Modelling and estimating volatility with arch familymodels: An application on financial time series
Başlık çevirisi mevcut değil.
- Tez No: 645346
- Danışmanlar: PROF. DR. ROBERT M. KUNST
- Tez Türü: Yüksek Lisans
- Konular: Ekonomi, Maliye, Economics, Finance
- Anahtar Kelimeler: BIST, Time Series, Volatility, Conditional Mean, Conditional Variance, Conditional Heteroscedasticity, Volatility Clustering, Leverage Effect, ISE Indexes, ARMA, ARIMA, ARCH, GARCH, EGARCH, TGARCH, PARCH, APARCH
- Yıl: 2017
- Dil: İngilizce
- Üniversite: Universität Wien
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 102
Özet
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Özet (Çeviri)
Nowadays, the assumption of constant variance, which was valuable in traditional econometric methods, has lost its validity in modeling and predicting of financial time series. Because it is known that financial time series exhibit a wide volatility. It is seen that the variance and covariance of error terms of the observation change over time, depending on erratic movements in financial time series. Thus, instead of using linear time series in the modelling of variance and covariance, the use of nonlinear conditionally varying variance models becomes more common. The greatest advantage of ARCH models is that they have modeling power, which is seen in almost all of the time series, inter-period dependence and non-stationary variance, that is, volatility, without requiring any additional data other than the past values of the series. In this study, the concepts of risk, uncertainty, volatility and return, which increasingly become important for financial markets, and volatility clustering frequently encountered in financial statements, and asymmetric price movements, leverage effect and thick tail features are being investigated. The theoretical structure of ARMA and ARCH family models have also been introduced. Particularly, the capacities of these models for estimating and predicting the volatility of the BIST (Istanbul Stock Exchange) index have been examined. The model, which showed, had the best modeling performance, has also been tested for static and dynamic estimation performances for specified date ranges.
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