Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects

Lamoureux, Christopher G.; Lastrapes, William D.

doi:10.2307/2328817

Cited by 288 publications

(252 citation statements)

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“…Variables that have been shown to help predict volatility are trading volume, macroeconomic news announcements ( [58], [43], [17]), implied volatility from option prices and realized volatility ( [82], [11]), overnight returns ( [46], [68]), and after hours realized volatility ([21])…”

Section: Explanatory Variables In the Conditional Variance Equationmentioning

confidence: 99%

“…Second, they argue that observed IGARCH behavior may result from misspecification of the conditional variance function. For example, a two components structure or ignored structural breaks in the unconditional variance ( [58] and [70]) can result in IGARCH behavior. Table 9 gives Lo's modified R/S statistic (20) applied to r 2 t and |r t | for Microsoft and the S&P 500.…”

Section: Integrated Garch Modelmentioning

confidence: 99%

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Practical Issues in the Analysis of Univariate GARCH Models

Zivot

2009

Handbook of Financial Time Series

124

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Section: Explanatory Variables In the Conditional Variance Equationmentioning

confidence: 99%

Section: Integrated Garch Modelmentioning

confidence: 99%

Practical Issues in the Analysis of Univariate GARCH Models

Zivot

2009

Handbook of Financial Time Series

124

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“…A GARCH process of order 1 and 1, denoted as GARCH(1,1), for the random error term, e t, is specified as: A concern with the volatility generation process as defined is that current volatility is only related to the past values of innovation and volatility spillovers from previous periods. For example, Lamoureux and Lastrapes (1990) Engle and Ng (1993), Foster (1995), Andersen (1996), Andersen andBollerslev (1997, 1998), Wang and Yau (2000) and Rahman et al (2002), amongst others, argue that an appealing explanation for the presence of GARCH effects in financial markets is that the rate and timing of information arrival is the stochastic mixing variable that generates financial market returns. That is, market information, however defined, is strongly correlated with price volatility (Andersen and Bollerslev 1998).…”

Section: Model Specificationmentioning

confidence: 99%

Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects

Higgs¹,

Worthington²

2005

The Energy Journal

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This paper investigates the intraday price volatility process in four Australian wholesale electricity markets; namely New South Wales, Queensland, South Australia and Victoria. The data set consists of half-hourly electricity prices and demand volumes over the period January 1, 2002 to June 1, 2003. A range of processes including GARCH, RiskMetrics, normal Asymmetric Power ARCH or APARCH, Student APARCH and skewed Student APARCH are used to model the time-varying variance in prices and the inclusion of news arrival as proxied by the contemporaneous volume of demand, time-of-day, day-of-week and month-of-year effects as exogenous explanatory variables. The skewed Student APARCH model, which takes account of right skewed and fat tailed characteristics, produces the best results in all four markets. The results indicate significant innovation (ARCH effects) and volatility (GARCH effects) spillovers in the conditional standard deviation equation, even with market and calendar effects included. Intraday prices also exhibit significant asymmetric responses of volatility to the flow of information.

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“…The family of ARCH model, which was introduced by Engle (1982) have proven useful in financial applications and have attracted great attention in economics and statistical literature (Alberola (2006), Gao, Yu, and Chen (2009), Hardle and Hafner (2000), Lamoureux, et al(1990)). Let (X t , Y t ) denote vector of predictor variables and response variable at the time t respectively.…”

Section: Box-cox Transformed Arch Modelmentioning

confidence: 99%

Second Order Least Square Estimation on Arch(1) Model With Box-Cox Transformed Dependent Variable

Utami

Subanar

2014

JIMS

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Abstract. Box-Cox transformation is often used to reduce heterogeneity and to achieve a symmetric distribution of response variable. In this paper, we estimate the parameters of Box-Cox transformed ARCH(1) model using second-order least square method and then we study the consistency and asymptotic normality for second-order least square (SLS) estimators. The SLS estimation was introduced by Wang (2003Wang ( , 2004 to estimate the parameters of nonlinear regression models with independent and identically distributed errors.Key words: Box-Cox transformation, second-order least square, ARCH model.Abstrak. Transformasi Box-Cox sering digunakan untuk mengurangi heterogenitas dan mencapai distribusi simetris dari variabel respon. Pada paper ini dibahas estimasi parameter dari model ARCH(1) di mana variabel responnya ditransformasi Box-Cox dengan menggunakan metode estimasi second-order least square dan selanjutnya diteliti konsistensi dan normalitas asimtotik dari estimator second-order least square. Metode ini pertama kali diperkenalkan oleh Wang (2003Wang ( , 2004) untuk mengestimasi parameter model regresi nonlinier yang variabel errornya berdistribusi identik dan independen.Kata kunci: Transformasi Box-Cox, second-order least square, model ARCH

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Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects

Cited by 288 publications

References 0 publications

Practical Issues in the Analysis of Univariate GARCH Models

Practical Issues in the Analysis of Univariate GARCH Models

Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects

Second Order Least Square Estimation on Arch(1) Model With Box-Cox Transformed Dependent Variable

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