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Topic: Autoregressive conditional heteroskedasticity


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  ARCH and GARCH Processes
Autoregressive conditional heteroskedastic (ARCH) processes are a form of stochastic process that are widely used in finance and economics for modeling conditional heteroskedasticity and volatility clustering.
heteroskedasticity A condition where a stochastic process has non-constant second moments.
Engle, Robert F. Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation, Econometrica, 50, 987-1008.
www.riskglossary.com /articles/ARCH_GARCH.htm   (498 words)

  
  Autoregressive conditional heteroskedasticity - Wikipedia, the free encyclopedia
In econometrics, an autoregressive conditional heteroskedasticity (ARCH) model considers the variance of the current error term to be a function of the variances of the previous time period's error terms.
If an autoregressive moving average model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH) model.
Generally, when testing for heteroskedasticity in econometric models, the best test is the White test.
en.wikipedia.org /wiki/Autoregressive_conditional_heteroskedasticity   (210 words)

  
 Autoregressive moving average model - Wikipédia
Dina statistik, model autoregressive moving average (ARMA) nyaeta aplikasi tipikal nu dipake dina data deret waktu.
Model autoregressive model ngarupakeun hal penting dina infinite impulse response filter nu mibanda sawangan tambahan dina eta tempat.
Tempo oge model autoregressive conditional heteroskedasticity (ARCH) sarta model autoregressive integrated moving average (ARIMA).
su.wikipedia.org /wiki/Autoregressive_moving_average_model   (231 words)

  
 Autoregressive conditional heteroskedasticity   (Site not responding. Last check: )
Dina econometrics, model autoregressive conditional heteroskedasticity (ARCH) nimbangkeun yen wates salah varian ayeuna jadi fungsi wates salah varian dina waktu samemehna.
Lamun autoregressive moving average model di-asumsikeun keur kasalahan, modelna nyaeta model generalized autoregressive conditional heteroskedasticity (GARCH).
Sacara umum, lamun tes keur heteroskedasticity dina model econometric, tes nu panghadena White test.
su.efactory.pl /Autoregressive_conditional_heteroskedasticity   (123 words)

  
 NPG - Abstract   (Site not responding. Last check: )
the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China.
It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account.
Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals.
www.copernicus.org /site/EGU/npg/12/1/55.htm?FrameEngine=false   (304 words)

  
 Citations: Autoregressive conditional heteroskedasticity and changes in regime - Hamilton, Susmel (ResearchIndex)   (Site not responding. Last check: )
Hamilton, J. and R. Susmel (1994), "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics 64, 307-333.
proposed an approach to model the conditional variances within Markov switching framework, where they combined the regime switching process with an autoregressive conditional heteroskedasticity (ARCH) model by allowing the parameters of the ARCH process to come from different regimes.
Hamilton, J. and Susmel, R. Autoregressive conditional heteroskedasticity and changes in regime, Journal of Econometrics 64: 307--333.
citeseer.ist.psu.edu /context/483830/0   (3432 words)

  
 Homoskedasticity and Heteroskedasticity
Heteroskedasticity is an important concept in finance because asset returns in the capital, commodity and energy markets often exhibit heteroskedasticity.
For example, stock or bond returns tend to be conditionally heteroskedastic.
If a process is not unconditionally heteroskedastic or not conditionally heteroskedastic, it is said to be unconditionally homoskedastic or conditionally homoskedastic, respectively.
www.riskglossary.com /articles/heteroskedasticity.htm   (326 words)

  
 Latest TSP Examples
Robust (resistant to outliers or heteroskedasticity) and non-parametric (31)
Condition number of X'X or any @VCOV matrix (version which is independent of the scale of X).
The condition number is a measure of multicollinearity.
www.stanford.edu /~clint/tspex   (4831 words)

  
 Journal of Business & Economic Statistics: Dynamic conditional correlation: a simple class of multivariate generalized ...   (Site not responding. Last check: )
Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models.(Statistical Data Included)
Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data.
A new class of multivariate models called dynamic conditional correlation models is proposed.
www.highbeam.com /library/doc0.asp?DOCID=1G1:88575148&refid=holomed_1   (212 words)

  
 Bibliography   (Site not responding. Last check: )
Bollerslev, T. `Generalized autoregressive conditional heteroskedasticity', Journal of Econometrics 31(3), 307-327.
Engle, R. `Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom inflation', Econometrica 50(4), 987-1007.
Nelson, D. `Conditional heteroskedasticity in asset returns: A new approach', Econometrica 59(2), 347-370.
mywebpages.comcast.net /ylding/returns/html/node8.html   (745 words)

  
 RePEc
The proposed class encompasses several functional forms for autoregressive conditional heteroskedasticity which have been put forth in the literature.
A Lagrange multiplier test is developed to test Engle's autoregressive conditional heteroskedasticity specification against the wider class of models.
The theory is applied to a number of weekly exchange rate series and the authors find strong evidence of nonlinear autoregressive conditional heteroskedasticity.
inomics.com /cgi/repec?handle=RePEc:ier:iecrev:v:33:y:1992:i:1:p:137-58   (163 words)

  
 [No title]   (Site not responding. Last check: )
.- help for ^archlm^ (STB-55: sg135).- Perform a LM test for autoregressive conditional heteroskedasticity (ARCH) -------------------------------------------------------------------------- ^archlm^ [^if^ exp] [^in^ range] [, ^l^ags^(^numlist^) nos^ample] ^archlm^ is for use after ^regress^; see help @regress@.
Description ----------- ^archlm^ computes a Lagrange multiplier test for autoregressive conditional heteroskedasticity (ARCH) effects in a regression residual series for a specified number of lags p, as proposed by Engle (1982).
^nosample^ inidicates that the test be performed on either all observations included in ^archlm^'s ^if^ and ^in^ conditions if specified.
www.stata.com /stb/stb55/sg135/archlm.hlp   (271 words)

