Risk and Volatility: Econometric Models and Financial Practice

By Robert F. Engle. 2004

584 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/b07d8ac2ca725c06f37c376f9a2b3323da7d66f5

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Citations in the corpus

Forecasting: theory and practice (2020, background): “Yet, the right period in which to estimate future volatility has been controversial as volatility based on too long a period will make irrelevant the forecast horizon of our interests, whereas too short a period results in too much noise (Engle, 2004).”
An Introduction to Univariate GARCH Models (2006, methodology): “According to Engle (2004), the original idea was to nd a model that could assess the validity of the conjecture of Friedman (1977) that the unpredictability of in ation was a primary cause of business cycles.”
Frontiers in VaR forecasting and backtesting (2016, methodology): “, Engle (2003).”
Contagion and Banking Crisis - International Evidence for 2007-2009 (2015, background): “The latter captures the argument that financial markets exhibit explosive volatility during crises that may spillover to other markets (Edwards, 1998; Engle, 2004; Hamao et al., 1990).”
Bayesian Inference Methods for Univariate and Multivariate GARCH Models: A Survey (2014, unknown): “As Engle (2004) puts it, “there is now an alphabet soup” of ARCH family models, such as AARCH, APARCH, FIGARCH, STARCH etc, which try to incorporate such return features as fat tails, volatility clustering and volatility asymmetry.”
The Term Structure of Variance Swap Rates and Optimal Variance Swap Investments (2010, background): “stochastic ( Engle (2004) ) and the variance risk is heavily priced (?).”
Forecasting risk with Markov-switching GARCH models:A large-scale performance study (2018, background): “…and its generalization by Bollerslev (1986), andmultiple extensions of the GARCH scedastic function have been proposed for capturing additional stylized facts that are observed in financial markets, such as nonlinearities, asymmetries, and long-memory properties; see Engle (2004) for a review.”
Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns (2010, background): “However, it is now well established that key features of the conditional return distribution, and the conditional variance in particular, are highly predictable (e.g., Engle, 2004).”
Forecasting time-varying covariance with a range-based dynamic conditional correlation model (2009, background): “Tailored to the needs of different asset classes, these various models have achieved remarkable success [see Bollerslev et al. (1992), and Engle (2004) , for a comprehensive review].”
GARCH Models (2010, unknown): “See, among others, Bollerslev, Chou and Kroner (1992), Bollerslev, Engle and Nelson (1994), Pagan (1996), Palm (1996), Shephard (1996), Kim, Shephard, and Chib (1998), Engle (2001, 2002b, 2004), Engle and Patton (2001), Diebold (2004), Bauwens, Laurent and Rombouts (2006) and Giraitis et al. (2006).”
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).”