|
on Forecasting |
| By: | Pierzak, Agnieszka (Ministry of Finance in Poland) |
| Abstract: | This paper investigates the use of dynamic factor model for forecasting headline and core inflation as well as food price index in Poland. Method applied in the study extend conventional approaches by using bayesian techniques to dynamic factors' estimation, way of handling "ragged edge" data structure and allowing for the model to change over time. Forecasting results confirm that including current information extracted from data-rich environment improves inflation forecast precision and consequently DFMs perform better than the best autoregressive models. The analysis suggest also that applying dynamic model selection procedure can additionally reduce out-of-sample prediction errors. |
| Keywords: | dynamic factor model; forecasting; inflation; CPI |
| JEL: | C35 C38 E31 E37 |
| Date: | 2013–08 |
| URL: | http://d.repec.org/n?u=RePEc:ris:mfplwp:0017&r=for |
| By: | Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside) |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:201432&r=for |
| By: | Fullerton, Thomas M., Jr.; Mukhopadhyay, Somnath |
| Abstract: | Border region transportation forecast analysis is fraught with difficulty. In the case of El Paso, Texas and Ciudad Juarez, Chihuahua, Mexico, dual national business cycles and currency market fluctuations further complicate modeling efforts. Incomplete data samples and asymmetric data reporting conventions further confound forecasting exercises. Under these conditions, a natural alternative to structural econometric models to consider is neural network analysis. Neural network forecasts of air transportation and international bridge activity are developed using a multi-layered perceptron approach. Those out-of sample simulations are then compared to previously published forecasts produced with a system of simultaneous econometric equations. Empirical results indicate that the econometric approach is generally more accurate. In several cases, the two sets of forecasts are found to contain complementary information. |
| Keywords: | Regional Transport Demand; Neural Networks; Econometric Forecasting |
| JEL: | C53 R41 |
| Date: | 2013–04–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:59583&r=for |
| By: | Mauro Bernardi; Leopoldo Catania; Lea Petrella |
| Abstract: | In this paper we investigate the impact of news to predict extreme financial returns using high frequency data. We consider several model specifications differing for the dynamic property of the underlying stochastic process as well as for the innovation process. Since news are essentially qualitative measures, they are firstly transformed into quantitative measures which are subsequently introduced as exogenous regressors into the conditional volatility dynamics. Three basic sentiment indexes are constructed starting from three list of words defined by historical market news response and by a discriminant analysis. Models are evaluated in terms of their predictive accuracy to forecast out-of-sample Value-at-Risk of the STOXX Europe 600 sectors at different confidence levels using several statistic tests and the Model Confidence Set procedure of Hansen et al. (2011). Since the Hansen's procedure usually delivers a set of models having the same VaR predictive ability, we propose a new forecasting combination technique that dynamically weights the VaR predictions obtained by the models belonging to the optimal final set. Our results confirms that the inclusion of exogenous information as well as the right specification of the returns' conditional distribution significantly decrease the number of actual versus expected VaR violations towards one, as this is especially true for higher confidence levels. |
| Date: | 2014–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1410.6898&r=for |
| By: | Byström, Hans (Department of Economics, Lund University) |
| Abstract: | This study discusses how to compute and forecast long-term stock return volatilities, typically with a 5-year horizon or longer, using credit derivatives, and how such volatilities can be used in different areas ranging from the valuation of employee stock options and other long-term derivatives to the construction of market-based fear gauges in selected countries or market segments. In the empirical part of the paper I focus on the European financial sector and find the credit-implied volatilities and fear gauges to behave well. The forecasting accuracy of the credit-implied volatilities is found to be better than that of horizon-matched historical volatilities. |
| Keywords: | credit default swaps; implied volatility; CreditGrades; VIX; fear gauge; long-term forecast |
| JEL: | G10 |
| Date: | 2014–09–04 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:lunewp:2014_034&r=for |
| By: | Gerda Claeskens (KU Leuven, Belgium); Jan Magnus (VU University Amsterdam, the Netherlands); Andrey Vasnev (University of Sydney, Australia); Wendun Wang (Erasmus University, Rotterdam, the Netherlands) |
| Abstract: | This papers offers a theoretical explanation for the stylized fact that forecast combinations with estimated optimal weights often perform poorly in applications. The properties of the forecast combination are typically derived under the assumption that the weights are fixed, while in practice they need to be estimated. If the fact that the weights are random rather than fixed is taken into account during the optimality derivation, then the forecast combination will be biased (even when the original forecasts are unbiased) and its variance is larger than in the fixed-weights case. In particular, there is no guarantee that the 'optimal' forecast combination will be better than the equal-weights case or even improve on the original forecasts. We provide the underlying theory, some special cases and an application in the context of model selection. |
| Keywords: | forecast combination, optimal weights, model selection |
| JEL: | C53 C52 |
| Date: | 2014–09–19 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20140127&r=for |
| By: | Mauro Bernardi; Leopoldo Catania |
| Abstract: | This paper presents the R package MCS which implements the Model Confidence Set (MCS) procedure recently developed by Hansen et al. (2011). The Hansen's procedure consists on a sequence of tests which permits to construct a set of 'superior' models, where the null hypothesis of Equal Predictive Ability (EPA) is not rejected at a certain confidence level. The EPA statistic tests is calculated for an arbitrary loss function, meaning that we could test models on various aspects, for example punctual forecasts. The relevance of the package is shown using an example which aims at illustrating in details the use of the functions provided by the package. The example compares the ability of different models belonging to the ARCH family to predict large financial losses. We also discuss the implementation of the ARCH--type models and their maximum likelihood estimation using the popular R package rugarch developed by Ghalanos (2014). |
| Date: | 2014–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1410.8504&r=for |