New Economics Papers
on Market Microstructure
Issue of 2007‒01‒28
seven papers chosen by
Thanos Verousis

  1. Financial Econometric Analysis at Ultra–High Frequency: Data Handling Concerns By Christian T. Brownlees; Giampiero Gallo
  2. Time-varying Mixing Weights in Mixture Autoregressive Conditional Duration Models By Giovanni De Luca; Giampiero M. Gallo
  3. Trading strategies in the Italian Interbank Market By Giulia Iori; Roberto Renò; Giulia de Masi; Guido Caldarelli
  4. Weighted Network Analysis of High Frequency Cross-Correlation Measures By Giulia Iori; Ovidiu V. Precup
  5. Cross-Correlation Measures in the High-Frequency Domain By Ovidiu Precup; Giulia Iori
  6. Currency Futures Volatility during the 1997 East Asian Crisis: An Application of Fourier Analysis By Vanessa Mattiussi; Giulia Iori
  7. Modeling Stock Pinning By Marc Jeannin; Giulia Iori; David Samuel

  1. By: Christian T. Brownlees (Università di Firenze, Dipartimento di Statistica "G. Parenti"); Giampiero Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti")
    Abstract: The financial econometrics literature on Ultra High-Frequency Data (UHFD) has been growing steadily in recent years. However, it is not always straightforward to construct time series of interest from the raw data and the consequences of data handling procedures on the subsequent statistical analysis are not fully understood. Some results could be sample or asset specific and in this paper we address some of these issues focussing on the data produced by the New York Stock Exchange, summarizing the structure of their TAQ ultra high-frequency dataset. We review and present a number of methods for the handling of UHFD, and explain the rationale and implications of using such algorithms. We then propose procedures to construct the time series of interest from the raw data. Finally, we examine the impact of data handling on statistical modeling within the context of financial durations ACD models.
    Keywords: Ultra-high Frequency Data, ACD models, Outliers, New York Stock Exchange
  2. By: Giovanni De Luca (Dipartimento di Statistica e Matematica per la Ricerca Economica Università di Napoli Parthenope.); Giampiero M. Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti")
    Abstract: Financial market price formation and exchange activity can be investigated by means of ultra-high frequency data. In this paper we investigate an extension of the Autoregressive Conditional Duration (ACD) model of Engle and Russell (1998) by adopting a mixture of distribution approach with time varying weights. Empirical estimation of the Mixture ACD model shows that the limitations of the standard base model and its inadequacy of modelling the behavior in the tail of the distribution are suitably solved by our model. When the weights are made dependent on some market activity data, the model lends itself to some structural interpretation related to price formation and information diffusion in the market.
  3. By: Giulia Iori (Department of Economics, City University, London); Roberto Renò; Giulia de Masi; Guido Caldarelli
    Abstract: Using a data set which includes all transactions among banks in the Italian money market, we study their trading strategies and the dependence among them. We use the Fourier method to compute the variance-covariance matrix of trading strategies. Our results indicate that well defined patterns arise. Two main communities of banks, which can be coarsely identified as small and large banks, emerge.
    Date: 2006–04
  4. By: Giulia Iori (Department of Economics, City University, London); Ovidiu V. Precup
    Abstract: In this paper we implement a Fourier method to estimate high frequency correlation matrices from small data sets. The Fourier estimates are shown to be considerably less noisy than the standard Pearson correlation measure and thus capable of detecting subtle changes in correlation matrices with just a month of data. The evolution of correlation at different time scales is analysed from the full correlation matrix and its Minimum Spanning Tree representation. The analysis is performed by implementing measures from the theory of random weighted networks.
    Keywords: High Frequency Correlation, Fourier Method, Random weighted Networks
    Date: 2006–11
  5. By: Ovidiu Precup (King’s College London); Giulia Iori (Department of Economics, City University, London)
    Abstract: On a high-frequency scale, financial time series are not homogeneous, therefore standard correlation measures can not be directly applied to the raw data. To deal with this problem the time series have to be either homogenized through interpolation or methods that can handle raw non-synchronous time series need to be employed. This paper compares two traditional methods that use interpolation with an alternative method applied directly to the actual time series. The three methods are tested on simulated data and actual trades time series. The temporal evolution of the correlation matrix is revealed through the analysis of the full correlation matrix and of the Minimum Spanning Tree representation. To perform the analysis we implement several measures from the theory of random weighted networks.
    Keywords: High-Frequency Correlation, Fourier method, Epps Effect, Minimum Spanning Tree, random networks
    Date: 2005–10
  6. By: Vanessa Mattiussi (Department of Economics, City University, London); Giulia Iori (Department of Economics, City University, London)
    Abstract: We analyze a recently proposed method to estimate volatility and correlation when prices are observed at a high frequency rate. The method is based on Fourier analysis and does not require any data manipulation, leading to more robust estimates than the traditional methodologies proposed so far. In the first part of the paper, we evaluate the performance of the Fourier algorithm to reconstruct the time volatility of simulated univariate and bivariate models. In the second part, the Fourier method is used to investigate the volatility and correlation dynamics of futures markets over the Asian crisis period, with the purpose of detecting possible interdependencies and volatility transmissions across countries amid a period of financial turmoil.
    Keywords: high frequency data, Fourier analysis, Asian crisis, volatility spillover
    Date: 2006–09
  7. By: Marc Jeannin; Giulia Iori (Department of Economics, City University, London); David Samuel
    Abstract: The paper investigates the effect of hedging strategies on the so called pinning effect, i.e. the tendency of stock’s prices to close near the strike price of heavily traded options as the expiration date nears. In the paper we extend the analysis of Avellaneda and Lipkin (2003) who propose an explanation of stock pinning in terms of delta hedging strategies for long option positions. We adopt a model introduced by Frey and Stremme (1997) and show that in this case pinning is driven by two effects: a hedging dependent drift term that pushes the stock price toward the strike price and a hedging dependent volatility term that constrains the stock price near the strike as it approaches it. Finally we show that pinning can be gnerated by dynamic hedging strategies under more realistic market conditions by simulating trading in a double auction model.
    Date: 2006–05

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