Conditional Heteroskedasticity
Study About the Minimum Value at Risk of Stock Index Futures Hedging Applying Exponentially Weighted Moving Average - Generalized Autoregressive Conditional Heteroskedasticity Model
... What investors often wish to insure is that the maximum possible loss of their portfolios falling below a certain value. Namely, the maximum possible loss that a portfolio will lose under normal market fluctuations, with ...
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Estimation of Volatility and Correlation with Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models: An Application to Moroccan Stock Markets
... autoregressive conditional heteroskedasticity (GARCH) models were developed for this purpose and have known a great ...dynamic conditional correlation (DCC) and Baba, Engle, Kraft and Kroner (BEKK) ...
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FORECASTING GOLD PRICES IN SRI LANKA USING GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY APPROACH
... Various statistical models to forecast gold prices are available in the literatures such as Autoregressive Integrated Moving Average (ARIMA) (Pitigalaarachchi et al., 2016, Davis et al., 2014, Ali Khan, 2013), ...
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Nonlinear Adjustment in US Bond Yields: an Empirical Analysis with Conditional Heteroskedasticity
... Our models for US bonds approximate a nonlinear adjustment mecha- nism via a simple variable addition to an otherwise ordinary VAR model. Moreover, incorporating conditional heteroskedasticity can be done ...
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Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes
... called conditional heteroskedasticity, and can be modeled by ARCH-type models, including the ARCH model proposed by Engle (1982) and the later extension GARCH (general- ized ARCH) model proposed by ...
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Conditional heteroskedasticity in crypto asset returns
... Autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH), market volatility, nonlinear time series, Khmaladze ...
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Functional generalized autoregressive conditional heteroskedasticity
... the conditional variance depends on the whole (intra-day) path of the previous obser- ...the conditional volatility function of the present observation is given as a functional linear combination of the ...
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Estimating value at risk for sukuk market using generalized autoregressive conditional heteroskedasticity models
... 4.3 In- sample evaluation and parameters estimates of all GARCH models for Malaysian Sukuk return series, using the entire dataset and assuming three different distribut[r] ...
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Autoregressive Conditional Heteroskedasticity Models and the Dynamic Structure of the Athens Stock Exchange
... on conditional variance, c) fat tails in the conditional distribution of returns d) the contribution of non-trading days to volatility e) the inverse relation between volatility and serial correlation and ...
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Non Linear Moving Average Conditional Heteroskedasticity
... N LM AC H is the repli ation of fat tails; the estimation results indi ate however that this pro ess is preferred to ARCH models using a student-t as onditional distribution only in one [r] ...
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Modelling asymmetric conditional heteroskedasticity in financial asset returns: an extension of Nelson’s EGARCH model
... the conditional density, failure to capture leverage effect when the parameters are of the same signs, assuming independence of the innovations, lack of asymptotic theory for its estimators et ...
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When A Factor Is Measured with Error: The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Factor Models
... the conditional mean or, equivalently, through the assumed existence of valid ...the conditional heteroskedasticty (CH) a¤ecting the structural errors to the triangular system allows for identi…cation in ...
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Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review
... In this paper, a number of univariate and multivariate ARCH models are presented and their estimation is discussed. The main features of what seem to be most widely used ARCH models are described with emphasis on their ...
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On the Performance of Garch Family Models in the Presence of Additive Outliers
... autoregressive conditional heteroskedasticity (ARCH) model introduced by Fredrick Engel in 1982 is the first model that assumed that volatility is not ...
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Consistent Pseudo Maximum Likelihood Estimators and Groups of Transformations
... a conditional expectation, a conditional median and/or a conditional variance [see Gouriéroux et ...with conditional heteroskedasticity, Cholesky ARCH model, and model with homogenous ...
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A Note on the Oil Price Trend and GARCH Shocks
... This paper investigates the trend in the monthly real price of oil between 1990 and 2008 with a generalized autoregressive conditional heteroskedasticity (GARCH) model. Trend and volatility are estimated ...
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Real Time Monitoring of Carbon Monoxide Using Value at Risk Measure and Control Charting
... Keywords: Air Quality Surveillance, Atmospheric Pollution, Autoregressive Conditional Heteroskedasticity modelling, Control Charts, Diag-aVECH, Multivariate Statistical Process Monitorin[r] ...
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Rolling sampled parameters of ARCH and Levy stable models
... autoregressive conditional heteroskedasticity (ARCH) model and a Levy-stable distribution are applied to some well-known financial indices (DAX30, FTSE20, FTSE100 and SP500), using a rolling sample of ...
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Testing for Non Fundamentalness
... the conditional heteroskedasticity of unknown form, does not need information outside of the specified model and could be accomplished with a standard ...
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Structural VAR analysis of monetary transmission mechanism and central bank’s response to equity volatility shock in Taiwan
... Autoregressive Conditional Heteroskedasticity (GARCH) volatility of TWSE on Taiwan’s daily exchange rate, overnight interbank loan rate, and Taiwan Government Bond Index ...
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