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on Econometric Time Series |
| By: | Wang, Luya; Li, Kunpeng; Wang, Zhengwei |
| Abstract: | This paper considers the problem of estimating a simultaneous spatial autoregressive model (SSAR). We propose using the quasi maximum likelihood method to estimate the model. The asymptotic properties of the maximum likelihood estimator including consistency and limiting distribution are investigated. We also run Monte Carlo simulations to examine the finite sample performance of the maximum likelihood estimator. |
| Keywords: | Simultaneous equations model, Spatial autoregressive model, Maximum likelihood estimation, Asymptotic theory. |
| JEL: | C31 |
| Date: | 2014–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:59901&r=ets |
| By: | Chao, Wang; Richard, Gerlach |
| Abstract: | The realized GARCH framework is extended to incorporate the realized range, and the intra-day range, as potentially more efficient series of information than re- alized variance or daily returns, for the purpose of volatility and tail risk forecasting in a financial time series. A Bayesian adaptive Markov chain Monte Carlo method is employed for estimation and forecasting. Compared to a range of well known parametric GARCH models, predictive log-likelihood results across six market in- dex return series favor the realized GARCH models incorporating the realized range. Further, these same models also compare favourably for tail risk forecasting, both during and after the global financial crisis. |
| Keywords: | Tail Risk Forecasting; Predictive Likelihood; Realized GARCH; Realized Variance; Intra-day Range; Realized Range |
| Date: | 2014–11–07 |
| URL: | http://d.repec.org/n?u=RePEc:syb:wpbsba:2123/12235&r=ets |
| By: | Jing Zeng (Department of Economics, University of Konstanz, Germany) |
| Abstract: | Including disaggregate variables or using information extracted from the disaggregate variables into a forecasting model for an economic aggregate may improve the forecasting accuracy. In this paper we suggest to use the boosting method to select the disaggregate variables which are most helpful in predicting an aggregate of interest. We conduct a simulation study to investigate the variable selection ability of this method. To assess the forecasting performance a recursive pseudo-out-of-sample forecasting experiment for six key Euro area macroeconomic variables is conducted. The results suggest that using boosting to select relevant predictors is a feasible and competitive approach in forecasting an aggregate. |
| Keywords: | aggregation, macroeconomic forecasting, componentwise boosting, factor analysis |
| JEL: | C22 C43 C52 C53 C82 |
| Date: | 2014–09–23 |
| URL: | http://d.repec.org/n?u=RePEc:knz:dpteco:1420&r=ets |
| By: | Jozef Baruník (Institute of Economic Studies, Charles University, Opletalova 26, 110 00, Prague, CR and Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic.); František Èech (Institute of Economic Studies, Charles University, Opletalova 26, 110 00, Prague, CR and Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic.) |
| Abstract: | We introduce a methodology for dynamic modelling and forecasting of realized covariance matrices based on generalization of the heterogeneous autoregressive model (HAR) for realized volatility. Multivariate extensions of popular HAR framework leave substantial information unmodeled in residuals. We propose to employ a system of seemingly unrelated regressions to capture the information. The newly proposed generalized heterogeneous autoregressive (GHAR) model is tested against natural competing models. In order to show the economic and statistical gains of the GHAR model, portfolio of various sizes is used. We find that our modeling strategy outperforms competing approaches in terms of statistical precision, and provides economic gains in terms of mean-variance trade-o . Additionally, our results provide a comprehensive comparison of the performance when realized covariance and more ecient, noise-robust multivariate realized kernel estimator, is used. We study the contribution of both estimators across di erent sampling frequencies, and we show that the multivariate realized kernel estimator delivers further gains compared to realized covariance estimated on higher frequencies. |
| Keywords: | GHAR, portfolio optimisation, economic evaluation |
| JEL: | C18 C58 G15 |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2014_23&r=ets |
| By: | Jozef Baruník (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nábreží 6, 111 01 Prague 1, Czech Republic; Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic); Lucie Kraicová (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nábreží 6, 111 01 Prague 1, Czech Republic) |
| Abstract: | In this work we focus on the application of wavelet-based methods in volatility modeling. We introduce a new, wavelet-based estimator (wavelet Whittle estimator) of a FIEGARCH model, ARCH-family model capturing long-memory and asymmetry in volatility, and study its properties. Based on an extensive Monte Carlo experiment, both the behavior of the new estimator in various situations and its relative performance with respect to two more traditional estimators (maximum likelihood estimator and Fourier-based Whittle estimator) are assessed, along with practical aspects of its application. Possible solutions are proposed for most of the issues detected, including suggestion of a new specication of the estimator. This uses maximal overlap discrete wavelet transform, which improves the estimator perfor- mance, as we show in the experiment extension. Next, we study all the estimators in case of a FIEGARCH-Jump model, which brings interesting insights to their mechanism. We conclude that, after optimization of the estimation setup, the wavelet-based estimator may become an attractive robust alternative to the traditional methods |
