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Abstrakty
The recent financial crisis has seen huge swings in corporate bond spreads. It is analyzed what quality VAR-based forecasts would have had prior and during the crisis period. Given that forecasts of the mean of interest rates or financial market prices are subject to large uncertainty independent of the class of models used, major emphasis is put on the quality of measures of forecast uncertainty. The VAR considered is based on a model first suggested in the literature in 2005. In a rolling window analysis, both the model's forecasts and joint prediction bands are calculated making use of recently proposed methods. Besides a traditional analysis of the forecast quality, the performance of the proposed prediction bands is assessed. It is shown that the actual coverage of joint prediction bands is superior to the coverage of naïve prediction bands constructed pointwise. (original abstract)
Rocznik
Tom
Numer
Strony
89--104
Opis fizyczny
Twórcy
autor
- University of Lodz, Poland
autor
- Justus-Liebig-University Giessen, Germany
Bibliografia
- [1] Carriero, A., T.E. Clark and M. Marcellino (2013). Bayesian VARs: Specification choices and forecast accuracy. Journal of Applied Econometrics, forthcoming. DOI: 10.1002/jae.2315.
- [2] Demetrescu, M. and M.-C. Wang (2014). Incorporating asymmetric preferences into fan charts and paths forecasts. Oxford Bulletin of Economics and Statistics 76(2), 287-297.
- [3] Financial Stability Report (2005), Finanzmarktstabilitätsbericht'2005, Deutsche Bundesbank, Frankfurt am Main, available at: http://www.bundesbank.de/Redaktion/EN/Downloads/Publications/Financial_Stability_Review/2005_financial_stability_review.pdf?__blob=publicationFile
- [4] Fischer, H. (2014). Approaching the corporate bond credit spread puzzle with subset vector autoregressive models, unpublished manuscript
- [5] Inoue, A. and L. Kilian (2014). Joint confidence sets for structural impulse responses. Departmental Working Papers 1401. Southern Methodist University, Department of Economics.
- [6] Lütkepohl, H., A. Staszewska-Bystrova and P. Winker (2014a). Comparison of methods for constructing joint confidence bands for impulse response functions. International Journal of Forecasting, forthcoming. DOI: 10.1016/j.ijforecast.2013.08.003.
- [7] Lütkepohl, H., A. Staszewska-Bystrova and P. Winker (2014b). Confidence bands for impulse responses: Bonferroni versus Wald. Working Paper no. 1354. DIW. Berlin.
- [8] Nicholls, D.F. and A.L. Pope (1988). Bias in estimation of multivariate autoregression. Australian Journal of Statistics 30A(1), 296-309.
- [9] Savin, I. and P. Winker (2013). Heuristic model selection for leading indicators in Russia and Germany. Journal of Business Cycle Measurement and Analysis 10(2), 67-89.
- [10] Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics 6(2), 461-464.
- [11] Staszewska-Bystrova, A. (2011). Bootstrap prediction bands for forecast paths from vector autoregressive models. Journal of Forecasting 30(8), 721-735.
- [12] Staszewska-Bystrova, A. (2013). Modified Scheffé's prediction bands. Jahrbücher für Nationalökonomie und Statistik 233(5-6), 680-690.
- [13] Staszewska-Bystrova, A. and P. Winker (2013). Constructing narrowest pathwise bootstrap prediction bands using threshold accepting. International Journal of Forecasting 29(2), 221-233.
- [14] Waggoner, D.F. and T. Zha (1999). Conditional forecasts in dynamic multivariate models. Review of Economics and Statistics 81(4), 639-651.
- [15] Wolf, M. and D. Wunderli (2012). Bootstrap joint prediction regions. Working Paper 748. National Center of Competence in Research Financial Valuation and Risk Management
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.ekon-element-000171285099