Niels Haldrup, Mika Meitz, and Pentti Saikkonen (eds)
- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199679959
- eISBN:
- 9780191760136
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199679959.001.0001
- Subject:
- Economics and Finance, Econometrics
This book is a collection of 14 original research articles presented at the conference Nonlinear Time Series Econometrics that was held in Ebeltoft, Denmark, in June 2012. The conference gathered ...
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This book is a collection of 14 original research articles presented at the conference Nonlinear Time Series Econometrics that was held in Ebeltoft, Denmark, in June 2012. The conference gathered several eminent time series econometricians to celebrate the work and outstanding career of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The book is divided into four broad themes that all reflect Timo Teräsvirta’s work and methodology: testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent the state of the art in econometrics, such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had, and will continue to have, on the profession.Less
This book is a collection of 14 original research articles presented at the conference Nonlinear Time Series Econometrics that was held in Ebeltoft, Denmark, in June 2012. The conference gathered several eminent time series econometricians to celebrate the work and outstanding career of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The book is divided into four broad themes that all reflect Timo Teräsvirta’s work and methodology: testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent the state of the art in econometrics, such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had, and will continue to have, on the profession.
Heather M. Anderson and Farshid Vahid
- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199679959
- eISBN:
- 9780191760136
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199679959.003.0004
- Subject:
- Economics and Finance, Econometrics
Decreases in stock market returns often lead to higher increases in volatility than increases in returns of the same magnitude, and it is common to incorporate these so-called leverage effects in ...
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Decreases in stock market returns often lead to higher increases in volatility than increases in returns of the same magnitude, and it is common to incorporate these so-called leverage effects in GARCH and stochastic volatility models. Recent research has also found it useful to account for leverage in models of realized volatility, as well as in models of the continuous and jump components of realized volatility. This chapter explores the use of smooth transition autoregressive (STAR) models for capturing leverage effects in multiple series of the continuous components of realized volatility. We find that the leverage effect is well captured by a common nonlinear factor driven by returns, even though the persistence in each country’s volatility is country specific. A three-country model that incorporates both country specific persistence and a common leverage effect offers slight forecast improvements for mid-range horizons, relative to other models that do not allow for the common nonlinearity.Less
Decreases in stock market returns often lead to higher increases in volatility than increases in returns of the same magnitude, and it is common to incorporate these so-called leverage effects in GARCH and stochastic volatility models. Recent research has also found it useful to account for leverage in models of realized volatility, as well as in models of the continuous and jump components of realized volatility. This chapter explores the use of smooth transition autoregressive (STAR) models for capturing leverage effects in multiple series of the continuous components of realized volatility. We find that the leverage effect is well captured by a common nonlinear factor driven by returns, even though the persistence in each country’s volatility is country specific. A three-country model that incorporates both country specific persistence and a common leverage effect offers slight forecast improvements for mid-range horizons, relative to other models that do not allow for the common nonlinearity.
KatarinaM Juselius and Mikael Juselius
- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199679959
- eISBN:
- 9780191760136
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199679959.003.0005
- Subject:
- Economics and Finance, Econometrics
Edmund Phelps (1994) introduced a modified Phillips curve where the natural rate of unemployment is a function of the real interest rate instead of a constant. Koo (2010) argues that the effect of ...
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Edmund Phelps (1994) introduced a modified Phillips curve where the natural rate of unemployment is a function of the real interest rate instead of a constant. Koo (2010) argues that the effect of the interest rate on the macro economy is likely to be diluted during a balance sheet recession such as those recently seen in many countries. In the late 1980s, after having deregulated credit and capital movements, Finland experienced a housing boom which subsequently developed into a serious economic crisis similar to the recent ones. To learn from the Finnish experience we estimate the Phelps modified Phillips curve and use a Smooth Transition (STR) model to distinguish between ordinary periods and balance sheet recessions.Less
Edmund Phelps (1994) introduced a modified Phillips curve where the natural rate of unemployment is a function of the real interest rate instead of a constant. Koo (2010) argues that the effect of the interest rate on the macro economy is likely to be diluted during a balance sheet recession such as those recently seen in many countries. In the late 1980s, after having deregulated credit and capital movements, Finland experienced a housing boom which subsequently developed into a serious economic crisis similar to the recent ones. To learn from the Finnish experience we estimate the Phelps modified Phillips curve and use a Smooth Transition (STR) model to distinguish between ordinary periods and balance sheet recessions.
