nep-for New Economics Papers
on Forecasting
Issue of 2009‒09‒19
eight papers chosen by
Rob J Hyndman
Monash University

  1. Understanding forecast failure of ESTAR models of real exchange rates By Buncic, Daniel
  2. Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets By Erie Febrian; Aldrin Herwany
  3. Higher-order beliefs among professional stock market forecasters: some first empirical tests By Rangvid, Jesper; Schmeling, Maik; Schrimpf, Andreas
  4. Combining Forecasts Based on Multiple Encompassing Tests in a Macroeconomic Core System By Costantini, Mauro; Kunst, Robert M.
  5. Forecasting economy with Bayesian autoregressive distributed lag model: choosing optimal prior in economic downturn By Bušs, Ginters
  6. Forecasting Stocks of Government Owned Companies (GOCS):Volatility Modeling By Erie Febrian; Aldrin Herwany
  7. "Dynamic Conditional Correlations for Asymmetric Processes" By Manabu Asai; Michael McAleer
  8. Identifying a Forward-Looking Monetary Policy in an Open Economy By Rokon Bhuiyan

  1. By: Buncic, Daniel
    Abstract: The forecast performance of the empirical ESTAR model of Taylor, Peel and Sarno (2001) is examined for 4 bilateral real exchange rate series over an out-of-sample evaluation period of nearly 12 years. Point as well as density forecasts are constructed, considering forecast horizons of 1 to 22 steps head. The study finds that no forecast gains over a simple AR(1) specification exist at any of the forecast horizons that are considered, regardless of whether point or density forecasts are utilised in the evaluation. Non-parametric methods are used in conjunction with simulation techniques to learn about the models and their forecasts. It is shown graphically that the nonlinearity in the point forecasts of the ESTAR model decreases as the forecast horizon increases. The non-parametric methods show also that the multiple steps ahead forecast densities are normal looking with no signs of bi-modality, skewness or kurtosis. Overall, there seems little to be gained from using an ESTAR specification over a simple AR(1) model.
    Keywords: Purchasing power parity, regime modelling, non-linear real exchange rate models, ESTAR, forecast evaluation, density forecasts, non-parametric methods.
    JEL: C53 C52 C22 F47 F31
    Date: 2009–02–03
  2. By: Erie Febrian (Finance & Risk Management Study Group (FRMSG) FE UNPAD); Aldrin Herwany (Research Division, Laboratory of Management FE UNPAD)
    Abstract: Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in different volatility approximations. The concern becomes more complicated when one tries to use the forecasting for asset distribution and risk management purposes in the linked regional markets. This paper aims at observing the effectiveness of the contending models of statistical and econometric volatility forecasting in the three South-east Asian prominent capital markets, i.e. STI, KLSE, and JKSE. In this paper, we evaluate eleven different models based on two classes of evaluation measures, i.e. symmetric and asymmetric error statistics, following Kumar’s (2006) framework. We employ 10-year data as in sample and 6-month data as out of sample to construct and test the models, consecutively. The resulting superior methods, which are selected based on the out of sample forecasts and some evaluation measures in the respective markets, are then used to assess the markets cointegration. We find that the best volatility forecasting models for JKSE, KLSE, and STI are GARCH (2,1), GARCH(3,1), and GARCH (1,1), respectively. We also find that international portfolio investors cannot benefit from diversification among these three equity markets as they are cointegrated.
    Keywords: Volatility Forecasting, Capital Market, Risk Management
    JEL: G0
    Date: 2009–09
  3. By: Rangvid, Jesper; Schmeling, Maik; Schrimpf, Andreas
    Abstract: A sizeable literature reports that financial market analysts and forecasters herd for reputational reasons. Using new data from a large survey of professional forecasters' expectations about stock market movements, we find strong evidence that the expected average of all forecasters' forecasts (the expected consensus forecast) influences an individual forecaster's own forecast. This looks like herding. In our survey, forecasters do not herd for reputational reasons, however. Instead of herding, we suggest that forecasters form higher-order expectations in the spirit of Keynes (1936). We find that young forecasters and portfolio managers, who in previous studies have been reported to be those who in particular herd, rely more on the expected consensus forecasts than other forecasters. Given that forecasters have no incentive to herd in our study, we conclude that our results indicate that the incorporation of the expected consensus forecast into individual forecasts is most likely due to higher-order expectations.
    Keywords: Higher-order expectations,stock market forecasts,forecaster heterogeneity
    JEL: G10 G15
    Date: 2009
  4. By: Costantini, Mauro (Department of Economics, University of Vienna BWZ, Vienna, Austria); Kunst, Robert M. (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria, and Department of Economics, University of Vienna, Vienna, Austria)
