nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒05‒21
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Parallel Bayesian Inference for High Dimensional Dynamic Factor Copulas By Galeano San Miguel, Pedro; Ausín Olivera, María Concepción; Nguyen, Hoang
  2. Testing the Order of Fractional Integration of a Time Series in the Possible Presence of a Trend Break at an Unknown Point By Iacone, Fabrizio; Leybourne, Stephen J; Taylor, A M Robert
  3. Noisy independent component analysis of auto-correlated components By Jakob Knollm\"uller; Torsten A. En{\ss}lin
  4. The Fiction of Full BEKK By Chang, C-L.; McAleer, M.J.
  5. Forecasting the Volatility of Nikkei 225 Futures By Asai, M.; McAleer, M.J.
  6. "The Simultaneous Multivariate Hawkes-type Point Processes and Their Application to Financial Markets" By Naoto Kunitomo; Daisuke Kurisu; Yusuke Amano; Naoki Awaya
  7. Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks By Karol Szafranek

  1. By: Galeano San Miguel, Pedro; Ausín Olivera, María Concepción; Nguyen, Hoang
    Abstract: Copula densities are widely used to model the dependence structure of financial time series. However, the number of parameters involved becomes explosive in high dimensions which results in most of the models in the literature being static. Factor copula models have been recently proposed for tackling the curse of dimensionality by describing the behaviour of return series in terms of a few common latent factors. To account for asymmetric dependence in extreme events, we propose a class of dynamic one factor copula where the factor loadings are modelled as generalized autoregressive score (GAS) processes. We perform Bayesian inference in different specifications of the proposed class of dynamic one factor copula models. Conditioning on the latent factor, the components of the return series become independent, which allows the algorithm to run in a parallel setting and to reduce the computational cost needed to obtain the conditional posterior distributions of model parameters. We illustrate our approach with the analysis of a simulated data set and the analysis of the returns of 150 companies listed in the S&P500 index.
    Keywords: Parallel estimation; Generalized hyperbolic skew Student-t copula; GAS model; Factor copula models; Bayesian inference
    Date: 2017–05
  2. By: Iacone, Fabrizio; Leybourne, Stephen J; Taylor, A M Robert
    Abstract: We develop a test, based on the Lagrange multiplier [LM] testing principle, for the value of the long memory parameter of a univariate time series that is composed of a fractionally integrated shock around a potentially broken deterministic trend. Our proposed test is constructed from data which are de-trended allowing for a trend break whose (unknown) location is estimated by a standard residual sum of squares estimator. We demonstrate that the resulting LM-type statistic has a standard limiting null chi-squared distribution with one degree of freedom, and attains the same asymptotic local power function as an infeasible LM test based on the true shocks. Our proposed test therefore attains the same asymptotic local optimality properties as an oracle LM test in both the trend break and no trend break environments. Moreover, and unlike conventional unit root and stationarity tests, this asymptotic local power function does not alter between the break and no break cases and so there is no loss in asymptotic local power from allowing for a trend break at an unknown point in the sample, even in the case where no break is present. We also report the results from a Monte Carlo study into the finite-sample behaviour of our proposed test.
    Keywords: Fractional integration; trend break; Lagrange multiplier test; asymptotically locally most powerful test
    Date: 2017–05
  3. By: Jakob Knollm\"uller; Torsten A. En{\ss}lin
    Abstract: We present a new method for the separation of superimposed, independent, auto-correlated com- ponents from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and thereby increases the effective signal-to-noise ratio considerably, allowing separations even in the high noise regime. Characteristics of the measurement instruments can be included, allowing for application in complex measurement situations. Independent posterior samples can be provided, permitting error estimates on all de- sired quantities. Using the concept of information field theory, the algorithm is not restricted to any dimensionality of the underlying space or discretization scheme thereof.
    Date: 2017–05
  4. By: Chang, C-L.; McAleer, M.J.
    Abstract: The purpose of the paper is to show that univariate GARCH is not a special case of multivariate GARCH, specifically the Full BEKK model, except under parametric restrictions on the off-diagonal elements of the random coefficient autoregressive coefficient matrix, provides the regularity conditions that arise from the underlying random coefficient autoregressive process, and for which the (quasi-) maximum likelihood estimates have valid asymptotic properties under the appropriate parametric restrictions. The paper provides a discussion of the stochastic processes, regularity conditions, and asymptotic properties of univariate and multivariate GARCH models. It is shown that the Full BEKK model, which in practice is estimated almost exclusively, has no underlying stochastic process, regularity conditions, or asymptotic properties.
    Keywords: Random coefficient stochastic process, Off-diagonal parametric restrictions, Diagonal and Full BEKK, Regularity conditions, Asymptotic properties, Conditional volatility, Univariate and multivariate models
    JEL: C22 C32 C52 C58
    Date: 2017–01–15
  5. By: Asai, M.; McAleer, M.J.
    Abstract: For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers the stochastic volatility model with asymmetry and long memory, using high frequency data for the underlying asset. Empirical results for Nikkei 225 futures indicate that the adjusted R2 supports the appropriateness of the indirect method, and that the new method based on stochastic volatility models with the asymmetry and long memory outperforms the forecasting model based on the direct method using the pseudo long time series.
    Keywords: Forecasting, Volatility, Futures, Realized Volatility, Realized Kernel, Leverage Effects, Long Memory
    JEL: C22 C53 C58 G17
    Date: 2017–01–15
  6. By: Naoto Kunitomo (School of Political Sicence and Economics, Meiji University,); Daisuke Kurisu (Graduate School of Economics, The University of Tokyo); Yusuke Amano (Graduate School of Economics, The University of Tokyo); Naoki Awaya (Graduate School of Economics, The University of Tokyo)
    Abstract: In economic and financial time series we sometimes observe sudden and large jumps. Although these events are relatively rare, they would have significant influence not only on a financial market but also several different markets and macro economies. By using the simultaneous Hawkes-type multivariate point processes (SHPP) models, it is possible to analyze the causal effects of large events in the sense of the Granger-non-causality (GNC) and the instantaneous Granger-non-causality (IGNC). We investigate the financial market of Tokyo and other markets, and apply the Granger non-causality tests. We have found several important empirical findings among financial markets and macro economies.
    Date: 2017–04
  7. By: Karol Szafranek (Narodowy Bank Polski, Warsaw School of Economics)
    Abstract: Accurate inflation forecasts lie at the heart of effective monetary policy. By utilizing a thick modelling approach, this paper investigates the out-of-sample quality of the short-term Polish headline inflation forecasts generated by a combination of thousands of bagged single hidden-layer feed-forward artificial neural networks in the period of systematically falling and persistently low inflation. Results indicate that the forecasts from this model outperform a battery of popular approaches, especially at longer horizons. During the excessive disinflation it has more accurately accounted for the slowly evolving local mean of inflation and remained only mildly biased. Moreover, combining several linear and nonlinear approaches with diverse underlying model assumptions delivers further statistically significant gains in the predictive accuracy and statistically outperforms a panel of examined benchmarks at multiple horizons. The robustness analysis shows that resigning from data preprocessing and bootstrap aggregating severely compromises the forecasting ability of the model.
    Keywords: inflation forecasting, artificial neural networks, principal components, bootstrap aggregating, forecast combination
    JEL: C22 C38 C45 C53 C55
    Date: 2017

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