In this issue we have Volatility Forecasting: The Jumps Do Matter, Pooling versus model selection for nowcasting with many predictors, Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model, Taylor Rules and the Euro and more.
|This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is correctly separated into its continuous and discontinuous component. To this purpose, we introduce the concept of threshold multipower variation (TMPV), which is based on the joint use of bipower variation and threshold estimation. With respect to alternative methods, our TMPV estimator provides less biased and robust estimates of the continuous quadratic variation and jumps. This technique also provides a new test for jump detection which has substantially more power than traditional tests. We use this separation to forecast volatility by employing an heterogeneous autoregressive (HAR) model which is suitable to parsimoniously model long memory in realized volatility time series. Empirical analysis shows that the prop! osed techniques improve significantly the accuracy of volatility forecasts for the S&P500 index, single stocks and US bond yields, especially in periods following the occurrence of a jump.|
|Keywords:||volatility forecasting, jumps, bipower variation, threshold estimation, stock, bond|
|JEL:||G1 C1 C22 C53|
|This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to different sampling frequencies and publication delays. Two model classes suited in this context are factor models based on large datasets and mixed-data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst others, the factor estimation method and the number of factors, lag length and indicator selection. Thus, there are many sources of mis-specification when selecting a particular model, and an alternative could be pooling over a large set of models with different specifications. We evaluate the relative performance of pooling and model selection for now- and forecasting quarterly German GDP, a key macroeconomic indicator for t! he largest country in the euro area, with a large set of about one hundred monthly indicators. Our empirical findings provide strong support for pooling over many specifications rather than selecting a specific model.|
|Keywords:||factor models; forecast combination; forecast pooling; MIDAS; mixed-frequency data; model selection; nowcasting|
|In this paper, we apply the ARFIMA-GARCH model to the realized volatility and the continuous sample path variations constructed from high-frequency Nikkei 225 data. While the homoskedastic ARFIMA model performs excellently in predicting the Nikkei 225 realized volatility time series and their square-root and log transformations, the residuals of the model suggest presence of strong conditional heteroskedasticity similar to the finding of Corsi et al. (2007) for the realized S&P 500 futures volatility. An ARFIMA model augmented by a GARCH(1,1) specification for the error term largely captures this and substantially improves the fit to the data. In a multi-day forecasting setting, we also find some evidence of predictable time variation in the volatility of the Nikkei 225 volatility captured by the ARFIMA-GARCH model.|
|Keywords:||ARFIMA-GARCH, Volatility of realized volatility, Realized bipower variation, Jump detection, BDS test, Hong-Li test, High-frequency Nikkei 225 data|
|JEL:||C22 C53 G15|
David H. Papell
|This paper uses real-time data to show that inflation and either the output gap or unemployment, the variables which normally enter central banks' Taylor rules for interest-rate-setting, can provide evidence of out-of-sample predictability and forecasting ability for the United States Dollar/Euro exchange rate from the inception of the Euro in 1999 to the end of 2007. We also present less formal evidence that, with real-time data, the Taylor rule provides a better description of ECB than of Fed policy during this period. The strongest evidence is found for specifications that neither incorporate interest rate smoothing nor include the real exchange rate in the forecasting regression, and the results are robust to whether or not the coefficients on inflation and the real economic activity measure are constrained to be the same for the U.S. and the Euro Area. The evidence is stronger with inflation forecasts than with in! flation rates and with real-time data than with revised data. Bad news about inflation and good news about real economic activity both lead to out-of-sample predictability and forecasting ability through forecasted exchange rate appreciation.|
|This study seeks to forecast land use change in a North Georgia ecosystem, and estimate the economic value of the ecosystem using benefit transfer techniques. We forecast land use change based on a structural time series model and a simple growth rate model. The study suggests a lower bound willingness to pay value of about USD 16,000 per year to ensure compliance with fishing and drinking water quality standards with regard to fecal coliform bacteria and dissolved oxygen. Conservation efforts are likely to cost less than the cost of defensive behavior or ecosystem restoration.|
|Keywords:||Ecosystem, Economic value, North Georgia, land use, water quality, structural time series, benefit transfer, forecasting, Environmental Economics and Policy, Land Economics/Use, Q51, Q53, Q57, R14,|
Taken from the NEP-FOR mailing list edited by Rob Hyndman.