Semiparametric ARCH models

By Stephen Gray, V. Ragunathan. 2001

209 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/034709446687ce6784ca0dd072ee952907f11845

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Citations in the corpus

Extreme Value Theory and Value at Risk (2003, background): “It is important to mention that, even if Zt is not truly normally distributed, the maximization of (17) still provides consistent and asymptotically normal estimates (see, for example, Engle and Gonzalez-Rivera, 1991).”
Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check (2006, methodology): “, the semi-parametric model of Engle and González-Rivera (1991), the historically simulated density and the nonparametric density. When {zt} is not IID, its time-varying conditional distribution Gt(·) may be modelled parametrically (e.g., Hansen, 1994; Harvey and Siddique, 1999, 2000), nonparametric”
Robust Value at Risk Prediction (2007, unknown): “We compare our method to several alternative VaR prediction procedures, such as (i) historical simulation, (ii) RiskMetrics, (iii) semiparametric GARCH model of Engle and Gonzalez-Rivera (1991), (iv) CAViaR model of Engle and Manganelli (2004), and for 10-day ahead VaR predictions, (v) GARCH model a”
Autoregressive Conditional Duration Models in Finance: A Survey of the Theoretical and Empirical Literature (2008, methodology): “As an alternative to specifying a parametric distribution of ε, Engle (2000) and Engle and Russell (1998) use a semiparametric density estimation technique similar to the one used by Engle and Gonzalez-Rivera (1991) in the ARCH context.”
A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors (2001, background): “Some important results for nonstationary AR models can be found in Fuller (1976), Dickey and Fuller (1979), Phillips (1987), Chan and Wei (1987, 1988), Tsay and Tiao (1990) and Jeganathan (1995), among many others.”
Entropy Densities: With an Application to Autoregressive Conditional Skewness and Kurtosis (2002, methodology): “…applications of this model, the unconditional distribution is assumed to allow for some form of fat-tailedness, modeled for instance as a Student-t (Bollerslev, 1987), a generalized error distribution (Nelson, 1991), or as a fully nonparametric density (e.g. Engle and Gonzales-Rivera, 1991).”
Regime Shifts, Risk Premiums in the Term Structure, and the Business Cycle (2004, unknown): “This “semiparametric ARCH” specification is similar to that proposed by Engle and González-Rivera (1991) and allows for skewness and kurtosis in the error distribution.”
Mispricing of S&P 500 Index Options (2006, methodology): “11 In the first way, we apply the semi-parametric GARCH (1,1) method of Engle and Gonzalez-Rivera (1991), a method that does not impose the restriction that conditional returns are normally distributed, as explained in Appendix C.”
Central Bank intervention and foreign exchange rates: new evidence from FIGARCH estimations (2002, background): “Furthermore, some authors including Engle and Gonzalez-Rivera (1991) show that, in general, these estimates are inefficient, with the degree of inefficiency increasing with the magnitude of the departure of the conditional distribution from normality.”
Quasi-Maximum Likelihood Estimation of GARCH Models With Heavy-Tailed Likelihoods (2014, unknown): “Engle and Gonzalez-Rivera (1991) have suggested a semiparametric procedure that can improve the efficiency of the parameter estimates up to 50% over the GQMLE based on their Monte Carlo simulations, but this is still incapable of capturing the total potential gain in efficiency, as shown by Linton (”
An algorithm for nonparametric GARCH modelling (2002, methodology): “Engle and GonzV alez-Rivera (1991) address this problem with an alternative semiparametric approach using ARCH dynamics together with a general nonparametric form for the innovation distribution. In summary, the proposed algorithm for nonparametric GARCH is an attractive ;exible 1tting method that r”
Value at Risk Models in Finance (2001, unknown): “Engle and Gonzalez-Rivera (1991), for example, propose a semiparametric estimation of GARCH models.”
Multivariate GARCH models (2008, unknown): “This model may be seen as a multivariate extension of the semiparametric GARCH model by Engle and González-Rivera (1991).”
Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models (2007, methodology): “All of the chapters in the book edited by Barnett, Powell and Tauchen (1991) and several chapters2 in the Handbook of Econometrics Volume 4 edited by Engle and McFadden (1994) have already reviewed the work in semiparametric and nonparametric econometrics that has been conducted up to the mid-1990s.”
Practical Issues in the Analysis of Univariate GARCH Models (2009, methodology): “[35] and [16] evaluated the accuracy of the quasi-maximum likelihood estimation of GARCH(1,1) models.”
Evaluating Covariance Matrix Forecasts in a Value-at-Risk Framework (2000, unknown): “…by fmt+k, itst k t t kx | ~ f .+ +Ω 15 The intuition for specifying a VaR model as a separate portfolio variance (whether based on a modeled covariance matrix or portfolio variance) and a distributional assumption arises from the two-step procedure proposed by Engle and Gonzalez-Rivera (1991).”