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on Forecasting |
| By: | Rangan Gupta (Department of Economics, University of Pretoria); Josine Uwilingiye (Department of Economics, University of Pretoria) |
| Abstract: | This paper derives the econometric restrictions imposed by the Barro and Gordon (1983) model of dynamic time inconsistency on a bivariate time-series model of Consumer Price Index (CPI) inflation and real Gross Domestic Product (GDP), and tests these restrictions based on quarterly data for South Africa covering the period of 1960:01 through 1999:04, i.e., for the pre-inflation targeting period. The results show that the data are consistent with the short- and long-run implications of the theory of time-consistent monetary policy. Moreover, when the model is used to forecast one-step-ahead inflation over the period of 2001:01 to 2008:02, i.e., the period covering the starting point of the inflation targeting regime till date we, on average, obtain lower rates of inflation. The result tends to suggest that the South African Reserve Bank (SARB), perhaps needs to manage the inflation targeting framework better than it has done so far. |
| Keywords: | Dynamic Time Inconsistency; Inflation Targeting; One-Step-Ahead Forecasts |
| JEL: | E31 E52 E61 |
| Date: | 2008–10 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:200833&r=for |
| By: | Sonali Das (CSIR, Pretoria); Rangan Gupta (Department of Economics, University of Pretoria); Alain Kabundi (Department of Economics and Econometrics, University of Johannesburg) |
| Abstract: | This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01 to 2000:04, the large-scale BVARs, estimated under alternative hyperparameter values specifying the priors, are used to forecast real house price growth rates over a 24-quarter out-of-sample horizon of 2001:01 to 2006:04. The forecast performance of the large-scale BVARs are then compared with classical and Bayesian versions of univariate and multivariate Vector Autoregressive (VAR) models, merely comprising of the real growth rates of the large-, medium- and small-middle-segment houses, and a large-scale Dynamic Factor Model (DFM), which comprises of the same 268 variables included in the large-scale BVARs. Based on the one- to four-quarters ahead Root Mean Square Errors (RMSEs) over the out-of-sample horizon, we find the large-scale BVARs to not only outperform all the other alternative models, but to also predict the recent downturn in the real house price growth rates for the three categories of the middle-segment-housing over an ex ante period of 2007:01 to 2008:02. |
| Keywords: | Dynamic Factor Model, BVAR, Forecast Accuracy |
| JEL: | C11 C13 C33 C53 |
| Date: | 2008–10 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:200831&r=for |
| By: | Visser, Marcel P. |
| Abstract: | This paper decomposes volatility proxies according to upward and downward price movements in high-frequency financial data, and uses this decomposition for forecasting volatility. The paper introduces a simple Garch-type discrete time model that incorporates such high-frequency based statistics into a forecast equation for daily volatility. Analysis of S&P 500 index tick data over the years 1988-2006 shows that taking into account the downward movements improves forecast accuracy significantly. The R2 statistic for evaluating daily volatility forecasts attains a value of 0.80, both for in-sample and out-of-sample prediction. |
| Keywords: | volatility proxy; downward absolute power variation; log-Garch; volatility asymmetry; leverage effect; SP500; volatility forecasting; high-frequency data |
| JEL: | C53 C22 G10 |
| Date: | 2008–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:11100&r=for |
| By: | Thomas Busch (Danske Bank and CREATES); Bent Jesper Christensen (University of Aarhus and CREATES); Morten Ørregaard Nielsen (Queen's University and CREATES) |
| Abstract: | We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets, and of the separate continuous sample path and jump components of this, from variables in the information set, including implied volatility backed out from option prices. Recent nonparametric statistical techniques of Barndorff-Nielsen & Shephard (2004, 2006) are used to separate realized volatility into its continuous and jump components, which enhances forecasting performance, as shown by Andersen, Bollerslev & Diebold (2007). The heterogeneous autoregressive (HAR) model of Corsi (2004) is applied with implied volatility as an additional forecasting variable, and separating the forecasts of the two realized components. A new vector HAR (VecHAR) model for the resulting simultaneous system is introduced, controlling for possible endogeneity issues. Implied volatility contains incremental information about future volatility in all three markets, even when separating the continuous and jump components of past realized volatility in the information set, and it is an unbiased forecast in the foreign exchange and stock markets. In the foreign exchange market, implied volatility completely subsumes the information content of daily, weekly, and monthly realized volatility measures when forecasting future realized volatility or the continuous or jump component of this. In out-of-sample forecasting experiments, implied volatility alone is the preferred forecast of future realized volatility in all three markets, as mean absolute forecast error increases if realized volatility components are included in the forecast. Perhaps surprisingly, the jump component of realized volatility is, to some extent, predictable, and options appear to be calibrated to incorporate information about future jumps in all three markets. |
| Keywords: | Bipower variation, HAR, Heterogeneous Autoregressive Model, implied volatility, jumps, options, realized volatility, VecHAR, volatility forecasting |
| JEL: | C22 C32 F31 G1 |
| Date: | 2008–10 |
| URL: | http://d.repec.org/n?u=RePEc:qed:wpaper:1181&r=for |
| By: | de Silva, Ashton |
| Abstract: | This paper has a twofold purpose; the first is to present a small macroeconomic model in state space form, the second is to demonstrate that it produces accurate forecasts. The first of these objectives is achieved by fitting two forms of a structural state space macroeconomic model to Australian data. Both forms model short and long run relationships. Forecasts from these models are subsequently compared to a structural vector autoregressive specification. This comparison fulfills the second objective demonstrating that the state space formulation produces more accurate forecasts for a selection of macroeconomic variables. |
| Keywords: | State space; multivariate time series; macroeconomic model; forecast; SVAR |
| JEL: | C32 C51 C53 |
| Date: | 2008–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:11060&r=for |
| By: | Roxana Chiriac (Universität Konstanz); Valeri Voev |
| Abstract: | This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model’s forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model. |
| Date: | 2008–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0806&r=for |
| By: | Seymen, Atilim |
| Abstract: | The paper questions the reasonability of using forecast error variance decompositions for assessing the role of different structural shocks in business cycle fluctuations. It is shown that the forecast error variance decomposition is related to a dubious definition of the business cycle. A historical variance decomposition approach is proposed to overcome the problems related to the forecast error variance decomposition. |
| Keywords: | Business Cycles, Structural Vector Autoregression Models, Forecast Error Variance Decomposition, Historical Variance Decomposition |
| JEL: | C32 E32 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:7388&r=for |