New Economics Papers
on Market Microstructure
Issue of 2011‒03‒26
four papers chosen by
Thanos Verousis

  1. Constrained Mixture Models for Asset Returns Modelling By Iead Rezek
  2. The Identification of Price Jumps By Jan Hanousek; Evzen Kocenda; Jan Novotny
  3. Markov-Switching MIDAS Models By Pierre Guerin; Massimiliano Marcellino
  4. Denoising Surprises in Option Pricing By V. T. X. de Almeida; L. Moriconi

  1. By: Iead Rezek
    Abstract: The estimation of asset return distributions is crucial for determining optimal trading strategies. In this paper we describe the constrained mixture model, based on a mixture of Gamma and Gaussian distributions, to provide an accurate description of price trends as being clearly positive, negative or ranging while accounting for heavy tails and high kurtosis. The model is estimated in the Expectation Maximisation framework and model order estimation also respects the model's constraints.
    Date: 2011–03
  2. By: Jan Hanousek; Evzen Kocenda; Jan Novotny
    Abstract: We performed an extensive simulation study to compare the relative performance of many price-jump indicators with respect to false positive and false negative probabilities. We simulated twenty different time series specifications with different intraday noise volatility patterns and price-jump specifications. The double McNemar (1947) non-parametric test has been applied on constructed artificial time series to compare fourteen different price-jump indicators that are widely used in the literature. The results suggest large differences in terms of performance among the indicators, but we were able to identify the best-performing indicators. In the case of false positive probability, the best-performing price-jump indicator is based on thresholding with respect to centiles. In the case of false negative probability, the best indicator is based on bipower variation.
    Keywords: price jumps; price-jump indicators; non-parametric testing; Monte Carlo simulations; financial econometrics
    JEL: C14 F37 G15
    Date: 2011–03
  3. By: Pierre Guerin; Massimiliano Marcellino
    Abstract: This paper introduces a new regression model - Markov-switching mixed data sampling (MS-MIDAS) - that incorporates regime changes in the parameters of the mixed data sampling (MIDAS) models and allows for the use of mixed-frequency data in Markov-switching models. After a discussion of estimation and inference for MS-MIDAS, and a small sample simulation based evaluation, the MS-MIDAS model is applied to the prediction of the US and UK economic activity, in terms both of quantitative forecasts of the aggregate economic activity and of the prediction of the business cycle regimes. Both simulation and empirical results indicate that MSMIDAS is a very useful specification.
    Keywords: Business cycle, Mixed-frequency data, Non-linear models, Forecasting, Nowcasting
    JEL: C22 C53 E37
    Date: 2011
  4. By: V. T. X. de Almeida; L. Moriconi
    Abstract: We perform wavelet decomposition of high frequency financial time series into high and low-energy spectral sectors. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out, very unsuspectedly, that the high-energy component and a fraction of the low-energy contribution, defined {\it{in toto}} by most ($\simeq$ 98%) of the wavelet coefficients, can be neglected for the purpose of option premium evaluation with expiration times in the range of a few days to one month. The relevant low-energy component, which has attenuated volatility (reduction by a factor $\simeq$ 1/10), is (i) normally distributed, (ii) long-range correlated for intraday prices and volatility, and (iii) time-reversal asymmetric. Our results indicate that the usual non-gaussian profiles of log-return distributions contain much more information than needed for option pricing, which is essentially dependent on hidden self-correlation properties of the underlying asset fluctuations.
    Date: 2011–03

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