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on Econometric Time Series |
| By: | Peter Martey Addo (Centre d'Economie de la Sorbonne); Philippe De Peretti (Centre d'Economie de la Sorbonne) |
| Abstract: | The recent financial crisis has lead to a need for regulators and policy makers to understand and track systemic linkages. We provide a new approach to understanding systemic risk tomography in finance and insurance sectors. The analysis is achieved by using a recently proposed method on quantifying causal coupling strength, which identifies the existence of causal dependencies between two components of a multivariate time series and assesses the strength of their association by defining a meaningful coupling strength using the momentary information transfer (MIT). The measure of association is general, causal and lag-specific, reflecting a well interpretable notion of coupling strength and is practically computable. A comprehensive analysis of the feasibility of this approach is provided via simulated data and then applied to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. |
| Keywords: | Systemic risk, financial crisis, Coupling strength, financial institutions |
| JEL: | G12 C40 C32 G29 |
| Date: | 2014–10 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:14069&r=ets |
| By: | Steven Trypsteen |
| Abstract: | This paper examines growth forecasts of models that allow for crosscountry interactions and/or a time-varying variance plus feedback from volatility to growth. Allowing for these issues is done by augmenting an autoregressive model with cross-country weighted averages of growth and/or the GARCH-M framework. The models also allow for structural breaks in the mean and variance of growth. The obtained forecasts are then evaluated using statistical criteria, i.e. point and density forecasts, and an economic criterion, i.e. forecasting recessionary events. The results show that the two components are important to obtain improved point and density forecasts, but that forecasting recessionary events remains difficult. |
| URL: | http://d.repec.org/n?u=RePEc:not:notcfc:14/15&r=ets |
| By: | Rossen, Anja |
| Abstract: | Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, such models easily become very complex and difficult to estimate. The aim of this study is to investigate whether simple nonlinear extensions of autoregressive processes are able to provide more accurate forecasting results than linear models. Therefore, simple autoregressive processes are extended by means of nonlinear transformations (quadratic, cubic, trigonometric, exponential functions) of lagged time series observations and autoregression residuals. The proposed forecasting models are applied to a large set of macroeconomic and financial time series for 10 European countries. Findings suggest that such models, including nonlinear transformation of lagged autoregression residuals, are somewhat able to provide better forecasting results than simple linear models. Thus, it may be possibile to improve the forecasting accuracy of linear models by including nonlinear components. |
| Keywords: | nonlinear models,forecasting,transformations |
| JEL: | C22 C53 C51 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:hwwirp:157&r=ets |
| By: | Gregor Chliamovitch; Alexandre Dupuis; Bastien Chopard; Anton Golub |
| Abstract: | We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, at least in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, for then it provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates. |
| Date: | 2014–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1411.7805&r=ets |
| By: | Offer Lieberman (Bar-Ilan University); Peter C.B. Phillips (Cowles Foundation, Yale University) |
| Abstract: | This paper extends recent findings of Lieberman and Phillips (2014) on stochastic unit root (SUR) models to a multivariate case including a comprehensive asymptotic theory for estimation of the model's parameters. The extensions are useful because they lead to a generalization of the Black-Scholes formula for derivative pricing. In place of the standard assumption that the price process follows a geometric Brownian motion, we derive a new form of the Black-Scholes equation that allows for a multivariate time varying coefficient element in the price equation. The corresponding formula for the value of a European-type call option is obtained and shown to extend the existing option price formula in a manner that embodies the effect of a stochastic departure from a unit root. An empirical application reveals that the new model is consistent with excess skewness and kurtosis in the price distribution relative to a lognormal distribution. |
| Keywords: | Autoregression; Derivative, Diffusion, Options, Similarity, Stochastic unit root, Time-varying coefficients |
| JEL: | C22 |
| Date: | 2014–12 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:1964&r=ets |
| By: | Christiane Baumeister; James D. Hamilton |
| Abstract: | This paper makes the following original contributions to the literature. (1) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions that can be used for models that are overidentified, just-identified, or underidentified. (2) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n-variable VAR is confined to the set of values that orthogonalize the population variance-covariance matrix of OLS residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (3) We provide analytical characterizations of the informative prior distributions for impulse-response functions that are implicit in the traditional sign-restriction approach to VARs, and note, as a special case of result (2), that the influence of these priors does not vanish asymptotically. (4) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just-identified models. (5) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and illustrate how this could be done using a simple model of the U.S. labor market. |
| JEL: | C11 C32 E24 |
| Date: | 2014–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:20741&r=ets |
| By: | BAUWENS, Luc (Université catholique de Louvain, CORE, Belgium); GRIGORYEVA, Lyudmila (Laboratoire de Mathematiques de Besançon, Université de Franche-Comté, France); ORTEGA, Juan-Pablo (Laboratoire de Mathematiques de Besançon, Université de Franche-Comté, France) |
| Abstract: | This paper presents a method capable of estimating richly parametrized versions of the dynamic conditional correlation (DCC) model that go beyond the standard scalar case. The algorithm is based on the maximization of a Gaussian quasi-likelihood using a Bregman-proximal trust-region method to handle the various non-linear stationarity and positivity constraints that arise in this context. We consider the general matrix Hadamard DCC model with full rank, rank equal to two and, additionally, two different rank one matrix specifications. In the last mentioned case, the elements of the vectors that determine the rank one parameter matrices are either arbitrary or parsimoniously defined using the Almon lag function. We use actual stock returns data in dimensions up to thirty in order to carry out performance comparisons according to several in- and out-of-sample criteria. Our empirical results show that the use of richly parametrized models adds value with respect to the conventional scalar case. |
| Keywords: | multivariate volatility modeling, dynamic conditional correlations (DCC), non-scalar DCC models, constrained optimization, Bregman divergences, Bregman-proximal trust-region method |
| JEL: | C13 C32 G17 |
| Date: | 2014–06–11 |
| URL: | http://d.repec.org/n?u=RePEc:cor:louvco:2014012&r=ets |
| By: | Roberto Casarin (Department of Economics, University of Venice Cà Foscari) |
| Abstract: | This article discusses Windle and Carvalho's (2014) state-space model for observations and latent variables in the space of positive symmetric matrices. The present discussion focuses on the model specification and on the contribution to the positive-value time series literature. I apply the proposed model to financial data with a view to shedding light on some modeling issues. |
| Keywords: | Exponential Smoothing, Positive-Valued Processes, State-Space Models, Stochastic Volatility. |
| JEL: | C11 C18 C22 C53 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2014:23&r=ets |