On the Economic Sources of Stock Market Volatility

By Robert F. Engle, Eric Ghysels, Bumjean Sohn. 2009

183 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/b165ade60144795ff51e8fbd4509db359a5a9212

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. Eric Ghysels
2. Riccardo Colacito
3. Robert F. Engle
4. Jennie Bai
5. Jonathan H. Wright
6. Grigory Vilkov
7. Raman Uppal
8. Yuliya Plyakha

In Finance

1. Eric Ghysels
2. Riccardo Colacito
3. Robert F. Engle
4. Jennie Bai
5. Grigory Vilkov
6. Jonathan H. Wright
7. Raman Uppal
8. Yuliya Plyakha

Citations in the corpus

• Why Does an Equal-Weighted Portfolio Outperform Value- and Price-Weighted Portfolios? (2015, methodology): “Motivated by the findings from the non-parametric monotonicity tests, we use the standard four-factor model (Fama and French (1993) and Carhart (1997)) to decompose the total returns of the equal-, value-, and price-weighted portfolios into a systematic component, which is related to factor exposure”
• A Comprehensive Look at Financial Volatility Prediction by Economic Variables (2011, unknown): “On a more positive note, Engle, Ghysels, and Sohn (2008) analyze the effect of inflation and industrial production growth on stock return volatility, considering each macroeconomic variable separately.”
• Is the Potential for International Diversification Disappearing? A Dynamic Copula Approach (2012, unknown): “24Our variable selection is motivated by the work of Schwert (1989), Engle, Ghysels, and Sohn (2008), Engle and Rangel (2008), Boyer, Mitton, and Vorkink (2010), Ghysels, Plazzi, and Valkanov (2011), Bekaert, Harvey, Lundblad, and Siegel (2012), Carrieri, Chaieb, and Errunza (2011), and Baele,…”
• Deja Vol: Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables (2012, unknown): “The construction of this variable follows Engle et al. (2008) using data sourced from the Federal Reserve Bank of St.”
• State Space Models and MIDAS Regressions (2013, methodology): “1The original work on MIDAS focused on volatility predictions, see e.g. Alper, Fendoglu, and Saltoglu (2008), Chen and Ghysels (2009), Engle, Ghysels, and Sohn (2008), Forsberg and Ghysels (2006), Ghysels, Santa-Clara, and Valkanov (2005), Ghysels, Santa-Clara, and Valkanov (2006), León, Nave, and…”
• Stock market volatility and business cycle: Evidence from linear and nonlinear causality tests (2016, background): “Recent work also establishes a strong link between stock market volatility and macroeconomic fundamentals (see, Engle and Rangel, 2008; Engle et al., 2008; Diebold and Yilmaz, 2010; and Corradi et al., 2013).”
• Stock Market Volatility and the Business Cycle (2010, background): “…market volatility (e.g. Schwert, 1989) finds only weak linkages between stock market volatility and macroeconomic variables, recent empirical research (e.g. Engle et al., 2008; Diebold and Yilmaz, 2010) establishes important linkages between macroeconomic fundamentals and stock market volatility.”
• A Component Model for Dynamic Correlations (2011, methodology): “We follow a procedure suggested in Engle, Ghysels, and Sohn (2006), since the GARCH-MIDAS and DCC-MIDAS class of models share similar model selection issues.”
• The Conditional Autoregressive Wishart Model for Multivariate Stock Market Volatility (2010, unknown): “In the following, we introduce two of such extensions, where we combine the CAW model with the MIDAS (mixed data sampling) approach of Engle et al. (2008) and Colacito et al. (2009), and with an HAR (heterogenous autoregressive) component as used by Corsi (2009) and Bonato et al. (2009).”