nep-rmg New Economics Papers
on Risk Management
Issue of 2007‒08‒18
three papers chosen by
Stan Miles
Thompson Rivers University

  1. How Frequently Does the Stock Price Jump? – An Analysis of High-Frequency Data with Microstructure Noises By Jin-Chuan Duan; András Fülöp
  2. The Comovements between Futures Markets for Crude Oil: Evidence from a Structural GARCH Model By Spargoli, Fabrizio; Zagaglia, Paolo
  3. Some Properties of Absolute Returns as a Proxy for Volatility By David E. Giles

  1. By: Jin-Chuan Duan (Joseph L. Rotman School of Management, University of Toronto); András Fülöp (ESSEC Paris and CREST.)
    Abstract: The stock price is assumed to follow a jump-diffusion process which may exhibit time-varying volatilities. An econometric technique is then developed for this model and applied to high-frequency time series of stock prices that are subject to microstructure noises. Our method is based on first devising a localized particle filter and then employing fixed-lag smoothing in the Monte Carlo EM algorithm to perform the maximum likelihood estimation and inference. Using the intra-day IBM stock prices, we find that high-frequency data are crucial to disentangling frequent small jumps from infrequent large jumps. During the trading sessions, jumps are found to be frequent but small in magnitude, which is in sharp contrast to infrequent but large jumps when the market is closed. We also find that at the 5- or 10-minute sampling frequency, the conclusion will critically depend on whether heavy-tailed microstructure noises have been accounted for. Ignoring microstructure noises can, for example, lead to an overestimation of the jump intensity of 50% or more.
    Keywords: Particle filtering, jump-diffusion, maximum likelihood, EM-algorithm.
    JEL: C22
    Date: 2007
  2. By: Spargoli, Fabrizio (Università Politecnica delle Marche); Zagaglia, Paolo (Dept. of Economics, Stockholm University)
    Abstract: This paper studies the linkages between the prices of oil futures traded on the New York Mercantile Exchange and the Intercontinental Exchange of London. We estimate a structural BEKK-GARCH model that allows for non-zero correlation between the structural innovations. We identify the structural parameters through restrictions on the reduced-form GARCH model. We find that the oil futures traded on the NYMEX and ICE can be used for mutual hedging purposes only when the structural conditional variances of both innovations are modest and, as such, no turbulent events have taken place. Periods with positive structural correlations are instead associated with peaks in the structural conditional variance of both innovations. During times of market turmoil, the structural variance of the returns on NYMEX futures becomes larger than that of ICE futures. This means that, when there are common shocks to both markets, the NYMEX reacts more strongly than the ICE. Our empirical evidence explains the negative reduced-form correlation between the two returns which is observed in turbulent periods.
    Keywords: oil prices; futures markets; GARCH; structural VAR
    JEL: C22 G19
    Date: 2007–08–14
  3. By: David E. Giles (Department of Economics, University of Victoria)
    Abstract: We use the stochastic volatility model as a basis for investigating the statistical properties of absolute returns as a measure of latent volatility in financial markets. Our results are compared with existing results for squared returns.
    Keywords: Volatility, stochastic volatility model, absolute returns, squared returns
    JEL: C10 C46 G10
    Date: 2007–08–09

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