Search results

1 – 10 of 37
Per page
102050
Citations:
Loading...
Access Restricted. View access options
Book part
Publication date: 29 February 2008

David E. Rapach and Mark E. Wohar

We thank the Simon Center for Regional Forecasting at the John Cook School of Business at Saint Louis University – especially Jack Strauss, Director of the Simon Center and Ellen…

Abstract

We thank the Simon Center for Regional Forecasting at the John Cook School of Business at Saint Louis University – especially Jack Strauss, Director of the Simon Center and Ellen Harshman, Dean of the Cook School – for its generosity and hospitality in hosting a conference during the summer of 2006 where many of the chapters appearing in this volume were presented. The conference provided a forum for discussing many important issues relating to forecasting in the presence of structural breaks and model uncertainty, and participants viewed the conference as helping to significantly improve the quality of the research appearing in the chapters of this volume.3 This volume is part of Elsevier's new series, Frontiers of Economics and Globalization, and we also thank Hamid Beladi for his support as an Editor of the series.

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Available. Content available
Book part
Publication date: 29 February 2008

Abstract

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Access Restricted. View access options
Book part
Publication date: 29 February 2008

David E. Rapach, Jack K. Strauss and Mark E. Wohar

We examine the role of structural breaks in forecasting stock return volatility. We begin by testing for structural breaks in the unconditional variance of daily returns for the…

Abstract

We examine the role of structural breaks in forecasting stock return volatility. We begin by testing for structural breaks in the unconditional variance of daily returns for the S&P 500 market index and ten sectoral stock indices for 9/12/1989–1/19/2006 using an iterative cumulative sum of squares procedure. We find evidence of multiple variance breaks in almost all of the return series, indicating that structural breaks are an empirically relevant feature of return volatility. We then undertake an out-of-sample forecasting exercise to analyze how instabilities in unconditional variance affect the forecasting performance of asymmetric volatility models, focusing on procedures that employ a variety of estimation window sizes designed to accommodate potential structural breaks. The exercise demonstrates that structural breaks present important challenges to forecasting stock return volatility. We find that averaging across volatility forecasts generated by individual forecasting models estimated using different window sizes performs well in many cases and appears to offer a useful approach to forecasting stock return volatility in the presence of structural breaks.

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Access Restricted. View access options
Book part
Publication date: 29 February 2008

Abstract

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Available. Content available
Book part
Publication date: 29 February 2008

Abstract

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Available. Content available
Book part
Publication date: 29 February 2008

Abstract

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Access Restricted. View access options
Book part
Publication date: 29 February 2008

Massimo Guidolin and Carrie Fangzhou Na

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence…

Abstract

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Access Restricted. View access options
Book part
Publication date: 29 February 2008

Nii Ayi Armah and Norman R. Swanson

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin…

Abstract

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Access Restricted. View access options
Book part
Publication date: 29 February 2008

Hamid Beladi and E. Kwan Choi

This series is aimed at economists and financial economists worldwide and will provide an in depth look at current global topics. Each volume in the series will focus on…

Abstract

This series is aimed at economists and financial economists worldwide and will provide an in depth look at current global topics. Each volume in the series will focus on specialized topics for greater understanding of the chosen subject and provide a detailed discussion of emerging issues. The target audiences are professional researchers, graduate students, and policy makers. It will offer cutting-edge views on new horizons and deepen the understanding in these emerging topics.

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

Access Restricted. View access options
Book part
Publication date: 29 February 2008

Jennifer L. Castle and David F. Hendry

Structural models' inflation forecasts are often inferior to those of naïve devices. This chapter theoretically and empirically assesses this for UK annual and quarterly…

Abstract

Structural models' inflation forecasts are often inferior to those of naïve devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, helping to interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

1 – 10 of 37
Per page
102050