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on Econometric Time Series |
| By: | Antolin-Diaz, Juan (Fulcrum Asset Management); Rubio-Ramirez, Juan F. (Emory University & Federal Reserve Bank of Atlanta) |
| Abstract: | We identify structural vector autoregressions (SVARs) using narrative sign restrictions. Narrative sign restrictions constrain the structural shocks and the historical decomposition around key historical events, ensuring that they agree with the established narrative account of these episodes. Using models of the oil market and monetary policy, we show that narrative sign restrictions are highly informative. We highlight that adding a single narrative sign restriction dramatically sharpens and even changes the inference of SVARs originally identified via traditional sign restrictions. Our approach combines the appeal of narrative methods with the popularized usage of traditional sign restrictions. |
| Keywords: | narrative information; SVARs; Bayesian approach; sign restrictions; oil market; monetary policy |
| JEL: | C32 E52 Q35 |
| Date: | 2016–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedawp:2016-16&r=ets |
| By: | Masafumi Nakano (Graduate School of Ecnonomics, University of Tokyo); Akihiko Takahashi (Faculty of Economics, University of Tokyo); Soichiro Takahashi (Graduate School of Ecnonomics, University of Tokyo) |
| Abstract: | This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in financial markets. In particular, we effectively apply a particle filter (PF) to sequential estimation of states and parameters in a state space framework. Moreover, we develop three types of anomaly detectors, which are implemented easily in the PF algorithm to be used for investment decision. As a result, a simple investment strategy with our scheme is superior to the one based on the standard EMA and well-known traditional strategies such as equally-weighted, minimum-variance and risk parity portfolios. Our dataset is monthly total returns of global financial assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics, namely compound returns, Sharpe ratios, Sortino ratios and drawdowns. |
| Date: | 2016–12 |
| URL: | http://d.repec.org/n?u=RePEc:cfi:fseres:cf403&r=ets |
| By: | Vasily E. Tarasov; Valentina V. Tarasova |
| Abstract: | Long and short memory in economic processes is usually described by the so-called discrete fractional differencing and fractional integration. We prove that the discrete fractional differencing and integration are the Grunwald-Letnikov fractional differences of non-integer order d. Equations of ARIMA(p,d,q) and ARFIMA(p,d,q) models are the fractional-order difference equations with the Grunwald-Letnikov differences of order d. We prove that the long and short memory with power law should be described by the exact fractional-order differences, for which the Fourier transform demonstrates the power law exactly. The fractional differencing and the Grunwald-Letnikov fractional differences cannot give exact results for the long and short memory with power law, since the Fourier transform of these discrete operators satisfy the power law in the neighborhood of zero only. We prove that the economic processes with the continuous time long and short memory, which is characterized by the power law, should be described by the fractional differential equations. |
| Date: | 2016–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1612.07903&r=ets |
| By: | Richard Gerlach; Chao Wang |
| Abstract: | A new model framework called Realized Conditional Autoregressive Expectile (Realized-CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a manner analogous to the Realized-GARCH model. Competing realized measures (e.g. Realized Variance and Realized Range) are employed as the dependent variable in the measurement equation and to drive expectile dynamics. The measurement equation here models the contemporaneous dependence between the realized measure and the latent conditional expectile. We also propose employing the quantile loss function as the target criterion, instead of the conventional violation rate, during the expectile level grid search. For the proposed model, the usual search procedure and asymmetric least squares (ALS) optimization to estimate the expectile level and CARE parameters proves challenging and often fails to convergence. We incorporate a fast random walk Metropolis stochastic search method, combined with a more targeted grid search procedure, to allow reasonably fast and improved accuracy in estimation of this level and the associated model parameters. Given the convergence issue, Bayesian adaptive Markov Chain Monte Carlo methods are proposed for estimation, whilst their properties are assessed and compared with ALS via a simulation study. In a real forecasting study applied to 7 market indices and 2 individual asset returns, compared to the original CARE, the parametric GARCH and Realized-GARCH models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed Realized-CARE model, especially when incorporating the Realized Range and the sub-sampled Realized Range as the realized measure in the model. |
| Date: | 2016–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1612.08488&r=ets |
| By: | Abdelkamel Alj; Rajae Azrak; Christophe Ley; Guy Melard |
