Deja Vol: Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables

By Bradley S. Paye. 2012

456 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/9c66708be06e99bd5fcb6630239b63c027db2046

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. Yu Wei
2. Elie Bouri
3. Chao Liang
4. Yudong Wang
5. Chongfeng Wu
6. Xiafei Li
7. Yifeng Zhang
8. Xuhui Zhang

In Finance

1. Yu Wei
2. Elie Bouri
3. Yudong Wang
4. T. Walther
5. Tony Klein
6. Chao Liang
7. Xiafei Li
8. Guofu Zhou

Citations in the corpus

• Time series momentum: Is it there? (2020, background): “(3) and (5) with volatility scaling while volatility varies across assets and is predictable by its lagged values (Paye, 2012)? Table 6 reports the t-statistics with real data and boostrapped t-statistics from Eqs.”
• Anticipating Long‐Term Stock Market Volatility (2014, unknown): “…in the predictive regressions relativ to the AR(1) benchmark model.7 The outcome is consistent with the findings in Christiansen et al. (2012) and Paye (2012) and seems to suggest that macro variables do not carry information that is beyond that contained in lagged realized volatilities In the…”
• Oil and the short-term predictability of stock return volatility (2018, methodology): “…the seminal work of Schwert (1989a), economic sources of financial volatility have been investigated extensively (e.g., Asgharian et al. (2013); Choudhry et al. (2016); Christiansen et al. (2012); Diebold and Yilmaz (2008); Engle and Rangel (2008); Engle et al. (2013); Nonejad (2017); Paye (2012)).”
• A Comprehensive Look at Financial Volatility Prediction by Economic Variables (2011, unknown): “This echoes results by Paye (2012) that macro-finance variables are less powerful predictors for equity volatility in recent data.”
• Uncertainty and crude oil market volatility: new evidence (2020, methodology): “According to the studies of Welch and Goyal (2008) and Paye (2012), we construct the monthly realized volatility for all used variables.”
• Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “Because recessions typically coincide with regimes of high volatility, our results are consistent with the finding from the previous literature that macroeconomic variables are particularly useful to predict financial volatility during the onset of recessions (see, e.g., Paye, 2012).”
• Stock market volatility: Identifying major drivers and the nature of their impact (2015, background): “Paye (2012) and, especially, Christiansen et al. (2012) consider extended sets of macroeconomic factors and a broader range of asset classes.”
• Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting (2019, background): “Remarkably, there is a large body of research examining the role of macro-economic and financial variables in predicting the volatility of equities (e.g. Schwert, 1989, Paye, 2012) and other assets such as bonds, foreign exchange, and commodities (e.g. Christiansen et al., 2012).”