The price of variance risk

Dew-Becker, Ian; Giglio, Stefano; Le, Anh; Rodriguez, Marius

doi:10.1016/j.jfineco.2016.04.003

Cited by 161 publications

(38 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This evidence complements that found on other markets; for example, short‐term variance swaps are special contracts, as they mainly reflect the fear of a market crash and promptly react to systemic risk (Dew‐Becher et al. , Li and Zinna ). More fundamentally, this result, combined with the drop in systematic‐sovereign risk that we document around the introduction of the OMT, lends support to the choice of the ECB to contrast the unfolding of the sovereign debt crisis by purchasing short‐term sovereign debt.…”

Section: Discussionsupporting

confidence: 82%

How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe

Zinna

2018

J of Money Credit Banking

View full text Add to dashboard Cite

We examine European banks' exposures to systematic and country‐specific sovereign risk. We organize our investigation around a multifactor affine credit risk model estimated on credit default swap data of different maturities. During the 2008–15 period, about one third of banks' credit risk is sovereign. However, banks strongly differ both in the magnitude and type of their sovereign exposures. Measures of indirect exposures, such as bank size and return on equity, capture these cross‐sectional differences better than measures of direct exposures. Furthermore, the properties of the distress risk premiums turn out to be important to understand the effect of sovereign risk on bank funding costs.

show abstract

Section: Discussionsupporting

confidence: 82%

How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe

Zinna

2018

J of Money Credit Banking

View full text Add to dashboard Cite

show abstract

“…Fortunately, the literature has suggested a possible solution to this puzzle by adding time‐varying volatility of volatility (vol‐of‐vol) to the model (see, e.g., Bollerslev, Tauchen, and Zhou (), Drechsler and Yaron (), Tauchen (), Bollerslev, Xu, and Zhou (), and Dew‐Becker et al. ()). Bollerslev, Xu, and Zhou () consider a slight variation of the long‐run risk factor compared to the baseline model (7) in which the vol‐of‐vol factor

q_{t}

drives the volatility,

\begin{matrix} true \\ right \begin{matrix} σ_{t + 1}^{2} & = & {\overset{σ}{true}}^{2} false(1 - ν false) + ν σ_{t}^{2} + φ_{σ} \sqrt{q_{t}} ω_{σ, t + 1}, \\ q_{t + 1} & = & μ_{q} false(1 - ρ_{q} false) + ρ_{q} q_{t} + φ_{q} \sqrt{q_{t}} ω_{q, t + 1}, \\ x_{t + 1} & = & ρ x_{t} + φ_{x} \sqrt{q_{t}} η_{x, t + 1} \\ \times η_{x, t + 1}, ω_{σ, t + 1}, ω_{q, t + 1} \sim normali . normali . normald . N false(0, 1 false) . \end{matrix} \end{matrix}

The vol‐of‐vol factor

q_{t}

follows a square root process.…”

Section: Higher Order Dynamics In Six Asset Pricing Modelsmentioning

confidence: 99%

Higher Order Effects in Asset Pricing Models with Long‐Run Risks

Pohl¹,

Schmedders²,

Wilms

2018

The Journal of Finance

115

View full text Add to dashboard Cite

This paper shows that the latest generation of asset pricing models with long‐run risk exhibit economically significant nonlinearities, and thus the ubiquitous Campbell‐Shiller log‐linearization can generate large numerical errors. These errors translate in turn to considerable errors in the model predictions, for example, for the magnitude of the equity premium or return predictability. We demonstrate that these nonlinearities arise from the presence of multiple highly persistent processes, which cause the exogenous states to attain values far away from their long‐run means with nonnegligible probability. These extreme values have a significant impact on asset price dynamics.

show abstract

“…Barras and Malkhozov (2016) compare the variance risk premium implied by the crosssection of stock returns with the one computed from option data, and they find that the difference between these measures depends on the financial strength of intermediaries. Dew-Becker, Giglio, Le, and Rodriguez (2017) show that fluctuations in the variance risk premium are difficult to reconcile with consumption-based models, which may be due to segmentation of the option market. Our results contribute to this literature by theoretically and empirically characterizing the role of option market makers in the determination of the market variance risk premium.…”

Section: Related Literaturementioning

confidence: 96%

A Tractable Framework for Option Pricing with Dynamic Market Maker Inventory and Wealth

Fournier¹,

Jacobs²

2019

J. Financ. Quant. Anal.

View full text Add to dashboard Cite

We develop a tractable dynamic model of an index option market maker with limited capital. We solve for the variance risk premium and option prices as a function of the asset dynamics and market maker option holdings and wealth. The market maker absorbs end users’ positive demand and requires a more negative variance risk premium when she incurs losses. We estimate the model using returns, options, and inventory and find that it performs well, especially during the financial crisis. The restrictions imposed by nested existing reduced-form stochastic-volatility models are strongly rejected in favor of the model with a market maker.

show abstract

The price of variance risk

Cited by 161 publications

References 59 publications

How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe

How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe

Higher Order Effects in Asset Pricing Models with Long‐Run Risks

A Tractable Framework for Option Pricing with Dynamic Market Maker Inventory and Wealth

Contact Info

Product

Resources

About