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on Forecasting |
| By: | Luc Bauwens (Université catholique de Louvain, CORE); Gary Koop (University of Strathclyde); Dimitris Korobilis (Université catholique de Louvain, CORE); Jeroen V.K. Rombouts (Institute of Applied Economics at HEC Montréal, CIRANO, CIRPEE; Université catholique de Louvain, CORE) |
| Abstract: | This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. We find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling window based forecasts perform well. |
| Keywords: | Forecasting, change-points, Markov switching, Bayesian inference |
| JEL: | C11 C22 C53 |
| Date: | 2011–07 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:38_11&r=for |
| By: | Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, Complutense University of Madrid, and Institute of Economic Research, Kyoto University); Paulo Araújo Santos (Escola Superior de Gestão e Tecnologia de Santarém and Center of Statistics and Applications University of Lisbon); Juan-Ángel Jiménez-Martín (Department of Quantitative Economics Complutense University of Madrid); Teodosio Pérez Amaral (Department of Quantitative Economics Complutense University of Madrid) |
| Abstract: | In McAleer et al. (2010b), a robust risk management strategy to the Global Financial Crisis (GFC) was proposed under the Basel II Accord by selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models. The robust forecast was based on the median of the point VaR forecasts of a set of conditional volatility models. In this paper we provide further evidence on the suitability of the median as a GFC-robust strategy by using an additional set of new extreme value forecasting models and by extending the sample period for comparison. These extreme value models include DPOT and Conditional EVT. Such models might be expected to be useful in explaining financial data, especially in the presence of extreme shocks that arise during a GFC. Our empirical results confirm that the median remains GFC-robust even in the presence of these new extreme value models. This is illustrated by using the S&P500 index before, during and after the 2008-09 GFC. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria, including several tests for independence of the violations. The strategy based on the median, or more generally, on combined forecasts of single models, is straightforward to incorporate into existing computer software packages that are used by banks and other financial institutions. |
| Keywords: | Value-at-Risk (VaR), DPOT, daily capital charges, robust forecasts, violation penalties, optimizing strategy, aggressive risk management, conservative risk management, Basel, global financial crisis. |
| JEL: | G32 G11 C53 C22 |
| Date: | 2011–07 |
| URL: | http://d.repec.org/n?u=RePEc:kyo:wpaper:782&r=for |
| By: | Axel Groß-Klußmann; Nikolaus Hautsch |
| Abstract: | We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of bid-ask spreads like the strong autocorrelation and discreteness of observations. We discuss theoretical properties of LMACP models and evaluate rolling window forecasts of quoted bid-ask spreads for stocks traded at NYSE and NASDAQ. We show that Poisson time series models significantly outperform forecasts from ARMA, ARFIMA, ACD and FIACD models. The economic significance of our results is supported by the evaluation of a trade schedule. Scheduling trades according to spread forecasts we realize cost savings of up to 13 % of spread transaction costs. |
| Keywords: | Bid-ask spreads, forecasting, high-frequency data, stock market liquidity, count data time series, long memory Poisson autoregression |
| JEL: | G14 C32 |
| Date: | 2011–07 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2011-044&r=for |
| By: | João Sousa (Banco de Portugal); Ricardo M. Sousa (Universidade do Minho - NIPE) |
| Abstract: | The goal of thes paper is to analyze predictability of future asset returns in the context of model uncertainty. Using data for the euro area, the US and the U.K., we show that one can improve the forecasts of stock returns using a Bayesian Model Averaging (BMA) approach, and there is a large amount of model uncertainty. The empirical evidence for the euro area suggests that several macroeconomic, financial and macro-financial variables are consistently among the most prominent determinants of risk premium. As for the U.S, only a few number of predictors play an important role. In the case of the UK, future stock returns are better forecasted by financial variables. These results are corroborated for both the M-open and the M-closed perspectives and in the context of "in-sample" and "out-of-sample" forescating. Finally, we highlight that the predictive ability of the BMA framework is stronger at longer periods, and clearly outperforms the constant expected returns and the autoregressive benchmark models. |
| Keywords: | stock returns, model uncertainty, Bayesian Model Averaging |
| JEL: | E21 G11 E44 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:nip:nipewp:21/2011&r=for |
| By: | Miguel A. G. Belmonte (University of Strathclyde); Gary Koop (University of Strathclyde); Dimitris Korobilis (Université Catholique de Louvain) |
| Abstract: | In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach. |
| Keywords: | hierarchical prior; time-varying parameters; Bayesian Lasso |
| JEL: | C11 C52 E37 E47 |
| Date: | 2011–07 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:35_11&r=for |
| By: | Michael Artis; Marianne Sensier |
| Abstract: | This paper assesses turning points in the economic cycle of Welsh unitary authorities by applying a mathematical algorithm to the claimant count unemployment data. All but one unitary authority has now emerged from recession (Anglesey being the exception). We also date the business cycle for the UK and country-level employment data and Wales has emerged from recession but Scotland is yet to exit recession. We estimate a logistic model which utilises housing sector and survey data to forecast the Welsh employment cycle. The model predicts that employment in Wales will continue to grow into 2011. |
| Keywords: | classical business cycles, forecasting |
| JEL: | C22 E32 E37 E40 |
| Date: | 2011–04 |
| URL: | http://d.repec.org/n?u=RePEc:cep:sercdp:0079&r=for |
| By: | Tongkui Yu; Shu-Heng Chen |
| Abstract: | We propose a simple agent-based model of the political election prediction market which reflects the intrinsic feature of the prediction market as an information aggregation mechanism. Each agent has a vote, and all agents’ votes determine the election result. Some of the agents participate in the prediction market. Agents form their beliefs by observing their neighbors’ voting disposition, and trade with these beliefs by following some forms of the zero-intelligence strategy. In this model, the mean price of the market is used as a forecast of the election result. We study the effect of the radius of agents’ neighborhood and the geographical distribution of information on the prediction accuracy. In addition, we also identify one of the mechanisms which can replicate the favorite-longshot bias, a stylized fact in the prediction market. This model can then provide a framework for further analysis on the prediction market when market participants have more sophisticated trading behavior. |
| Keywords: | Prediction market, Agent-based simulation, Information aggregation mechanism, Prediction accuracy, Zero-intelligence agents, Favorite-longshot bias |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:trn:utwpas:1119&r=for |