nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒12‒22
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Testing the Conditional Mean Function of Autoregressive Conditional Duration Models By Nikolaus Hautsch
  2. Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models By Chris M Strickland; Gael Martin; Catherine S Forbes
  3. Unspanned Stochastic Volatility and the Pricing of Commodity Derivatives By Anders B. Trolle; Eduardo S. Schwartz

  1. By: Nikolaus Hautsch (Department of Economics, University of Copenhagen)
    Abstract: This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors. In order to capture persistent serial dependence in the duration process, we extend the model by an observation driven ARMA dynamic based on generalized errors. We illustrate the maximum likelihood estimation of both the model parameters and discrete points of the underlying unspecified baseline survivor function. The dynamic properties of the model as well as an assessment of the estimation quality is investigated in a Monte Carlo study. It is illustrated that the model is a useful approach to estimate conditional failure probabilities based on (persistent) serial dependent duration data which might be subject to censoring structures. In an empirical study based on financial transaction data we present an application of the model to estimate conditional asset price change probabilities. Evaluating the forecasting properties of the model, it is shown that the proposed approach is a promising competitor to well-established ACD type models.
    Keywords: augmented ACD models; semiparametric ACD models; news impact function; Lagrange multiplier tests
    JEL: C22 C41 C52
    Date: 2006–12
  2. By: Chris M Strickland; Gael Martin; Catherine S Forbes
    Abstract: The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MCMC) algorithms for two non-Gaussian state space models is examined. Specifically, focus is given to particular forms of the stochastic conditional duration (SCD) model and the stochastic volatility (SV) model, with four alternative parameterisations of each model considered. A controlled experiment using simulated data reveals that relationships exist between the simulation efficiency of the MCMC sampler, the magnitudes of the population parameters and the particular parameterisation of the state space model. Results of an empirical analysis of two separate transaction data sets for the SCD model, as well as equity and exchange rate data sets for the SV model, are also reported. Both the simulation and empirical results reveal that substantial gains in simulation efficiency can be obtained from simple reparameterisations of both types of non-Gaussian state space models.
    Keywords: Bayesian methodology, stochastic volatility, durations, non-centred in location, non-centred in scale, inefficiency factors.
    JEL: C11 C22 G1
    Date: 2006–12
  3. By: Anders B. Trolle; Eduardo S. Schwartz
    Abstract: We conduct a comprehensive analysis of unspanned stochastic volatility in commodity markets in general and the crude-oil market in particular. We present model-free results that strongly suggest the presence of unspanned stochastic volatility in the crude-oil market. We then develop a tractable model for pricing commodity derivatives in the presence of unspanned stochastic volatility. The model features correlations between innovations to futures prices and volatility, quasi-analytical prices of options on futures and futures curve dynamics in terms of a low-dimensional affine state vector. The model performs well when estimated on an extensive panel data set of crude-oil futures and options.
    JEL: G13
    Date: 2006–12

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