Practical Issues in the Analysis of Univariate GARCH Models

Zivot, Eric

doi:10.1007/978-3-540-71297-8_5

Cited by 124 publications

(86 citation statements)

References 84 publications

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“…However, as Engle and Ng (1993) argue, the joint test is more powerful than the individual tests. In the same way, Zivot (2009) argued that the negative value of sample correlation between rt 2 and rt 1  provides some evidence for potential asymmetric effect. As shown in table 3, the correlation between rt 2 and rt 1  is positive indicating strong evidence for the lack of asymmetric effect in the dynamic of Iran stock return series.…”

Section: Data and Empirical Resultsmentioning

confidence: 89%

Asymmetric Effects in Emerging Stock Markets - The Case of Iran Stock Market

Oskooe¹,

Shamsavari²

2011

IJEF

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In view of the mixed empirical results in the literature, this paper assesses the extent of asymmetric volatility effects in the Iranian stock market as an emerging stock market, using a variety of nonlinear autoregressive conditional heteroskedasticity specifications. Tests based on standardized residuals from a fitted GARCH model suggest a lack of asymmetric effects in the dynamic volatility of the Iranian stock market. Empirical analyses with asymmetric GARCH models also reject the hypothesis of asymmetric volatility, in contrast to most developed and emerging markets. Hence, good and bad news of the same magnitude have similar impacts on the volatility level in the Iranian stock market. The leverage effect modified by inflation advantage and price limit may be the main reasons for lack of asymmetric effect in emerging stock markets.

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Section: Data and Empirical Resultsmentioning

confidence: 89%

Asymmetric Effects in Emerging Stock Markets - The Case of Iran Stock Market

Oskooe¹,

Shamsavari²

2011

IJEF

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“…Figure 6 illustrates that volatility clustering is present which is typical in financial markets. This feature hints at autocorrelation in the data, which is emphasised by the Qstatistic for the squared and the absolute returns (Zivot, 2009).…”

Section: Modelmentioning

confidence: 95%

“…In the normal GARCH model, the coefficients in the variance equation, including the additional coefficients for γ, should be positive to ensure that the variance is always positive (Gallo and Pacini, 1998;Zivot, 2009). When a coefficient in the GARCH variance equation is negative, one can inspect the conditional variance and check whether it is always positive.…”

mentioning

confidence: 99%

The impact of wind power generation on the electricity price in Germany

Ketterer¹

2014

Energy Economics

412

250

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This paper provides insight into the relationship between intermittent wind power generation and electricity price behaviour in Germany. Using a GARCH model, the effect of wind electricity in-feed on level and volatility of the electricity price can be evaluated in an integrated approach. The results show that variable wind power reduces the price level but increases its volatility. With a low and volatile wholesale price, the profitability of electricity plants, conventional or renewable, is more uncertain. Consequently, the construction of new plants is at risk, which has major implications for the energy market and the security of supply. These challenges, related to the integration of renewables, require adjustments to the regulatory and the policy framework of the electricity market. This paper's results suggest that regulatory change is able to stabilise the wholesale price. It is found that the electricity price volatility has decreased in Germany after the marketing mechanism of renewable electricity was modified. This gives confidence that further adjustments to regulation and policy may foster a better integration of renewables into the power system. JEL Code: Q42, Q48, C22. Terms of use: Documents in

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“…Bollerslev et al (2006) find a negative relationship between volatility and past and future returns using high-frequency aggregate equity index data and find that high-frequency data may be used to assess volatilities asymmetries of daily return horizon. Zivot (2008) finds asymmetric effect for the S&P500 index return. Ederington and Guan (2010) also find asymmetric effect in the US stock market.…”

Section: Introductionmentioning

confidence: 89%

Asymmetric Volatility of the Thai Stock Market: Evidence from High-Frequency Data

2015

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Abstract:This study employs the daily data of the Stock Exchange of Thailand to test for the leverage and volatility feedback effects. The period of investigation is during January 4, 2005 to December 27, 2013, which includes the Subprime crisis period in the US that might affect the volatility of stock market return in emerging stock markets. The results from this study show that the US subprime crisis imposes a minimal positive impact on volatility. In addition, the estimations of the three parametric asymmetric volatility models give the results showing some evidence of the volatility feedback and leverage effects. The findings give implications for portfolio diversification and risk management.

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Practical Issues in the Analysis of Univariate GARCH Models

Abstract: This paper gives a tour through the empirical analysis of univariate GARCH models for financial time series with stops along the way to discuss various practical issues associated with model specification, estimation, diagnostic evaluation and forecasting.

Cited by 124 publications

References 84 publications

Asymmetric Effects in Emerging Stock Markets - The Case of Iran Stock Market

Asymmetric Effects in Emerging Stock Markets - The Case of Iran Stock Market

The impact of wind power generation on the electricity price in Germany

Asymmetric Volatility of the Thai Stock Market: Evidence from High-Frequency Data

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