| Abstract: | This paper is about vector autoregressive-moving average (VARMA) models with time-dependent coefficients to represent non-stationary time series. Contrary to other papers in the univariate case, the coefficients depend on time but not on the series’ length n. Under appropriate assumptions, it is shown that a Gaussian quasi-maximum likelihood estimator is almost surely consistent and asymptotically normal. The theoretical results are illustrated by means of two examples of bivariate processes. It is shown that the assumptions underly- ing the theoretical results apply. In the second example the innovations are marginally heteroscedastic with a correlation ranging from −0.8 to 0.8. In the two examples, the asymptotic information matrix is obtained in the Gaussian case. Finally, the finite-sample behavior is checked via a Monte Carlo simulation study for n from 25 to 400. The results confirm the validity of the asymptotic properties even for short series and the asymptotic information matrix deduced from the theory. |
| Keywords: | non-stationary process; multivariate time series; time-varying models |
| Date: | 2016–12 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/241623&r=ets |
| By: | Abdelkamel Alj; Rajae Azrak; Christophe Ley; Guy Melard |
| Abstract: | This technical appendix contains proofs for the asymptotic properties of quasi-maximum likelihood (QML) estimators for vector autoregressive moving average (VARMA) models in the case where the coefficients depend on time instead of being constant. We refer to the main theorems of the paper Asymptotic properties of QML estimators for VARMA models with time-dependent coefficients" (Alj, Azrak, Ley and M elard, 2016). |
| Date: | 2016–12 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/241626&r=ets |
| By: | Christian Kleiber (University of Basel) |
| Abstract: | Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. Several recent methods for dating structural changes are compared using a time series of oil prices spanning more than 60 years. The methods broadly agree for the first part of the series up to the mid-1980s, for which changes are associated with major historical events, but provide somewhat different solutions thereafter, reflecting a gradual increase in oil prices that is not well described by a step function. As a further illustration, 1990s data on the volatility of the Hang Seng stock market index are reanalyzed. |
| Keywords: | change point problem, segmentation, structural change, time series |
| JEL: | C22 C87 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:bsl:wpaper:2016/06&r=ets |
| By: | Manuel Gonzalez-Astudillo; John M. Roberts |
| Abstract: | In this paper, we examine the results of GDP trend-cycle decompositions from the estimation of bivariate unobserved components models that allow for correlated trend and cycle innovations. Three competing variables are considered in the bivariate setup along with GDP: the unemployment rate, the inflation rate, and gross domestic income. We find that the unemployment rate is the best variable to accompany GDP in the bivariate setup to obtain accurate estimates of its trend-cycle correlation coefficient and the cycle. We show that the key feature of unemployment that allows for precise estimates of the cycle of GDP is that its nonstationary component is "small" relative to its cyclical component. Using quarterly GDP and unemployment rate data from 1948:Q1 to 2015:Q4, we obtain the trend-cycle decomposition of GDP and find evidence of correlated trend and cycle components and an estimated cycle that is about 2 percent below its trend at the end of the sample. |
| Keywords: | Unobserved components model ; Trend-cycle decomposition ; Trend-cycle correlation |
| JEL: | C13 C32 C52 |
| Date: | 2016–12–19 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2016-99&r=ets |
| By: | Korobilis, Dimitris; Pettenuzzo, Davide |
| Abstract: | We develop a novel, highly scalable estimation method for large Bayesian Vector Autoregressive models (BVARs) and employ it to introduce an "adaptive" version of the Minnesota prior. This flexible prior structure allows each coeffcient of the VAR to have its own shrinkage intensity, which is treated as an additional parameter and estimated from the data. Most importantly, our estimation procedure does not rely on computationally intensive Markov Chain Monte Carlo (MCMC) methods, making it suitable for high-dimensional VARs with more predictors that observations. We use a Monte Carlo study to demonstrate the accuracy and computational gains of our approach. We further illustrate the forecasting performance of our new approach by applying it to a quarterly macroeconomic dataset, and find that it forecasts better than both factor models and other existing BVAR methods. |
| Keywords: | Bayesian VARs, Minnesota prior, Large datasets, Macroeconomic forecasting |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:18626&r=ets |
| By: | Pierre Guerin; Danilo Leiva-Leon; Massimiliano Marcellino |
| Abstract: | We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-Switching three-pass regression filter (MS-3PRF), is suitable for datasets with large cross-sectional dimensions since estimation and inference are straightforward, as opposed to existing regime-switching factor models, where computational complexity limits applicability to few variables. In a Monte- Carlo experiment, we study the finite sample properties of the MS-3PRF and find that it performs favorably compared with alternative modelling approaches whenever there is structural instability in factor loadings. As empirical applications, we consider forecasting economic activity and bilateral exchange rates, finding that the MS-3PRF approach is competitive in both cases. Keywords: Factor model, Markov-switching, Forecasting. JEL Classification Code: C22, C23, C53. |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:igi:igierp:591&r=ets |