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Journal of Statistical Planning and Inference, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Horváth, Lajos, Liese, Friedrich
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Horváth, Lajos, Liese, Friedrich
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Robust modelling of ARCH models
Journal of Forecasting, 2001The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used in modelling changing variances in financial time series. Since the asset return distributions frequently display tails heavier than normal distributions, it is worth while studying robust ARCH modelling without a specific distribution assumption.
Jiancheng Jiang +2 more
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ARCH Models as Diffusion Approximations
Journal of Econometrics, 1990Abstract This paper investigates the convergence of stochastic difference equations (e.g. ARCH) to stochastic differential equations as the length of the discrete time intervals between observations goes to zero. These results are applied to the GARCH(l,1) model of Bollerslev (1986) and to the AR(l) Exponential ARCH model of Nelson (1989)
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Bayesian Semi-Nonparametric Arch Models
The Review of Economics and Statistics, 1994A Bayesian seminonparametric approach to ARCH models is developed with the advantage that small sample results are obtained even when the likelihood function is subject to nonlinear inequality constraints (as in the ARCH models used in this paper). The seminonparametric nature of the approach allows for the relaxation of the assumption of normal errors.
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Journal of Time Series Analysis, 1984
Abstract.This paper considers the class of ARMA models with ARCH errors. Maximum Likelihood and Least Squares estimates of the parameters of the model and their covariance matrices are noted and incorporated into techniques for model building based upon the application of the usual Box‐Jenkins methodology of identification, estimation and diagnostic ...
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Abstract.This paper considers the class of ARMA models with ARCH errors. Maximum Likelihood and Least Squares estimates of the parameters of the model and their covariance matrices are noted and incorporated into techniques for model building based upon the application of the usual Box‐Jenkins methodology of identification, estimation and diagnostic ...
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Innovations: Technology and Techniques in Cardiothoracic and Vascular Surgery, 2017
Objective Numerous surgical approaches regarding aortic arch advancement for neonatal arch hypoplasia have been described. These repairs can be classified into two categories: those that incorporate a patch and those that do not. The decision between repairs remains largely experiential, rather than empirical, because of the limited number of reported ...
Joseph R, Nellis +6 more
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Objective Numerous surgical approaches regarding aortic arch advancement for neonatal arch hypoplasia have been described. These repairs can be classified into two categories: those that incorporate a patch and those that do not. The decision between repairs remains largely experiential, rather than empirical, because of the limited number of reported ...
Joseph R, Nellis +6 more
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1997
In chapter 3, we studied univariate processes \( \in = \left( {{ \in _t}} \right) \) satisfying GARCH (p, q) representations. The conditional expectations and variances were defined by $$ \left\{ {\begin{array}{*{20}{c}} {E\left( {{\varepsilon _t}/{\varepsilon _{t - 1}}} \right) = 0,} \\ {V\left( {{\varepsilon _t}/{\varepsilon _{t - 1}}} \right) = {
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In chapter 3, we studied univariate processes \( \in = \left( {{ \in _t}} \right) \) satisfying GARCH (p, q) representations. The conditional expectations and variances were defined by $$ \left\{ {\begin{array}{*{20}{c}} {E\left( {{\varepsilon _t}/{\varepsilon _{t - 1}}} \right) = 0,} \\ {V\left( {{\varepsilon _t}/{\varepsilon _{t - 1}}} \right) = {
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1997
The aim of this chapter is to describe the major specifications with conditional heteroscedasticity found in the literature. We first present an autoregressive model of order one with heteroscedastic errors. This simple example allows us to study in detail the existence conditions of the process and to discuss its main properties.
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The aim of this chapter is to describe the major specifications with conditional heteroscedasticity found in the literature. We first present an autoregressive model of order one with heteroscedastic errors. This simple example allows us to study in detail the existence conditions of the process and to discuss its main properties.
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1994
Abstract This chapter evaluates the most important theoretical developments in ARCH type modeling of time-varying conditional variances. The coverage include the specification of univariate parametric ARCH models, general inference procedures, conditions for stationarity and ergodicity, continuous time methods, aggregation and forecasting of ARCH ...
Tim Bollerslev +2 more
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Abstract This chapter evaluates the most important theoretical developments in ARCH type modeling of time-varying conditional variances. The coverage include the specification of univariate parametric ARCH models, general inference procedures, conditions for stationarity and ergodicity, continuous time methods, aggregation and forecasting of ARCH ...
Tim Bollerslev +2 more
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Option Pricing in ARCH‐type Models
Mathematical Finance, 1998ARCH models have become popular for modeling financial time series. They seem, at first, however, to be incompatible with the option pricing approach of Black, Scholes, Merton et al., because they are discrete‐time models and possess too much variability.
Kallsen, Jan, Taqqu, Murad S.
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