nep-mst New Economics Papers
on Market Microstructure
Issue of 2017‒01‒15
seven papers chosen by
Thanos Verousis

  1. Intraday volatility, trading volume and trading intensity in the interbank market e-MID By Markus Engler; Vahidin Jeleskovic
  2. High Frequency Trading and Fragility By Cespa, Giovanni; Vives, Xavier
  3. Trading strategies for stock pairs regarding to the cross-impact cost By Shanshan Wang
  4. Threshold convergence between the federal fund rate and South African equity returns around the colocation period By Phiri, Andrew
  5. "Multivariate Stochastic Volatility Model with Realized Volatilities and Pairwise Realized Correlations " By Yuta Yamauchi; Yasuhiro Omori
  6. The Stock-Bond Comovements and Cross-Market Trading By Li, Mengling; Zheng, Huanhuan; Chong, Terence Tai Leung; Zhang, Yang
  7. Gauging market dynamics using trade repository data: the case of the Swiss franc de-pegging By Cielinska, Olga; Joseph, Andreas; Shreyas, Ujwal; Tanner, John; Vasios, Michalis

  1. By: Markus Engler (University of Kassel); Vahidin Jeleskovic (University of Kassel)
    Abstract: We apply a multivariate multiplicative error model (MMEM) and investigate effects in the simultaneous processes of high-frequency return volatilities, trading volume, and trading intensities on the Italien Electronic Interbank Credit Market (e-MID). Analysing five minutes data from the Italian interbank market (e-MID), we found that volatilities, volumes and trading intensities on electronic Interbank Credit Market share strong causal relationship resulting in highly significant estimates of MMEM. In addition, we run several estimations to observe a change in the market behaviour of the e-MID during the last financial crisis. The main results of our study are the usability of high-frequency data models for the analysis of interbank credit market data. Moreover, we find out that changes in the market behaviour occur during the crisis. Before the financial crises, liquidity variables have a negative influence on the volatility, in contrast to the time period after the outbrake of the financial turmoil. To our best knowledge, our paper presents the first empirical application of MMEM to an interbank credit market.
    Keywords: Multiplicative error models, interbank markets, e-MID, interstate volatility, trading intensity, intraday trading process, high-frequency financial data
    JEL: C15 C32 C52 C55 C58 E43 G01 G12
    Date: 2016
  2. By: Cespa, Giovanni; Vives, Xavier
    Abstract: We show that limited dealer participation in the market, coupled with an informational friction resulting from high frequency trading, can induce demand for liquidity to be upward sloping and strategic complementarities in traders' liquidity consumption decisions: traders demand more liquidity when the market becomes less liquid, which in turn makes the market more illiquid, fostering the initial demand hike. This can generate market instability, where an initial dearth of liquidity degenerates into a liquidity rout (as in a flash crash). While in a transparent market, liquidity is increasing in the proportion of high frequency traders, in an opaque market strategic complementarities can make liquidity U-shaped in this proportion as well as in the degree of transparency.
    Keywords: asymmetric information.; flash crash; high frequency trading; market fragmentation
    JEL: G10 G12 G14
    Date: 2016–12
  3. By: Shanshan Wang
    Abstract: We extend the framework of trading strategy for single stocks from Gatheral [2010] to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cross-impacts, i.e. the price change of one stock due to the trades of another stock, we can find out an optimal strategy for executing a sequence of trades from different stocks. We further apply our strategic model to a special pair of stocks, where the impacts of traded volumes and the cross-impacts of time lag are quantified with empirical data. We thus provide a view of how the cross-impact influences the trading strategy for a pair of stocks, although the trading strategy is to be improved by containing the cost of self-impact and to be extended to more than two stocks in a portfolio.
    Date: 2017–01
  4. By: Phiri, Andrew
    Abstract: Using weekly data collected from 20.09.2008 to 09.12.2016, this paper uses dynamic threshold adjustment models to demonstrate how the introduction of high-frequency and algorithmic trading on the Johannesburg Stock Exchange (JSE) has altered convergence relations between the federal fund rate and equity returns for aggregate and disaggregate South African market indices. We particularly find that for the post-crisis period, the JSE appears to operate more efficiently, in the weak-form sense, under high frequency trading platforms.
    Keywords: Colocation; High frequency trading; Global financial crisis; Federal fund rates; Equity returns; Threshold cointegration; Johannesburg Stock Exchange (JSE).
    JEL: C32 C51 C52 E44 E52
    Date: 2017–01–06
