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on Econometric Time Series |
| By: | Xin Li; Carlos F. Tolmasky |
| Abstract: | We propose a new volatility model based on two stylized facts of the volatility in the stock market: clustering and leverage effect. We calibrate our model parameters, in the leading order, with 77 years Dow Jones Industrial Average data. We find in the short time scale (10 to 50 days) the future volatility is sensitive to the sign of past returns, i.e. asymmetric feedback or leverage effect. However, in the long time scale (300 to 1000 days) clustering becomes the main factor. We study non-stationary features by using moving windows and find that clustering and leverage effects display time evolutions that are rather nontrivial. The structure of our model allows us to shed light on a few surprising facts recently found by Chicheportiche and Bouchaud. |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1512.01916&r=ets |
| By: | Giannone, Domenico (Federal Reserve Bank of New York); Monti, Francesca (Bank of England); Reichlin, Lucrezia (London Business School) |
| Abstract: | This paper develops a framework that allows us to combine the tools provided by structural models for economic interpretation and policy analysis with those of reduced-form models designed for nowcasting. We show how to map a quarterly dynamic stochastic general equilibrium (DSGE) model into a higher frequency (monthly) version that maintains the same economic restrictions. Moreover, we show how to augment the monthly DSGE with auxiliary data that can enhance the analysis and the predictive accuracy in now-casting and forecasting. Our empirical results show that both the monthly version of the DSGE and the auxiliary variables offer help in real time for identifying the drivers of the dynamics of the economy. |
| Keywords: | DSGE models; forecasting; temporal aggregation; mixed-frequency data; large data sets |
| JEL: | C33 C53 E30 |
| Date: | 2015–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:751&r=ets |
| By: | Lunsford, Kurt Graden (Federal Reserve Bank of Cleveland) |
| Abstract: | This paper develops a simple estimator to identify structural shocks in vector autoregressions (VARs) by using a proxy variable that is correlated with the structural shock of interest but uncorrelated with other structural shocks. When the proxy variable is weak, modeled as local to zero, the estimator is inconsistent and converges to a distribution. This limiting distribution is characterized, and the estimator is shown to be asymptotically biased when the proxy variable is weak. The F statistic from the projection of the proxy variable onto the VAR errors can be used to test for a weak proxy variable, and the critical values for different VAR dimensions, levels of asymptotic bias, and levels of statistical significance are provided. An important feature of this F statistic is that its asymptotic distribution does not depend on parameters that need to be estimated. |
| Keywords: | F Statistic; Productivity Shocks; Proxy Variable; Structural Vector Autoregression; TFP; Weak IV |
| JEL: | C12 C13 C32 C36 O47 |
| Date: | 2015–12–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwp:1528&r=ets |