The VIX, the Variance Premium and Stock Market Volatility

By Geert Bekaert, Marie Hoerova. Social Science Research Network, 2013

750 citations recorded by the source corpus.

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/9dbd8362852e3e64825dc32922e0f127e9045361

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Andrew J. Patton
2. Tim Bollerslev
3. Feng Ma
4. Yu Wei
5. M. Wahab
6. Rangan Gupta
7. Geert Bekaert
8. V. Todorov

In Finance

1. Andrew J. Patton
2. Tim Bollerslev
3. Feng Ma
4. M. Wahab
5. V. Todorov
6. Yu Wei
7. Geert Bekaert
8. Lai Xu

Citations in the corpus

• Quadratic Variance Swap Models (2016, positive): “, Jiang and Tian (2005), Carr and Wu (2009), Bollerslev, Tauchen, and Zhou (2009), Todorov (2010), Bollerslev and Todorov (2011), Drechsler and Yaron (2011), Mueller, Vedolin, and Yen (2011), Martin (2013), and Bekaert and Hoerova (2014).”
• Quantile Connectedness: Modelling Tail Behaviour in the Topology of Financial Networks (2018, positive): “We forecast the realised variance using the same augmented version of Corsi’s (2009) heterogeneous autoregressive model used by Bekaert and Hoerova (2014).”
• Risk, Uncertainty and Monetary Policy (2013, positive): “Fed chairman Bernanke (see Bernanke (2002)) interprets his work on the effect of monetary policy on the stock market (Bernanke and Kuttner (2005)) as suggesting that monetary policy would not have a sufficiently strong effect on asset markets to pop a “bubble” (see also Bernanke and Gertler (2001), ”
• Forecasting the realized range-based volatility using dynamic model averaging approach (2017, methodology): “This model is popularly employed in forecasting volatility and shows its outstanding performance in capturing "stylized facts" in financial market, such as long memory and multi-scale behavior of volatility (Andersen et al., 2007; Corsi et al., 2010; Bekaert and Hoerova, 2014; Duong and Swanson, 201”
• Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, methodology): “Studies on forecasting volatility using high-frequency data have mainly concentrated on the stock and exchange rate markets (e.g., Andersen et al., 2007a; Corsi et al., 2010; Bekaert and Hoerova, 2014; Bollersleva et al., 2015; Degiannakis, 2008; Duong and Swanson, 2015; Wang et al., 2016).”
• Volatility jumps: The role of geopolitical risks (2018, methodology): “Following, Bekaert and Hoerova (2014) we employ the threshold bipower variation ( ) as a jump-free volatility estimator as defined in Corsi et al.”
• Resolution of Policy Uncertainty and Sudden Declines in Volatility (2017, neutral): “In this regard, Bekaert and Hoerova (2014) evaluate a plethora of state-of-the-art forecasting models to produce an accurate measure of the conditional variance, and point out that a non-linear model may be better equipped to capture the behavior of conditional variance and variance risk premia in s”
• Tail risk premia and return predictability (2014, background): “…(2009) include Drechsler and Yaron (2011), Han and Zhou (2011), Du and Kapadia (2012), Eraker and Wang (2015), Almeida, Vicente, and Guillen (2013), Bekaert and Hoerova (2014), Bali and Zhou (2015), Camponovo, Scaillet, and Trojani (2013), Kelly and Jiang (2014), Li and Zinna (2014), Vilkov and…”
• Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection (2020, methodology): “Bekaert and Hoerova (2014) indicate that VIX has a high predictive power for financial instability.”