| Keywords: | volatility, long memory, FIEGARCH, wavelets, Whittle, Monte Carlo |
| JEL: | C13 C18 C51 G17 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2014_33&r=ets |
| By: | Lahaye, Jerome (Fordham University); Neely, Christopher J. (Federal Reserve Bank of St. Louis) |
| Abstract: | We investigate the role of jumps in transmitting volatility between foreign exchange markets (Engle, Ito, and Lin, 1990; Melvin and Peiers Melvin, 2003; Cai, Howorka, and Wongswan, 2008). We show that recently developed estimators have very different implications for the impact of jumps on exchange rate volatility transmission. Specifically, isolated and successive jumps have opposite predictions for future volatility. Although the realized volatility literature finds that heat wave effects prevail for volatility transmission, we find evidence of both meteor shower and heat wave transmission of integrated volatility; in contrast, that jumps operate mainly in a meteor shower fashion. We also demonstrate the EUR/USD volatility and jump shocks spillover to the USD/JPY but the reverse transmission is much weaker. |
| Keywords: | realized; volatility; jumps; transmission; periodicity; intraday; meteor shower; heat wave; exchange rate; euro; yen; dollar. |
| JEL: | C13 C14 C32 C58 F31 F37 G15 |
| Date: | 2014–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2014-034&r=ets |
| By: | Yuta Kurose (School of Science and Technology, Kwansei Gakuin University); Yasuhiro Omori (Faculty of Economics, The University of Tokyo) |
| Abstract: | A multivariate stochastic volatility model with dynamic equicorrelation and cross leverage effect is proposed and estimated. Using a Bayesian approach, an ecient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates multiple latent variables simultaneously. Numerical examples are provided to show its sampling efficiency in comparison with the simple algorithm that generates one latent variable at a time given other latent variables. Furthermore, the proposed model is applied to the multivariate daily stock price index data. The model comparisons based on the portfolio performances and DIC show that our model overall outperforms competing models. |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2014cf941&r=ets |
| By: | Tsunehiro Ishihara (Department of Economics, Hitotsubashi University); Yasuhiro Omori (Faculty of Economics, The University of Tokyo); Manabu Asai (Faculty of Economics, Soka University) |
| Abstract: | A multivariate stochastic volatility model with the dynamic correlation and the cross leverage effect is described and its efficient estimation method using Markov chain Monte Carlo is proposed. The time-varying covariance matrices are guaranteed to be positive definite by using a matrix exponential transformation. Of particular interest is our approach for sampling a set of latent matrix logarithm variables from their conditional posterior distribution, where we construct the proposal density based on an approximating linear Gaussian state space model. The proposed model and its extended models with fat-tailed error distribution are applied to trivariate returns data (daily stocks, bonds, and exchange rates) of Japan. Further, a model comparison is conducted including constant correlation multivariate stochastic volatility models with leverage and diagonal multivariate GARCH models. |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2014cf938&r=ets |
| By: | Simone D. Grose; Gael M. Martin; D.S. Poskitt |
| Abstract: | This paper investigates the accuracy of bootstrap-based bias correction of persistence measures for long memory fractionally integrated processes. The bootstrap method is based on the semi-parametric sieve approach, with the dynamics in the long memory process captured by an autoregressive approximation. With a view to improving accuracy, the sieve method is also applied to data pre-filtered by a semi-parametric estimate of the long memory parameter. Both versions of the bootstrap technique are used to estimate the finite sample distributions of the sample autocorrelation coefficients and the impulse response coefficients and, in turn, to bias-adjust these statistics. The accuracy of the resultant estimators in the case of the autocorrelation coefficients is also compared with that yielded by analytical bias adjustment methods when available. The (raw) sieve technique is seen to yield a reduction in the bias of both persistence measures. The pre-filtered sieve produces a substantial further reduction in the bias of the estimated impulse response function, whilst the extra improvement yielded by pre-filtering in the case of the sample autocorrelation function is shown to depend heavily on the accuracy of the pre-filter. |
| Keywords: | Long memory, ARFIMA, sieve bootstrap, bootstrap-based bias correction, sample autocorrelation function, impulse response function. |
| JEL: | C18 C22 C52 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2014-19&r=ets |