### Abstract

Original language | English |
---|---|

Title of host publication | Statistical Tools for Finance and Insurance, Second Edition |

Editors | P. Cizek, W.K. Härdle, R. Weron |

Place of Publication | Heidelberg |

Publisher | Springer Verlag |

Pages | 101-132 |

ISBN (Print) | 9783642180 |

Publication status | Published - 2011 |

### Fingerprint

### Cite this

*Statistical Tools for Finance and Insurance, Second Edition*(pp. 101-132). Heidelberg: Springer Verlag.

}

*Statistical Tools for Finance and Insurance, Second Edition.*Springer Verlag, Heidelberg, pp. 101-132.

**Modelling conditional heteroscedasticity in nonstationary series.** / Cizek, P.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific › peer-review

TY - CHAP

T1 - Modelling conditional heteroscedasticity in nonstationary series

AU - Cizek, P.

PY - 2011

Y1 - 2011

N2 - A vast amount of econometrical and statistical research deals with modeling financial time series and their volatility, which measures the dispersion of a series at a point in time (i.e., conditional variance). Although financial markets have been experiencing many shorter and longer periods of instability or uncertainty in last decades such as Asian crisis (1997), start of the European currency (1999), the “dot-Com” technology-bubble crash (2000–2002) or the terrorist attacks (September, 2001), the war in Iraq (2003) and the current global recession (2008–2009), mostly used econometric models are based on the assumption of stationarity and time homogeneity; in other words, structure and parameters of a model are supposed to be constant over time. This includes linear and nonlinear autoregressive (AR) and moving-average models and conditional heteroscedasticity (CH) models such as ARCH (Engel, 1982) and GARCH (Bollerslev, 1986), stochastic volatility models (Taylor, 1986), as well as their combinations.

AB - A vast amount of econometrical and statistical research deals with modeling financial time series and their volatility, which measures the dispersion of a series at a point in time (i.e., conditional variance). Although financial markets have been experiencing many shorter and longer periods of instability or uncertainty in last decades such as Asian crisis (1997), start of the European currency (1999), the “dot-Com” technology-bubble crash (2000–2002) or the terrorist attacks (September, 2001), the war in Iraq (2003) and the current global recession (2008–2009), mostly used econometric models are based on the assumption of stationarity and time homogeneity; in other words, structure and parameters of a model are supposed to be constant over time. This includes linear and nonlinear autoregressive (AR) and moving-average models and conditional heteroscedasticity (CH) models such as ARCH (Engel, 1982) and GARCH (Bollerslev, 1986), stochastic volatility models (Taylor, 1986), as well as their combinations.

M3 - Chapter

SN - 9783642180

SP - 101

EP - 132

BT - Statistical Tools for Finance and Insurance, Second Edition

A2 - Cizek, P.

A2 - Härdle, W.K.

A2 - Weron, R.

PB - Springer Verlag

CY - Heidelberg

ER -