    Abstract: We investigate whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test-based procedure, which assigns non-zero weights to candidate models that add information not covered by other models. The potential benefits of this procedure are explored in extensive Monte Carlo simulations using realistic designs that are adapted to U.K. and to French macroeconomic data. The real economic growth rates of these two countries serve as the target series to be predicted. Generally, we find that the test-based averaging of forecasts yields a performance that is comparable to a simple uniform weighting of individual models. In one of our role-model economies, test-based averaging achieves some advantages in small samples. In larger samples, pure prediction models outperform forecast averages.
    Keywords: Combining forecasts, encompassing tests, model selection, time series
    JEL: C32 C53
    Date: 2009–09
  5. By: Bušs, Ginters
    Abstract: Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a sharp economic slowdown changes the optimal prior in two directions. First, it changes the structure of the optimal weight prior, setting smaller weight on the lagged dependent variable compared to variables containing more recent information. Second, greater uncertainty brought by a rapid economic downturn requires more space for coefficient variation, which is set by the overall tightness parameter. It is shown that the optimal overall tightness parameter may increase to such an extent that Bayesian ADL becomes equivalent to frequentist ADL.
    Keywords: Forecasting; Bayesian inference; Bayesian autoregressive distributed lag model; optimal prior; Litterman prior; business cycle; mixed estimation; grid search
    JEL: C52 C11 N14 C32 C13 C53 E17 C15 C22
    Date: 2009–09–13
  6. By: Erie Febrian (Finance & Risk Management Study Group (FRMSG) FE UNPAD); Aldrin Herwany (Research Division, Laboratory of Management FE UNPAD)
    Abstract: The development in forecasting techniques has been quite significant, which is indicated by the evolution on how researchers perceive characteristics of financial data. The researchers used to employ mean in their prediction model, but nowadays they tend to employ variance in developing the model. In addition, they also move from the static approaches (e.g., Autoregreesive (AR), Moving Average (MA), ARMA and ARIMA) to the dynamic ones (especially estimation model employing volatility change that just won Nobel prize in 2004). In this research, we try to develop the best prediction model by using volatility model, such as ARCH, GARCH, TARCH and EGARCH, and employing listed stocks of government-owned companies (GOCs) as the sample. The result proves that the employed volatility model and its derivatives are fairly accurate in predicting fluctuation of GOCs stock prices, which are reflected by the associated returns. In addition, the resulted model is capable to measure risk of the observed stock, as well as appropriate price of an asset.
    Keywords: Forecasting, Volatility Model, Risk and Return
    JEL: G0
    Date: 2009–09
  7. By: Manabu Asai (Faculty of Economics, Soka University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo)
    Abstract: The paper develops two Dynamic Conditional Correlation (DCC) models, namely the Wishart DCC (WDCC) model and the Matrix-Exponential Conditional Correlation (MECC) model. The paper applies the WDCC approach to the exponential GARCH (EGARCH) and GJR models to propose asymmetric DCC models. We use the standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper presents an empirical example using the trivariate data of the Nikkei 225, Hang Seng and Straits Times Indices for estimating and forecasting the WDCC-EGARCH and WDCC-GJR models, and compares the performance with the asymmetric BEKK model. The empirical results show that AIC and BIC favour the WDCC-EGARCH model to the WDCC-GJR and asymmetric BEKK models. Moreover, the empirical results indicate that the WDCC-EGARCH-t model produces reasonable VaR threshold forecasts, which are very close to the nominal 1% to 3% values.
    Date: 2009–08
  8. By: Rokon Bhuiyan (QED)
    Abstract: I identify a forward-looking monetary policy function in a structural VAR model by using forecasts of macroeconomic variables, in addition to the realized variables used in a standard VAR. Both impulse responses and variance decompositions of the monetary policy variable of this forecast-augmented VAR model suggest that forecasted variables play a greater role than realized variables in a central bank’s policy decisions. I also find that a contractionary policy shock instantaneously increases the market interest rate as well as the forecast of the market interest rate. The policy shock also appreciates both the British pound and the forecast of the pound on impact. On the other hand, the policy shock lowers expected inflation immediately, but affects realized inflation with a lag. When I estimate the standard VAR model encompassed in the forecast-augmented model, I find that a contractionary policy shock raises the inflation rate and leads to a gradual appreciation of the domestic currency. However, the inclusion of inflation expectations reverses this puzzling response of the inflation rate, and the inclusion of both the market interest rate forecast and the exchange rate forecast removes the delayed overshooting response of the exchange rate. These findings suggest that a standard VAR may incorrectly identify the monetary policy function.
    JEL: C32 E52 F37
    Date: 2009–09

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