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on Econometric Time Series |
| By: | Voutilainen, Ville |
| Abstract: | We propose a wavelet-based approach for construction of a financial cycle proxy. Specifically, we decompose three key macro-financial variables – private credit, house prices, and stock prices – on a frequency-scale basis using wavelet multiresolution analysis. The resulting “wavelet-based” sub-series are aggregated into a composite index representing our cycle proxy. Selection of the sub-series deemed most relevant is done by emphasizing early warning properties. The wavelet-based financial cycle proxy is shown to perform well in detecting banking crises in out-of-sample exercises, outperforming the credit-to-GDP gap and a financial cycle proxy derived using the approach of Schüler et al. (2015). |
| JEL: | C49 E32 E44 |
| Date: | 2017–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:bof:bofrdp:2017_011&r=ets |
| By: | Hiroyuki Kasahara (Vancouver School of Economics, University of British Columbia); Katsumi Shimotsu (Faculty of Economics, The University of Tokyo) |
| Abstract: | Markov regime switching models have been widely used in numerous empirical applications in economics and finance. However, the asymptotic distribution of the maximum likelihood estimator (MLE) has not been proven for some empirically popular Markov regime switching models. In particular, the asymptotic distribution of the MLE has been unknown for models in which the regime-specific density depends on both the current and the lagged regimes, which include the seminal model of Hamilton (1989) and the switching ARCH model of Hamilton and Susmel (1994). This paper shows the asymptotic normality of the MLE and the consistency of the asymptotic covariance matrix estimate of these models. |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2016cf1049&r=ets |
| By: | Bo Pieter Johannes Andree (VU Amsterdam and Tinbergen Institute, The Netherlands); Francisco Blasques (VU Amsterdam and Tinbergen Institute, The Netherlands); Eric Koomen (VU Amsterdam, The Netherlands) |
| Abstract: | This paper introduces a new model for spatial time series in which cross-sectional dependence varies nonlinearly over space by means of smooth transitions. We refer to our model as the Smooth Transition Spatial Autoregressive (ST-SAR). We establish consistency and asymptotic Gaussianity for the MLE under misspecification and provide additional conditions for geometric ergodicity of the model. Simulation results justify the use of limit theory in empirically relevant settings. The model is applied to study spatio-temporal dynamics in two cases that differ in spatial and temporal extent. We study clustering in urban densities in a large number of neighborhoods in the Netherlands over a 10-year period. We pay particular focus to the advantages of the ST-SAR as an alternative to linear spatial models. In our second study, we apply the ST-SAR to monthly long term interest rates of 15 European sovereigns over 25-year period. We develop a strategy to assess financial stability across the Eurozone based on attraction of individual sovereigns toward the common stochastic trend. Our estimates reveal that stability attained a low during the Greek sovereign debt crisis, and that the Eurozone has remained to struggle in attaining stability since the onset of the financial crisis. The results suggest that the European Monetary System has not fully succeeded in aligning the economies of Ireland, Portugal, Italy, Spain, and Greece with the rest of the Eurozone, while attraction between other sovereigns has continued to increase. In our applications linearity of spatial dependence is overwhelmingly rejected in terms of model fit and forecast accuracy, estimates of control variables improve, and residual correlation is better neutralized. |
| Keywords: | Dynamic panel, Threshold models, Spatial heterogeneity, Spatial autocorrelation, Urban density, Interest Rates, Monetary Stability, Sovereign Debt Crisis |
| JEL: | C01 R1 |
| Date: | 2017–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20170050&r=ets |
| By: | Dovonon, Prosper; Goncalves, Silvia; Hounyo, Ulrich; Meddahi, Nour |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:ide:wpaper:31735&r=ets |
| By: | Goncalves, Silvia; Hounyo, Ulrich; Meddahi, Nour |
| Abstract: | The main contribution of this paper is to propose a bootstrap method for inference on integrated volatility based on the pre-averaging approach, where the pre-averaging is done over all possible overlapping blocks of consecutive observations. The overlapping nature of the pre-averaged returns implies that the leading martingale part in the pre-averaged returns are kn-dependent with kn growing slowly with the sample size n. This motivates the application of a blockwise bootstrap method. We show that the \blocks of blocks" bootstrap method is not valid when volatility is time-varying. The failure of the blocks of blocks bootstrap is due to the heterogeneity of the squared pre-averaged returns when volatility is stochastic. To preserve both the dependence and the heterogeneity of squared pre-averaged returns, we propose a novel procedure that combines the wild bootstrap with the blocks of blocks bootstrap. We provide a proof of the first order asymptotic validity of this method for percentile and percentile-t intervals. Our Monte Carlo simulations show that the wild blocks of blocks bootstrap improves the finite sample properties of the existing first order asymptotic theory. We use empirical work to illustrate its use in practice. |
| Keywords: | Block bootstrap, high frequency data, market microstructure noise, preaveraging, realized volatility, wild bootstrap. |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:ide:wpaper:31734&r=ets |
| By: | Dovonon, Prosper; Goncalves, Silvia; Hounyo, Ulrich; Meddahi, Nour |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:31740&r=ets |