What good is a volatility model?
By Robert F. Engle, Andrew J. Patton. 2001
843 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/15f9a9a09f42febe939e2634adb472f5139265d8
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
- Andrew J. Patton
- Tim Bollerslev
- Asger Lunde
- P. R. Hansen
- Kevin Sheppard
- N. Shephard
- Stavros Degiannakis *
- Torben G. Andersen
In Finance
- Andrew J. Patton
- Tim Bollerslev
- Kevin Sheppard
- Asger Lunde
- P. R. Hansen
- N. Shephard
- Stavros Degiannakis *
- Torben G. Andersen
Citations in the corpus
- Evaluating Value-at-Risk Models via Quantile Regression (2008, background): “Rt = t"t ; t = 1; :::; T; (15) (2)t = (1 ) + R(2) t 1 + (2)t 1; (16)”
- Data-based ranking of realised volatility estimators (2011, background): “…some structure on the time series dynamics of the target variable, t: Numerous papers on the conditional variance (see Bollerslev, et al., 1994, Engle and Patton, 2001, and Andersen, et al., 2005 for example), or integrated variance (see Andersen, et al., 2004 and 2005) have reported that…”
- Measuring and Modelling Variation in the Risk-Return Trade-off (2001, neutral): “Another is that the realized volatility measures using daily returns give a lot of weight to relatively rare, high-volatility periods (Engle and Patton, 2000).”
- New frontiers for arch models (2002, background): “See for example, Bollerslev, Chou and Kroner(1992), Bollerslev Engle and Nelson(1994), Bera and Higgins(1993), and recent pedagogical articles by Engle(2001) and Engle and Patton(2001).”
- Asymptotic Properties of GARCH-X Processes (2015, background): “…2 t1 + 2 t1 + x t1 (or x 2 t1 ); (1) where (y t ) is a demeaned time series and 2 t is its variance conditional on the information available at time t 1: As the covariate (x t ) in (1), Glosten et al. (1993), Brenner et al. (1996), Gray (1996) and Engle and Patton (2001) used interest rate levels.”
- A Multiple Indicators Model for Volatility Using Intra-Daily Data (2003, background): “Let us now consider the volatility for an asset (or an index) at a given maturity T+ k as the square root of the cumulated sum of j-step-ahead forecasts generated by expression (18), when j ¼ 1; or (19), when j is between 2 and k (cf. Engle and Patton, 2001).”
- VOLATILITY AND CORRELATION FORECASTING (2006, background): “…surveyed elsewhere; see, e.g., review articles by Andersen and Bollerslev (1998b), Bollerslev, Chou and Kroner (1992), Bollerslev, Engle and Nelson (1994), Diebold (2004), Diebold and Lopez (1995), Engle (2001, 2004), Engle and Patton (2001), Pagan (1996), Palm (1996), and Shephard (1996).”
- Neglecting parameter changes in GARCH models (2005, background): “prices over 4 years; Engle and Patton (2001) : daily returns on stock index over 12 years.”
- Multivariate Stochastic Volatility via Wishart Processes (2006, positive): “In addition, as Engle and Patton (2001) pointed out, the fact that stochastic volatility models are formulated in terms of latent variables, makes it easy to evaluate unconditional moments but relatively difficult to evaluate conditional moments.”