nep-mst New Economics Papers
on Market Microstructure
Issue of 2017‒02‒26
seven papers chosen by
Thanos Verousis

  1. Estimation for the Prediction of Point Processes with Many Covariates By Alessio Sancetta
  2. Price and Network Dynamics in the European Carbon Market By Andreas Karpf; Antoine Mandel; Stefano Battiston
  3. On the gains of using high frequency data and higher moments in Portfolio Selection By Rui Pedro Brito; Hélder Sebastião; Pedro Godinho
  4. Network-based Anomaly Detection for Insider Trading By Adarsh Kulkarni; Priya Mani; Carlotta Domeniconi
  5. Performance of information criteria used for model selection of Hawkes process models of financial data By J. M. Chen; A. G. Hawkes; E. Scalas; M. Trinh
  6. Banking Regulation and Market Making By David A. Cimon; Corey Garriott
  7. The amazing power of dimensional analysis: Quantifying market impact By Mathias Pohl; Alexander Ristig; Walter Schachermayer; Ludovic Tangpi

  1. By: Alessio Sancetta
    Abstract: Estimation of the intensity of a point process is considered within a nonparametric framework. The intensity measure is unknown and depends on covariates, possibly many more than the observed number of jumps. Only a single trajectory of the counting process is observed. Interest lies in estimating the intensity conditional on the covariates. The impact of the covariates is modelled by an additive model where each component can be written as a linear combination of possibly unknown functions. The focus is on prediction as opposed to variable screening. Conditions are imposed on the coefficients of this linear combination in order to control the estimation error. The rates of convergence are optimal when the number of active covariates is large. As an application, the intensity of the buy and sell trades of the New Zealand dollar futures is estimated and a test for forecast evaluation is presented. A simulation is included to provide some finite sample intuition on the model and asymptotic properties.
    Date: 2017–02
  2. By: Andreas Karpf (Centre d'Economie de la Sorbonne); Antoine Mandel (Paris School of Economics - Centre d'Economie de la Sorbonne); Stefano Battiston (Department of Banking and Finance - University of Zürich)
    Abstract: This paper presents an analysis of the European Emission Trading System as a transaction network. It is shown that, given the lack of a centralized market place, industrial actors had to resort to local connections and financial intermediaries to participate in the market. This gave rise to a hierarchical structure in the transaction network. To empirically relate networks statistics to market outcomes a PLS-PM modeling technique is introduced. It is shown that the asymmetries in the network induced market inefficiencies (e.g. increased bid-ask spread). Albeit the efficiency of the market has improved from the beginning of Phase II, the asymmetry persists, imposing unnecessary additional costs on agents and reducing the effectiveness of the market as a mitigation instrument
    Keywords: carbon market; network; climate economics
    JEL: L14 D85 Q56
    Date: 2017–02
  3. By: Rui Pedro Brito (CeBER and Faculty of Economics of the University of Coimbra); Hélder Sebastião (CeBER and Faculty of Economics of the University of Coimbra); Pedro Godinho (CeBER and Faculty of Economics of the University of Coimbra)
    Abstract: In this paper we conduct an empirical analysis on the performance gains of using high frequency data in Portfolio Selection. Within a CRRA-utility maximization framework, we suggest the construction of two different portfolios: a low and a high frequency portfolio. For ten different risk aversion levels, we compare the performance of both portfolios in terms of several out-of-sample measures. Using data on fourteen stocks of the CAC 40 stock market index, from January 1999 to December 2003, we conclude that the “fight” is always “won” by the high frequency portfolio for all the considered performance evaluation measures.
    Keywords: portfolio selection, utility maximization criteria, higher moments, high frequency data, out-of-sample analysis.
    JEL: C44 C55 C58 C61 C63 C88 G11
    Date: 2017–02
  4. By: Adarsh Kulkarni; Priya Mani; Carlotta Domeniconi
    Abstract: Insider trading is one of the numerous white collar crimes that can contribute to the instability of the economy. Traditionally, the detection of illegal insider trades has been a human-driven process. In this paper, we collect the insider tradings made available by the US Securities and Exchange Commissions (SEC) through the EDGAR system, with the aim of initiating an automated large-scale and data-driven approach to the problem of identifying illegal insider tradings. The goal of the study is the identification of interesting patterns, which can be indicators of potential anomalies. We use the collected data to construct networks that capture the relationship between trading behaviors of insiders. We explore different ways of building networks from insider trading data, and argue for a need of a structure that is capable of capturing higher order relationships among traders. Our results suggest the discovery of interesting patterns.
    Date: 2017–02
  5. By: J. M. Chen; A. G. Hawkes; E. Scalas; M. Trinh
    Abstract: We test three common information criteria (IC) for selecting the order of a Hawkes process with an intensity kernel that can be expressed as a mixture of exponential terms. These processes find application in high-frequency financial data modelling. The information criteria are Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and the Hannan-Quinn criterion (HQ). Since we work with simulated data, we are able to measure the performance of model selection by the success rate of the IC in selecting the model that was used to generate the data. In particular, we are interested in the relation between correct model selection and underlying sample size. The analysis includes realistic sample sizes and parameter sets from recent literature where parameters were estimated using empirical financial intra-day data. We compare our results to theoretical predictions and similar empirical findings on the asymptotic distribution of model selection for consistent and inconsistent IC.
    Date: 2017–02
  6. By: David A. Cimon; Corey Garriott
    Abstract: We present a model of market makers subject to recent banking regulations: liquidity and capital constraints in the style of Basel III and a position limit in the style of the Volcker Rule. Regulation causes market makers to reduce their intermediation by refusing principal positions. However, it can improve the bid-ask spread because it induces new market makers to enter. Since market makers intermediate less, asset prices exhibit a liquidity premium. Costs of regulation can be assessed by measuring principal positions and asset prices but not by measuring bid-ask spreads.
    Keywords: Financial markets, Financial system regulation and policies, Market structure and pricing
    JEL: G14 G20 L10
    Date: 2017
  7. By: Mathias Pohl; Alexander Ristig; Walter Schachermayer; Ludovic Tangpi
    Abstract: This note complements the inspiring work on dimensional analysis and market microstructure by Kyle and Obizhaeva \cite{kyle2016dimensional}. Our main theorem shows by a similar argument as usually applied in physics the following remarkable fact. If the market impact of a meta-order only depends on four well-defined and financially meaningful variables, then -- up to a constant -- there is only one possible form of this dependence. In particular, the market impact is proportional to the square root of the size of the meta-order. This theorem can be regarded as a special case of a more general result of Kyle and Obizhaeva. These authors consider five variables which might have an influence on the size of the market impact. In this case one finds a richer variety of functions which we precisely characterize. We also discuss the relation to classical arguments from physics, such as the period of a pendulum.
    Date: 2017–02

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