nep-for New Economics Papers
on Forecasting
Issue of 2014‒05‒17
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Time-Varying Correlation using the Dynamic Conditional Correlation (DCC) Model By Mapa, Dennis S.; Paz, Nino Joseph I.; Eustaquio, John D.; Mindanao, Miguel Antonio C.
  2. The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach By Franco, Ray John Gabriel; Mapa, Dennis S.
  3. Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models By Albis, Manuel Leonard F.; Mapa, Dennis S.
  4. Systemic Event Prediction by Early Warning System By Diana Zigraiova; Petr Jakubik
  5. Stochastic Volatility Estimation with GPU Computing By António Alberto Santos; João Andrade

  1. By: Mapa, Dennis S.; Paz, Nino Joseph I.; Eustaquio, John D.; Mindanao, Miguel Antonio C.
    Abstract: Hedging strategies have become more and more complicated as assets being traded have become more interrelated to each other. Thus, the estimation of risks for optimal hedging does not involve only the quantification of individual volatilities but also include their pairwise correlations. Therefore a model to capture the dynamic relationships is necessary to estimate and forecast correlations of returns through time. Engle’s dynamic conditional correlation (DCC) model is compared with other models of correlation. Performance of the correlation models are evaluated in this paper using only the daily log returns of the closing prices of the Peso-Dollar Exchange Rate and Philippine Stock Exchange index. Ultimately, Engle’s DCC model is adopted because of its consistency with expectations. Though generally negative, correlation between these two returns is not really constant as the results indicated. The forecast evaluation of the models was divided into in-sample and out-of-sample forecast performance with short-term (i.e., 22-day, 60-day, and 125-day) and medium-term (250-day and 500-day) rolling window correlations, or realized correlations, as proxies for the actual correlation. Based on the root mean squared error and mean absolute error, the integrated DCC model showed optimal forecast performance for the in-sample correlation patterns while the mean-reverting DCC model had the most desirable forecast properties for dynamic long-run forecasts. Also, the Diebold-Mariano tests showed that the integrated DCC has greater predictive accuracy in terms of the 3-month realized correlations than the rest of the models.
    Keywords: dynamic conditional correlation, Peso-Dollar exchange rate, PSE index, hedging
    JEL: C5 C52 C58 E47
    Date: 2014
  2. By: Franco, Ray John Gabriel; Mapa, Dennis S.
    Abstract: Frequency mismatch has been a problem in econometrics for quite some time. Many monthly economic and financial indicators are normally aggregated to match quarterly macroeconomic series such as GDP when analysed in a statistical model. However, temporal aggregation, although widely accepted, is prone to information loss. To address this issue, mixed frequency modelling was employed by using state space models with time-varying parameters. Quarter-on-quarter growth rate of GDP estimates were first treated as a monthly series with missing observation. Using Kalman filter algorithm, state space models were estimated with eleven monthly economic indicators as exogenous variables. A one-step-ahead predicted value for GDP growth rates was generated and as more indicators were included in the equation, the predicted values came closer to the actual data. Further evaluation revealed that among the group competing models, using Consumer Price Index (CPI), growth rates of PSEi, exchange rate, real money supply, WPI and merchandise exports are the more important determinants of GDP growth and generated the most desirable forecasts (lower forecast errors).
    Keywords: Multi-frequency models, state space model, Kalman filter, GDP forecast
    JEL: C5 C53 E3 E37
    Date: 2014
  3. By: Albis, Manuel Leonard F.; Mapa, Dennis S.
    Abstract: The estimated Vector AutoRegressive (VAR) model is sensitive to model misspecifications, such as omitted variables, incorrect lag-length, and excluded moving average terms, which results in biased and inconsistent parameter estimates. Furthermore, the symmetric VAR model is more likely misspecified due to the assumption that variables in the VAR have the same level of endogeneity. This paper extends the Bayesian Averaging of Classical Estimates, a robustness procedure in cross-section data, to a vector time-series that is estimated using a large number of Asymmetric VAR models, in order to achieve robust results. The combination of the two procedures is deemed to minimize the effects of misspecification errors by extracting and utilizing more information on the interaction of the variables, and cancelling out the effects of omitted variables and omitted MA terms through averaging. The proposed procedure is applied to simulated data from various forms of model misspecifications. The forecasting accuracy of the proposed procedure was compared to an automatically selected equal lag-length VAR. The results of the simulation suggest that, under misspecification problems, particularly if an important variable and MA terms are omitted, the proposed procedure is better in forecasting than the automatically selected equal lag-length VAR model.
    Keywords: BACE, AVAR, Robustness Procedures
    JEL: C5 C52 C58
    Date: 2014
  4. By: Diana Zigraiova (Czech National Bank and Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic); Petr Jakubik (European Insurance and Occupational Pensions Authority (EIOPA) and Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic)
    Abstract: This work develops an early warning system framework for assessing systemic risks and for predicting systemic events, i.e. periods of extreme financial instability with potential real costs, over the short horizon of six quarters and the long horizon of twelve quarters on the panel of 14 countries, both advanced and developing. First, we build Financial Stress Index to identify starting dates of systemic financial crises for each country in the panel. Second, early warning indicators for assessment and prediction of systemic risks are selected in a two-step approach; relevant prediction horizons for each indicator are found by the univariate logit model followed by the application of Bayesian model averaging method to identify the most useful indicators. Next, we validate early warning model, containing only useful indicators, for both horizons on the panel. Finally, the in-sample performance of the constructed EWS over both horizons is assessed for the Czech Republic. We find that the model over the 3 years’ horizon slightly outperforms the EWS with the horizon of 1.5 years on the Czech data. The long model attains the maximum utility in crises detection as well as it maximizes area under Receiver Operating Characteristics curve which measures the quality of the forecast.
    Keywords: Systemic risk, Financial stress, Financial crisis, Early warning indicators, Bayesian model averaging, Early warning system
    JEL: C33 E44 F47 G01
    Date: 2014–01
  5. By: António Alberto Santos (Faculty of Economics, University of Coimbra and GEMF, Portugal); João Andrade (Instituto de Telecomunicações, Dept. Electrical and Comp. Eng., University of Coimbra, Portugal)
    Abstract: In this paper, we show how to estimate the parameters of stochastic volatility models using Bayesian estimation and Markov chain Monte Carlo (MCMC) simulations through the approximation of the a-posteriori distribution of parameters. Simulated independent draws are made possible by using Graphics Processing Units (GPUs) to compute several Markov chains in parallel. We show that the higher computational power of GPUs can be harnessed and put to good use by addressing two challenges. Bayesian estimation using MCMC simulations benefit from powerful processors since it is a complex numerical problem. Moreover, sequential approaches are characterized for drawing highly correlated samples which reduces the Effective Sample Size (ESS) associated with the simulated values obtained from the posterior distribution under a Bayesian analysis. However, under the proposed parallel expression of the algorithm, we show that a faster convergence rate is possible by running independent Markov chains, drawing lower correlations and therefore increase the ESS. The results obtained with this approach are presented for the Stochastic Volatility (SV) model, basic and with leverage.
    Keywords: Bayesian Estimation; Graphics Processing Unit; Parallel Computing; Simulation; State-Space Models; Stochastic Volatility.
    JEL: C11 C13 C15 C53 C63 C87
    Date: 2014–04

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