the close relation between VP and risk aversion in a noarbitrage dynamic asset pricing model with time-varying risk aversion and uncertainty that consistently prices a wide range of domestic risky assets. Bekaert, Engstrom, and Xu (2017) establishes that risk aversion explains almost 90% of U.S. VP.…”

Section: Related Literature and Contributionsmentioning

confidence: 82%

“…The model has two key features. On the one hand, the disturbance of U.S. risk premium state variables (economic uncertainty and risk aversion) is modeled with heteroskedastic and asymmetric gamma shocks, as in Bekaert, Engstrom, and Xu (2017). This modeling choice allows us to introduce realistic shock assumptions while generating a parsimonious affine-class model solution.…”

Section: Introductionmentioning

confidence: 99%

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Variance Risk Premium Components and International Stock Return Predictability

Londoño

2019

internationalfinance

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In this paper, we document and explain the distinct behaviors of U.S. downside and upside variance risk premiums (DVP and UVP, respectively) and their international stock return predictability patterns. DVP, the compensation for bearing downside variance risk, is positive, highly correlated with the total variance premium, and countercyclical, whereas UVP is, on average, borderline positive and procyclical with large negative spikes around episodes of market turmoil. We then provide robust evidence that decomposing VP into its downside and upside components significantly improves domestic and international stock return predictability. DVP is a robust predictor at four to six months and exhibits a hump-shaped pattern, whereas UVP performs the best at very short horizons. These stylized facts highlight the importance of acknowledging asymmetry in equity risk premiums. Hence, in the second part of the paper, we rationalize the economic sources of DVP and UVP in an international dynamic asset pricing model featuring asymmetric and time-varying risk aversion and economic uncertainty in a partially integrated world economy. We show that DVP is mostly driven by the upside movements of risk aversion, whereas UVP loads significantly and negatively on downside economic uncertainty. Moreover, we find that DVP (UVP) transmits to international markets mostly through financial integration (real economic integration).

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Section: Related Literature and Contributionsmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Variance Risk Premium Components and International Stock Return Predictability

Londoño

2019

internationalfinance

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“…However, our evidence suggests that risk aversion captures significant predictive information, particular for shorter-maturity bonds and shorter forecast horizons, possibly capturing short-term funding concerns during turbulent periods, rather than expectations on future interest rates. Furthermore, considering that the risk aversion measure, by construction, is highly correlated with the variance risk premium embedded in option prices (Bekaert et al 2017), the finding that the forecasting performance of RA is largely limited to shorter forecast horizons may be related to investor's short-term protection strategies via relatively cheaper, short-term options, driving their prices higher (thus option implied volatility). On the other hand, the relative underperformance of risk aversion as a predictor for longer maturities and forecast horizons might reflect possible mean reversion in investor sentiment and/or biases in the long-term risk outlook.…”

Section: Resultsmentioning

confidence: 99%

“…where rx (n) t+1 is the continuously compounded excess return on an n-year zero coupon bond in period t + 1. Depending on the model specification, Z t includes the single forward factor (CP) of Cochrane and Piazzesi (2005) 2 , the macro factors (LN) constructed by Ludvigson andNg (2009, 2011) using dynamic factor analysis, 3 and the risk-aversion (RA) measure developed by Bekaert et al (2017)…”

Section: Methodsmentioning

confidence: 99%

“…Motivated by these studies and considering the role of Treasury securities as a safe haven during periods of market turbulence, this paper explores the predictive power of risk aversion over Treasury bond premia by utilizing a new measure of time-varying risk aversion developed by Bekaert et al (2017). The measure of risk aversion used as a predictor in our tests is obtained from observable financial information and distinguishes time variation in economic uncertainty (the amount of risk) from time variation in risk aversion (the price of risk), thus allowing us to explore the predictive ability of non-cash flow shocks over bond premia, even after controlling for the well-cited CP factor, and a large number of macro and financial factors (Ludvigson andNg 2009, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Time-varying risk aversion and the predictability of bond premia

Çepni

Demirer

Gupta

et al. 2020

Finance Research Letters

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We show that time-varying risk aversion captures significant predictive information over excess returns on U.S. government bonds even after controlling for a large number of financial and macro factors. Including risk aversion improves the predictive accuracy at all horizons (one-to twelve-months ahead) for shorter maturity bonds and at shorter forecast horizons (one-to three-months ahead) for longer maturity bonds.Given the role of Treasury securities in economic forecasting models and portfolio allocation decisions, our findings have significant implications for investors, policy makers and researchers interested in accurately forecasting return dynamics for these assets.

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Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

et al. 2022

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Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile‐based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011–10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward‐looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision‐making process of risk managers and policymakers.

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The Time Variation in Risk Appetite and Uncertainty

Cited by 25 publications

References 104 publications

Variance Risk Premium Components and International Stock Return Predictability

Variance Risk Premium Components and International Stock Return Predictability

Time-varying risk aversion and the predictability of bond premia

Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

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