Downside Variance Risk Premium

Feunou, Bruno; Jahan‐Parvar, Mohammad R.; Okou, Cédric

doi:10.17016/feds.2015.020

Cited by 33 publications

(55 citation statements)

References 91 publications

(78 reference statements)

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“…These different compensations reveal another form of asymmetry or non-linearity that can help explain the (joint) dynamics of higher-order asset moments. Kilic and Shaliastovich (2018), Held, Kapraun, Omachel, and Thimme (2018), and Feunou, Jahan-Parvar, and Okou (2017) propose various ways to decompose the VP into its downside (negative returns or bad states) and upside (positive returns or good states) components. We contribute to this literature by examining a wide range of models to obtain the physical expectation of downside and upside variances and, thus, to calculate the VP under different market environments.…”

Section: Related Literature and Contributionsmentioning

confidence: 99%

“…DVP and UVP capture asymmetric investors' attitudes toward variance risks emanating from the left and right tail of the return distribution, respectively. Yet, there is scanty research on the estimation, dynamics, and economic interpretation of the VP components (with a few notable exceptions like Kilic and Shaliastovich (2018) and Feunou, Jahan-Parvar, and Okou (2017)).…”

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: 99%

Section: Introductionmentioning

confidence: 99%

Variance Risk Premium Components and International Stock Return Predictability

Londoño

2019

internationalfinance

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“…Then, we assemble a new asymmetric fear connectedness index that is used as a forward-looking systemic risk monitoring tool. a growing strand of literature studying the asymmetric characteristics of volatility (see Barndorff-Nielsen et al, 2010;Patton and Sheppard, 2015;Segal et al, 2015;Feunou et al, 2017;Kilic and Shaliastovich, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution in this paper is the construction of a forward-looking monitoring tool extracted from stock option prices, that is, an asymmetric fear connectedness measure. When constructed from put options only, the new measure reflects investors' negative future expectations associated with "bad" volatility (e.g., Segal et al, 2015), which could be linked to a possible decrease in economic growth and equity value and an increase in uncertainty (see Baruník et al, 2016;Feunou et al, 2017). Moreover, in some cases, volatility may reflect a positive direction associated with events that may trigger higher returns (Diebold and Yilmaz, 2015) 2 .…”

Section: Introductionmentioning

confidence: 99%

“…Thus, there might be some events related to U.S. financial institutions that could be considered harmful because they increase "bad" volatility and can transmit across the system, creating the negative connectedness. However, other events that will increase "good" volatility and transmit across the system can create positive connectedness (see Segal et al, 2015;Baruník et al, 2016;Feunou et al, 2017). For instance, Kilic and Shaliastovich (2018) found a "synergy" between the good and bad components of the variance risk premium when they are included in the same model, increasing return predicability compared to the aggregate measure of the variance premium.…”

Section: Introductionmentioning

confidence: 99%

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Asymmetric Connectedness of Fears in the U.S. Financial Sector

2018

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Return predictability of variance differences: A fractionally cointegrated approach

Izzeldin

Yao

2020

Journal of Futures Markets

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This paper examines the fractional cointegration between downside (upside) components of realized and implied variances. A positive association is found between the strength of their cofractional relation and the return predictability of their differences. That association is established via the common longmemory component of the variances that are fractionally cointegrated, which represents the volatility-of-volatility factor that determines the variance premium. Our results indicate that market fears play a critical role not only in driving the long-run equilibrium relationship between implied-realized variances but also in understanding the return predictability. A simulation study further verifies these claims. K E Y W O R D S fractional cointegration, return predictability, variance risk premium J E L C L A S S I F I C A T I O N C14; C32; C51 | INTRODUCTIONNumerous empirical studies suggest that, unlike the variance, the variance risk premium (VRP) carries nontrivial predictive power for aggregate stock market returns over quarterly to annual horizons, where the degree of return predictability is greater than that afforded by more conventional predictors, see, Bollerslev, Tauchen, and Zhou (2009), Drechsler and Yaron (2010), Bollerslev, Marrone, Xu, and Zhou (2014), and Bali and Zhou (2016), among others. Those studies also provide strong evidence that much of the predictability implicit in the VRP may be attributed to its close relation with macroeconomic uncertainty and aggregate risk aversion.The VRP is formally defined as the wedge between option-implied and realized variances. It is measured as the difference between (the square of) the CBOE VIX index and the statistical expectation of the future return variation. In Bollerslev et al. (2009), Drechsler andYaron (2010) and Bollerslev, Sizova, and Tauchen (2012), the return predictability of the VRP is investigated using a self-contained general equilibrium model. This is in the spirit of the long-run risks (LRR) model pioneered by Bansal and Yaron (2004). Specifically, Bollerslev et al. (2009) andBollerslev et al. (2012) extend the LRR model by allowing the time-varying volatility-of-volatility (vol-of-vol) within the economy to be generated by its own stochastic process. They further show that the difference between the risk-neutral and the objective expectations of return variation isolates the vol-of-vol factor, which then serves as the sole source of the true VRP. The VRP, therefore, displays good predictive power for future returns in cases where the vol-of-vol plays a more dominant role in determining variation in the equity premium. A direct link between the VRP and the vol-of-vol is also demonstrated in a purely probabilistic model introduced by Barndorff-Nielsen and Veraart (2013). U D . The SJ and IVA are, respectively, measured as the difference between RV U and RV D and the difference between IV U and IV D . Using a semiparametric approach, we demonstrate the existence of a fractionally cointegrating relation in each pair of the semivariances...

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Downside Variance Risk Premium

Cited by 33 publications

References 91 publications

Variance Risk Premium Components and International Stock Return Predictability

Variance Risk Premium Components and International Stock Return Predictability

Asymmetric Connectedness of Fears in the U.S. Financial Sector

Return predictability of variance differences: A fractionally cointegrated approach

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