nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒11‒13
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. An invariance property of the common trends under linear transformations of the data By Søren Johansen; Katarina Juselius
  2. Modelling asset correlations during the recent FInancial crisis: A semiparametric approach By Nektarios Aslanidis; Isabel Casas
  3. Does Cointegration Matter? An Analysis in a RBC Perspective. By Laura Bisio; Andrea Faccini
  4. Stochastic Volatility, Long Run Risks, and Aggregate Stock Market Fluctuations By Stefan Avdjiev; Nathan
  5. Autoregressions in small samples, priors about observables and initial conditions By Marek Jarociński; Albert Marcet
  6. Financial correlations at ultra-high frequency: theoretical models and empirical estimation By Iacopo Mastromatteo; Matteo Marsili; Patrick Zoi

  1. By: Søren Johansen (Department of Economics, University of Copenhagen and CREATES, University of Aarhus); Katarina Juselius (Department of Economics, University of Copenhagen, University of Aarhus)
    Abstract: It is well known that if X(t) is a nonstationary process and Y(t) is a linear function of X(t), then cointegration of Y(t) implies cointegration of X(t). We want to find an analogous result for common trends if X(t) is generated by a finite order VAR. We first show that Y(t) has an infinite order VAR representation in terms of its prediction errors, which are a linear process in the prediction error for X(t). We then apply this result to show that the limit of the common trends for Y(t) are linear functions of the common trends for X(t). We illustrate the findings with a small analysis of the term structure of interest rates.
    Keywords: Cointegration vectors, common trends, prediction errors.
    JEL: C32
    Date: 2010–10–31
  2. By: Nektarios Aslanidis; Isabel Casas (School of Economics and Management and CREATES, Aarhus University)
    Abstract: 8000 Aarhus C, Denmark
    Keywords: Semiparametric Conditional Correlation Model, Nonparametric Correlations, DCC, Local Linear Estimator, Portfolio Evaluation.
    JEL: C14 G10
    Date: 2010–10–21
  3. By: Laura Bisio; Andrea Faccini
    Abstract: The aim of this paper is to verify if a proper SVEC representation of a standard Real Business Cycle model exists even when the capital stock series is omitted. The argument is relevant as the common unavailability of su¢ ciently long medium-frequency capital series prevent researchers from including capital in the widespread structural VAR (SVAR) repre- sentations of DSGE models - which is supposed to be the cause of the SVAR biased estimates. Indeed, a large debate about the truncation and small sample bias affecting the SVAR performance in approximat- ing DSGE models has been recently rising. In our view, it might be the case of a smaller degree of estimates distorsions when the RBC dynamics is approximated through a SVEC model as the information provided by the cointegrating relations among some variables might compensate the exclusion of the capital stock series from the empirical representation of the model.
    Keywords: RBC, SVAR, SVEC model, cointegration.
    JEL: E27 E32 C32 C52
    Date: 2010–05
  4. By: Stefan Avdjiev; Nathan
    Abstract: What are the main drivers of fluctuations in the aggregate US stock market? In this paper, we attempt to resolve the long-lasting debate surrounding this question by designing and solving a consumption-based asset pricing model which incorporates stochastic volatility, long-run risks in consumption and dividends, and Epstein-Zin preferences. Utilizing Bayesian MCMC techniques, we estimate the model by fitting it to US data on the level of the aggregate US stock market, the short-term real risk-free interest rate, real consumption growth, and real dividend growth. Our results indicate that, over short and medium horizons, fluctuations in the level of the aggregate US stock market are mainly driven by changes in expected excess returns. Conversely, low frequency movements in the aggregate stock market are primarily driven by changes in the expected long-run growth rate of real dividends.
    Keywords: asset pricing, stochastic volatility, long-run risks, Bayesian MCMC Methods
    Date: 2010–10
  5. By: Marek Jarociński (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.); Albert Marcet (London School of Economics.)
    Abstract: We propose a benchmark prior for the estimation of vector autoregressions: a prior about initial growth rates of the modeled series. We first show that the Bayesian vs frequentist small sample bias controversy is driven by different default initial conditions. These initial conditions are usually arbitrary and our prior serves to replace them in an intuitive way. To implement this prior we develop a technique for translating priors about observables into priors about parameters. We find that our prior makes a big difference for the estimated persistence of output responses to monetary policy shocks in the United States. JEL Classification: C11, C22, C32.
    Keywords: Vector Autoregression, Initial Condition, Bayesian Estimation, Prior about Growth Rate, Monetary Policy Shocks, Small Sample Distribution, Bias Correction.
    Date: 2010–11
  6. By: Iacopo Mastromatteo; Matteo Marsili; Patrick Zoi
    Abstract: A detailed analysis of correlation between stock returns at high frequency is compared with simple models of random walks. We focus in particular on the dependence of correlations on time scales - the so-called Epps effect. This provides a characterization of stochastic models of stock price returns which is appropriate at very high frequency.
    Date: 2010–11

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