New Forecasting Papers 2011-02-15

Date:

2011

By:

Luc Bauwens
Gary Koop
Dimitris Korobilis
Jeroen V.K. Rombouts

URL:

http://d.repec.org/n?u=RePEc:lvl:lacicr:1104&r=for

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 parameters defining 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. However, 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 OLS forecasts perform well.

Keywords:

Forecasting, change-points, Markov switching, Bayesian inference

JEL:

C11

Date:

2011-01-31

By:

Bhattacharya, Kaushik

URL:

http://d.repec.org/n?u=RePEc:pra:mprapa:28515&r=for

The paper examines forecast performances of some popular rules of thumb vis-à-vis more sophisticated time series models in the specific context of foreign tourist arrival in India. Among all forecasting approaches attempted in the study, exponential smoothing (ES) and ARIMA provided the best short-term forecasts, closely followed by autoregressive distributed lag (ADL) models. These results are largely in agreement with cross-country findings on tourism forecast. Foreign tourist arrival data in India, however, displayed a regularity that did not change substantially even in the face of major global or local events. Given the regularity, our study suggests that rules of thumb can play an important practical part in short-term forecasts of tourist arrival in India. Our study, however, reveals that forecasts from such thumb rules could be improved substantially through simple residual corrections and incorporation of other information available in the public domain.

Keywords:

Tourism; Tourist Arrival; Forecasting; Rules of Thumb; Exponential Smoothing; ARIMA; ADL

JEL:

C22

Date:

2011-01

By:

Manabu Asai (Faculty of Economics, Soka University)
Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)
Marcelo C. Medeiros (Department of Economics, Pontifical Catholic University of Rio de Janeiro)

URL:

http://d.repec.org/n?u=RePEc:kyo:wpaper:758&r=for

Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even bias-corrected and consistent realized volatility (RV) estimates of IV can contain residual microstructure noise and other measurement errors. Such noise is called “realized volatility error”. Since such errors are ignored, we need to take account of them in estimating and forecasting IV. This paper investigates through Monte Carlo simulations the effects of RV errors on estimating and forecasting IV with RV data. It is found that: (i) neglecting RV errors can lead to serious bias in estimators; (ii) the effects of RV errors on one-step ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large; and (iii) even the partially corrected R2 recently proposed in the literature should be fully corrected for evaluating forecasts. This paper proposes a full correction of R2 . An empirical example for S&P 500 data is used to demonstrate the techniques developed in the paper.

Keywords:

realized volatility; diffusion; financial econometrics; measurement errors; forecasting; model evaluation; goodness-of-fit.

JEL:

G32

Date:

2010-08-01

By:

ROMBOUTS, Jeroen V. K. (Institute of Applied Economics at HEC Montréal, CIRANO, CIRPEE, Canada; Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)
STENTOFT, Lars (Department of Finance at HEC Montréal, CIRANO, CIRPEE, CREATES, Canada)

URL:

http://d.repec.org/n?u=RePEc:cor:louvco:2010049&r=for

This paper uses asymmetric heteroskedastic normal mixture models to fit return data and to price options. The models can be estimated straightforwardly by maximum likelihood, have high statistical fit when used on S&P 500 index return data, and allow for substantial negative skewness and time varying higher order moments of the risk neutral distribution. When forecasting out-of-sample a large set of index options between 1996 and 2009, substantial improvements are found compared to several benchmark models in terms of dollar losses and the ability to explain the smirk in implied volatilities. Overall, the dollar root mean squared error of the best performing benchmark component model is 39% larger than for the mixture model. When considering the recent financial crisis this difference increases to 69%.

Keywords:

asymmetric heteroskadastic models, finite mixture models, option pricing, out-of- sample prediction, statistical fit

JEL:

C11

Date:

2011-01-01

By:

Michael McAleer (University of Canterbury)
Juan-Ángel Jiménez-Martín
Teodosio Pérez-Amaral

URL:

http://d.repec.org/n?u=RePEc:cbt:econwp:11/05&r=for

A risk management strategy that is designed to be robust to the Global Financial Crisis (GFC), in the sense of selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models, was proposed in McAleer et al. (2010c). The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models. Such a risk management strategy is robust to the GFC in the sense that, while maintaining the same risk management strategy before, during and after a financial crisis, it will lead to comparatively low daily capital charges and violation penalties for the entire period. This paper presents evidence to support the claim that the median point forecast of VaR is generally GFC-robust. 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. In the empirical analysis, we choose several major indexes, namely French CAC, German DAX, US Dow Jones, UK FTSE100, Hong Kong Hang Seng, Spanish Ibex35, Japanese Nikkei, Swiss SMI and US S&P500. The GARCH, EGARCH, GJR and Riskmetrics models, as well as several other strategies, are used in the comparison. Backtesting is performed on each of these indexes using the Basel II Accord regulations for 2008-10 to examine the performance of the Median strategy in terms of the number of violations and daily capital charges, among other criteria. The Median is shown to be a profitable and safe strategy for risk management, both in calm and turbulent periods, as it provides a reasonable number of violations and daily capital charges. The Median also performs well when both total losses and the asymmetric linear tick loss function are considered.

Keywords:

Median strategy; Value-at-Risk (VaR); daily capital charges; robust forecasts; violation penalties; optimizing strategy; aggressive risk management; conservative risk management; Basel II Accord; global financial crisis (GFC)

JEL:

G32

Date:

2010-11

By:

Zafer Dilaver (Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey)
Lester C Hunt (Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey)

URL:

http://d.repec.org/n?u=RePEc:sur:seedps:131&r=for

This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57 respectively and the estimated short run and long run price elasticities being -0.09 and -0.38 respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of the implementation of past policies, the influence of technical progress, the changes in consumer behaviour and the effects of energy market structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity consumption will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008.

Keywords:

Turkish Residential Electricity Demand, Structural Time Series Model (STSM), Future Scenarios, Energy Demand Modelling and Forecasting.

JEL:

C22

Date:

2010-09

By:

Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020)
Alain Pirotte (ERMES (CNRS) and TEPP (CNRS), Université Panthéon-Assas Paris II, France INRETS-DEST, National Institute of Research on Transports and Safety, France)

URL:

http://d.repec.org/n?u=RePEc:max:cprwps:125&r=for

This paper considers various estimators using panel data seemingly unrelated regressions (SUR) with spatial error correlation. The true data generating process is assumed to be SUR with spatial error of the autoregressive or moving average type. Moreover, the remainder term of the spatial process is assumed to follow an error component structure. Both maximum likelihood and generalized moments (GM) methods of estimation are used. Using Monte Carlo experiments, we check the performance of these estimators and their forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous versus homogeneous panel data models.

Keywords:

Seemingly unrelated regressions, panel data, spatial dependence, heterogeneity, forecasting.

JEL:

C33