nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒09‒25
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics By Davide Pettenuzzo; Rossen Valkanov; Allan Timmermann
  2. ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models By Michael Creel; Dennis Kristensen
  3. Adaptive Order Flow Forecasting with Multiplicative Error Models By Wolfgang Karl Härdle; Andrija Mihoci; Christopher Hian-Ann Ting;
  4. Affine Processes By Eberhard Mayerhofer
  5. Data-based priors for vector autoregressions with drifting coefficients By Korobilis, Dimitris

  1. By: Davide Pettenuzzo (International Business School, Brandeis University); Rossen Valkanov (University of California San Diego); Allan Timmermann (University of California San Diego)
    Abstract: We propose a new approach to predictive density modeling that allows for MI- DAS e¤ects in both the ?rst and second moments of the outcome and develop Gibbs sampling methods for Bayesian estimation in the presence of stochastic volatility dy- namics. When applied to quarterly U.S. GDP growth data, we ?nd strong evidence that models that feature MIDAS terms in the conditional volatility generate more accurate forecasts than conventional benchmarks. Finally, we ?nd that forecast combination methods such as the optimal predictive pool of Geweke and Amisano (2011) produce consistent gains in out-of-sample predictive performance.
    Keywords: MIDAS regressions; Bayesian estimation; stochastic volatility; out- of-sample forecasts; GDP growth.
    JEL: C53 C11 C32 E37
    Date: 2014–07
  2. By: Michael Creel (Universitat Autònoma de Barcelona and MOVE); Dennis Kristensen (University College London, CeMMaP, and CREATES)
    Abstract: We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative to standard likelihood-based versions since they rely on low-dimensional auxiliary statistics and so avoid computation of high-dimensional integrals. Despite their computational simplicity, we find that estimators and filters perform well in practice and lead to precise estimates of model parameters and latent variables. We show how the methods can incorporate intra-daily information to improve on the estimation and filtering. In particular, the availability of realized volatility measures help us in learning about parameters and latent states. The method is employed in the estimation of a flexible stochastic volatility model for the dynamics of the S&P 500 equity index. We find evidence of the presence of a dynamic jump rate and in favor of a structural break in parameters at the time of the recent financial crisis. We find evidence that possible measurement error in log price is small and has little effect on parameter estimates. Smoothing shows that, recently, volatility and the jump rate have returned to the low levels of 2004-2006.
    Keywords: Approximate Bayesian Computation, continuous-time processes, filtering, indirect inference, jumps, realized volatility, stochastic volatility
    JEL: C13 C14 C15 C33 G17
    Date: 2014–11–08
  3. By: Wolfgang Karl Härdle; Andrija Mihoci; Christopher Hian-Ann Ting;
    Abstract: A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e., the buyer and the seller-initiated trading volumes and the order flow dynamics. Analysing order flow series and its information content of mini Nikkei 225 index futures traded at the Osaka Securities Exchange in 2012 and 2013, a data-driven optimal length of local windows up to approximately 1-2 hours is reasonable to capture parameter variations and is suitable for short-term prediction. Our proposed trading strategies achieve statistical arbitrage opportunities and are therefore beneficial for quantitative finance practice.
    Keywords: multiplicative error models, trading volume, order flow, forecasting
    JEL: C41 C51 C53 G12 G17
    Date: 2014–07
  4. By: Eberhard Mayerhofer
    Abstract: We put forward a complete theory on moment explosion for fairly general state-spaces. This includes a characterization of the validity of the affine transform formula in terms of minimal solutions of a system of generalized Riccati differential equations. Also, we characterize the class of positive semidefinite processes, and provide existence of weak and strong solutions for Wishart SDEs. As an application, we answer a conjecture of M.L. Eaton on the maximal parameter domain of non-central Wishart distributions. The last chapter of this thesis comprises three individual works on affine models, such as a characterization of the martingale property of exponentially affine processes, an investigation of the jump-behaviour of processes on positive semidefinite cones, and an existence result for transition densities of multivariate affine jump-diffusions and their approximation theory in weighted Hilbert spaces.
    Date: 2014–09
  5. By: Korobilis, Dimitris
    Abstract: This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.
    Keywords: TVP-VAR, shrinkage, data-based prior, forecasting,
    Date: 2014

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