  
 ARCH Models   (Site not responding. Last check: )
ARCH (autoregressive conditional heteroskedasticity) models recognize the presence of successive periods of relative volatility and stability.
Bollerslev [1986] proposed the GARCH (generalized ARCH) conditional variance specification that allows for a parsimonious parameterisation of the lag structure.
Engle, R.F., "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation", Econometrica, Vol.
shazam.econ.ubc.ca /intro/garch.htm   (296 words)

  
 Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations
The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to an endogenous or exogenous transition variable.
An LM test is derived to test the constancy of correlations and LM and Wald tests to test the hypothesis of partially constant correlations.
"Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol.
ideas.repec.org /p/hhs/hastef/0577.html   (1145 words)

  
 FT.com / Business education / NYU Stern School of Business
Robert Engle (1982) “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation,” Econometrica, 50, pp987-1008
Riccardo Colacito and Robert Engle (2008) “Term structure of risk, the role of Known and Unknown Risks and Non-stationary Distributions” forthcoming in the book entitled “The Known, the Unknown and the Unknowable in Financial Risk Management”, edited by Francis X. Diebold
Robert Engle, professor of Finance at NYU Stern School of Business, was awarded the 2003 Nobel Prize in Economics for his research on the concept of autoregressive conditional heteroskedasticity (ARCH).
www.ft.com /businesseducation/stern   (556 words)

  
 Atlantic Economic Journal: The economics of conditional heteroskedasticity: evidence from Canadian and U.S. stock and ...   (Site not responding. Last check: )
The economics of conditional heteroskedasticity: evidence from Canadian and U.S. stock and futures markets.
Atlantic Economic Journal; 12/1/1997; Racine, Marie D. This paper provides insight into the sources of time variation and persistence in volatility by presenting new evidence concerning the price behavior of three index futures contracts and associated stock price indexes (the New York Stock Exchange Composite index, Standard and Poor's 500 index, and Toronto 35 index).
Although persistence in the second moments of stock returns distribution is widely documented, the economic explanation for generalized autoregressive conditional heteroskedasticity is not established.
highbeam.com /library/doc0.asp?docid=1G1:20600013&...   (197 words)

  
 S-WoPEc: Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition ...
Abstract: In this paper we propose a multivariate GARCH model with a time-varying conditional correlation structure.
The new Double Smooth Transition Conditional Correlation GARCH model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations.
The model is applied to a selection of world stock indices, and it is found that time is an important factor affecting correlations between them.
swopec.hhs.se /hastef/abs/hastef0652.htm   (277 words)

  
 G@RCH - an Ox Package for Estimating and Forecasting ARCH Models   (Site not responding. Last check: )
Bollerslev, T. (1986): “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 307—327.
Engle, R. (1982): “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50, 987—1007.
Nelson, D. (1991): “Conditional Heteroskedasticity in Asset Returns: a New Approach,” Econometrica, 59, 349—370.
www.core.ucl.ac.be:16080 /~laurent/G@RCH/site/garchbibli.html   (315 words)

  
 EconPapers: Autoregressive Conditional Heteroskedasticity and Changes in Regime
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Journal Article: Autoregressive conditional heteroskedasticity and changes in regime (1994)
This item may be available elsewhere in EconPapers: Search for items with the same title.
econpapers.repec.org /paper/cdlucsdec/93-28.htm   (136 words)

  
 Filippa Margiora   (Site not responding. Last check: )
Autoregressive Conditional Heteroskedasticity Models and the Dynamic Structure of the Athens Stock Exchange
ARCH (Autoregressive Conditional Heteroskedasticity) models have been applied in modelling the relation between conditional variance and asset risk premium The most important theoretical regularities that govern the dynamic structure of financial time series are presented.
The model fits well in Greek Stock Market, from 31 July 1987 to 30 July 1999, and provides empirical evidence on theoretical regularities.
stat-athens.aueb.gr /abstracts/Margioraen.html   (85 words)

  
 [No title]   (Site not responding. Last check: )
BOLLERSLEV, T. ‘A Conditional Heteroskedastic Time Series Model for Speculative Prices and Rates of Return’.
R.F. ‘Autoregressive Conditional Heteroskedasticity with Estimates of Variance of U.K. Inflation’.
‘Conditional Heteroskedasticity in Asset Returns: A New Approach’.
www.revista-eea.net /RePec/lrk/eeaart/Ref_11_1_8.txt   (282 words)

  
 S-WoPEc: Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations
S-WoPEc: Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations
Abstract: In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure.
The model is estimated for the full five-dimensional system as well as several subsystems and the results discussed in detail.
swopec.hhs.se /hastef/abs/hastef0577.htm   (277 words)

  
 Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices
Novel periodic extensions of dynamic long memory regression models with autoregressive conditional heteroskedastic errors are considered for the analysis of daily electricity spot prices.
The parameters of the model with mean and variance specifications are estimated simultaneously by the method of approximate maximum likelihood.
Apart from persistence, heteroskedasticity and extreme observations in prices, a novel empirical finding is the importance of day-of-the-week periodicity in the autocovariance function of electricity spot prices.
ideas.repec.org /p/dgr/uvatin/20050091.html   (850 words)

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