nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒07‒25
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Quantile Spectral Analysis for Locally Stationary Time Series By Stefan Birr; Stanislav Volgushev; Tobias Kley; Holger Dette; Marc Hallin
  2. Factor augmented autoregressive distributed lag models with macroeconomic applications By Dalibor Stevanovic
  3. Semi-parametric time series modelling with autocopulas By Antony Ware; Ilnaz Asadzadeh
  4. Quantile Correlations: Uncovering temporal dependencies in financial time series By Thilo A. Schmitt; Rudi Sch\"afer; Holger Dette; Thomas Guhr
  5. "Cholesky Realized Stochastic Volatility Model" By Shinichiro Shirota; Yasuhiro Omori; Hedibert. F. Lopes; Haixiang Piao

  1. By: Stefan Birr; Stanislav Volgushev; Tobias Kley; Holger Dette; Marc Hallin
    Keywords: copulas; nonstationarity; ranks; periodogram; laplace spectrum
    Date: 2015–07
  2. By: Dalibor Stevanovic
    Abstract: This paper proposes a factor augmented autoregressive distributed lag (FADL) framework for analyzing the dynamic effects of common and idiosyncratic shocks. We first estimate the common shocks from a large panel of data with a strong factor structure. Impulse responses are then obtained from an autoregression, augmented with a distributed lag of the estimated common shocks. The approach has three distinctive features. First, identification restrictions, especially those based on recursive or block recursive ordering, are very easy to impose. Second, the dynamic response to the common shocks can be constructed for variables not necessarily in the panel. Third, the restrictions imposed by the factor model can be tested. The relation to other identification schemes used in the FAVAR literature is discussed. The methodology is used to study the effects of monetary policy and news shocks.
    Keywords: Factor models, structural VAR, impulse response,
    JEL: C32 E1
    Date: 2015–07–13
  3. By: Antony Ware; Ilnaz Asadzadeh
    Abstract: In this paper we present an application of the use of autocopulas for modelling financial time series showing serial dependencies that are not necessarily linear. The approach presented here is semi-parametric in that it is characterized by a non-parametric autocopula and parametric marginals. One advantage of using autocopulas is that they provide a general representation of the auto-dependency of the time series, in particular making it possible to study the interdependence of values of the series at different extremes separately. The specific time series that is studied here comes from daily cash flows involving the product of daily natural gas price and daily temperature deviations from normal levels. Seasonality is captured by using a time dependent normal inverse Gaussian (NIG) distribution fitted to the raw values.
    Date: 2015–07
  4. By: Thilo A. Schmitt; Rudi Sch\"afer; Holger Dette; Thomas Guhr
    Abstract: We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S\&P 500 stocks from the New York Stock Exchange. After establishing an empirical overview we compare the quantile-based correlation function to stochastic processes from the GARCH family and find striking differences. This motivates us to propose the quantile-based correlation function as a powerful tool to assess the agreements between stochastic processes and empirical data.
    Date: 2015–07
  5. By: Shinichiro Shirota (Department of Statistical Science, Duke University); Yasuhiro Omori (Faculty of Economics, The University of Tokyo); Hedibert. F. Lopes (Insper Institute of Education and Research); Haixiang Piao (Nippon Life Insurance Company)
    Abstract: Multivariate stochastic volatility models are expected to play important roles in financial applications such as asset allocation and risk management. However, these models suffer from two major difficulties: (1) there are too many parameters to estimate using only daily asset returns and (2) estimated covariance matrices are not guaranteed to be positive denite. Our approach takes advantage of realized covariances to attain the efficient estimation of parameters by incorporating additional information for the co-volatilities, and considers Cholesky decomposition to guarantee the positive definiteness of the covariance matrices. In this framework, we propose a exible modeling for stylized facts of financial markets such as dynamic correlations and leverage effects among volatilities. Taking a Bayesian approach, we describe Markov Chain Monte Carlo implementation with a simple but efficient sampling scheme. Our model is applied to nine U.S. stock returns data, and the model comparison is conducted based on portfolio performances. --
    Date: 2015–07

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