  5. By: Yuta Yamauchi (Graduate School of Economics, The University of Tokyo); Yasuhiro Omori (Faculty of Economics, The University of Tokyo)
    Abstract: Although stochastic volatility and GARCH models have been successful to describe the volatility dynamics of univariate asset returns, their natural extension to the multivariate models with dynamic correlations has been difficult due to several major problems. Firstly, there are too many parameters to estimate if available data are only daily returns, which results in unstable estimates. One solution to this problem is to incorporate additional observations based on intraday asset returns such as realized covariances. However, secondly, since multivariate asset returns are not traded synchronously, we have to use largest time intervals so that all asset returns are observed to compute the realized covariance matrices, where we fail to make full use of available intraday informations when there are less frequently traded assets. Thirdly, it is not straightforward to guarantee that the estimated (and the realized) covariance matrices are positive definite. Our contributions are : (1) we obtain the stable parameter estimates for dynamic correlation models using the realized measures, (2) we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5) propose the flexible correlation structure model (e.g. such as setting some correlations to be identically zeros if necessary), and (6) the parsimonious specification for the leverage effect is proposed. Our proposed models are applied to daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations, and are shown to outperform the existing models with regard to portfolio performances.
    Date: 2016–11
  6. By: Li, Mengling; Zheng, Huanhuan; Chong, Terence Tai Leung; Zhang, Yang
    Abstract: We propose an asset pricing model with heterogeneous agents allocating capital to the stock and bond markets to optimize their portfolios, utilizing the dynamic interaction between the two markets. While some agents focus on the stock market and have more expertise in it, the others specialize in the bond market. Based on their comparative advantages in a particular market, heterogeneous agents constantly revise their investment portfolios by taking into account the time-varying stock-bond return comovements and the changing market conditions. Agents’ collective investment behavior shapes the stock-bond interlinkage, which feedbacks on their subsequent capital allocations. Using monthly US stock and bond data from January 1990 to June 2014, we estimate the vector autoregression model with threshold and Markov switching mechanisms. We find evidence in support of flight-to-quality and show that it is mainly driven by the technical traders who actively sell stocks and buy bonds during periods of high market uncertainty.
    Keywords: Heterogeneity, Stock-Bond Comovement, Markov Switching VAR, Threshold VAR.
    JEL: G12 G15
    Date: 2016–09–12
  7. By: Cielinska, Olga (Bank of England); Joseph, Andreas (Bank of England); Shreyas, Ujwal (Bank of England); Tanner, John (Bank of England); Vasios, Michalis (Bank of England)
    Abstract: The Bank of England (“the Bank”) has access to some of the granular transaction level data resulting from EMIR trade reports. The velocity, granularity and richness of this dataset puts it in the realm of Big Data in the derivatives market, which brings with it its own set of challenges. These data have a number of potential uses in monitoring the market and helping to set policy. But these uses are only possible if the data are both accurate and complete on the one hand and we are able to analyse them effectively on the other. To help determine the status of these factors, we carry out a study of an external event to see how it was represented in the data. A suitable event was identified in the decision of the Swiss National Bank to discontinue the Swiss franc’s floor of 1.20 Swiss francs per euro on the morning of 15 January 2015. This was expected to show a number of effects in the Swiss franc foreign exchange over-the-counter (FX OTC) derivatives market. The removal of the floor led to extreme price moves in the forwards market, similar to those observed in the spot market, while trading in the Swiss franc options market was practically halted. We find evidence that the rapid intraday price fluctuation was associated with poor underlying market liquidity conditions, in particular the limited provision of liquidity by dealer banks in the first hour after the event. Looking at longer-term effects, we observe a reduced level of liquidity, associated with an increased level of market fragmentation, higher market volatility and an increase in the degree of collateralisation in the weeks following the event. It is worth noting that whilst we analyse the impact of the event on the market and its visibility in the data, we are not commenting on the SNB’s policy decision itself.
    Keywords: Market Microstructure; FX Derivatives; Swiss franc; EMIR; Trade Reporting
    JEL: G15 G18
    Date: 2017–01–06

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