The variance risk premium and fundamental uncertainty

Conrad, Christian A.; Loch, Karin

doi:10.1016/j.econlet.2015.04.006

Cited by 25 publications

(14 citation statements)

References 9 publications

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“…The actual volatility forecasts, however, invariable depends on the model used for constructing the forecasts. Bekaert and Hoerova (2014) and Conrad and Loch (2015) have both recently demonstrated how the use of different realized volatility based forecasting models, including versions of the HAR, HAR-J and CHAR models analyzed here, can materially affect the estimates of the volatility risk premium and the interpretation thereof. The HARQ models, of course, hold the promise of even more accurate forecasts and better volatility risk premium estimates, and in turn new insights and a deeper understanding of the economic mechanisms behind the temporal variation in the premium.…”

Section: Resultsmentioning

confidence: 99%

Exploiting the errors: A simple approach for improved volatility forecasting

Bollerslev

Patton

Quaedvlieg

2016

Journal of Econometrics

347

224

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We propose a new family of easy-to-implement realized volatility based forecasting models.The models exploit the asymptotic theory for high-frequency realized volatility estimation to improve the accuracy of the forecasts. By allowing the parameters of the models to vary explicitly with the (estimated) degree of measurement error, the models exhibit stronger persistence, and in turn generate more responsive forecasts, when the measurement error is relatively low. Implementing the new class of models for the S&P500 equity index and the individual constituents of the Dow Jones Industrial Average, we document significant improvements in the accuracy of the resulting forecasts compared to the forecasts from some of the most popular existing models that implicitly ignore the temporal variation in the magnitude of the realized volatility measurement errors.

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Section: Resultsmentioning

confidence: 99%

Exploiting the errors: A simple approach for improved volatility forecasting

Bollerslev

Patton

Quaedvlieg

2016

Journal of Econometrics

347

224

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“…In an extended model presented by Feunou et al (2017), decompositions of the

V R P

are theoretically motivated and asymmetry in the upside and downside vol‐of‐vol factors is shown to drive the dynamics in the components of

V R P

. In the work of Conrad and Loch (2015), a long‐term volatility component from the GARCH‐MIDAS model is found to represent the vol‐of‐vol factor, and the new

V R P

measure based on that component displays higher predictive power for future market returns. In light of these findings, we propose to treat the common long‐memory component of variances that are fractionally cointegrated as the factor intimately associated with the vol‐of‐vol that determines the variance premium.…”

Section: Resultsmentioning

confidence: 99%

“…We follow Conrad and Loch (2015) by exploiting the model in Bollerslev et al (2012) where the

V R P

is written as an affine function of the vol‐of‐vol factor as follows:

\begin{matrix} true0.33em \\ right I V_{t} & center = & left c + R V_{t} + b^{(L M)} L M_{t} \\ right alternatively, I V_{t} - R V_{t} & center = & left c + b^{(L M)} L M_{t} \end{matrix}

where

b^{(L M)} > 0

. Given that we construct the

V R P

and its components under the assumption that the

R V_{t}

follows a martingale difference, we first regress

I V_{t}

against a constant,

R V_{t - 1}

and

L M_{t}

, and then regress the ex‐post

V R P

measured by

(I V_{t} - R V_{t})

against a constant,

R V_{t - 1}

and

L M_{t}

.…”

Section: Resultsmentioning

confidence: 99%

“…Recent literature has achieved limited progress in seeking an appropriate proxy of the vol‐of‐vol factor which is necessary to explain the return predictability suggested by the

V R P

. A notable exception is the work of Conrad and Loch (2015), where the vol‐of‐vol is represented by the long‐term volatility component of the GARCH‐MIDAS model.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The GARCH-MIDAS model is widely used in research of the financial markets. Asgharian et al (2013) and Conrad and Loch (2015) use the GARCH-MIDAS model to explore the relationship between the macroeconomic fundamentals and U.S. stock market volatility. Mo et al (2018), Fang et al (2018), and Asgharian et al (2013) use the GARCH-MIDAS model to explore the impact of macroeconomic fundamentals on the emerging commodities futures market.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Macroeconomic News on Chinese Futures

Liu

Yang

Ruan

2019

IJFS

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Inspired by the GARCH-MIDAS model, we revisit the relationship between Chinese futures and macroeconomic factors. We introduce the level of the macroeconomic variables into the GARCH-MIDAS model in order to test the impact of the macroeconomic level on the variance of futures’ return volatility. Based on the empirical results, we find the level of macroeconomic variables has a significant impact on the volatility of Chinese futures’ return. The influence of the macroeconomic level factor on the futures’ return volatility is statistically significant.

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The variance risk premium and fundamental uncertainty

Cited by 25 publications

References 9 publications

Exploiting the errors: A simple approach for improved volatility forecasting

Exploiting the errors: A simple approach for improved volatility forecasting

Return predictability of variance differences: A fractionally cointegrated approach

The Impact of Macroeconomic News on Chinese Futures

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