nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒06‒25
six papers chosen by
Thanos Verousis

  1. High-frequency trading in the Bund futures market By Schlepper, Kathi
  2. On the use of high frequency measures of volatility in MIDAS regressions By Andreou, Elena
  3. High Frequency Evidence on the Demand for Gasoline By Laurence Levin; Matthew S. Lewis; Frank A. Wolak
  4. The space of outcomes of semi-static trading strategies need not be closed By Beatrice Acciaio; Martin Larsson; Walter Schachermayer
  5. Black Monday, globalization and trading behavior of stock investors By Kurz-Kim, Jeong-Ryeol
  6. Distributed ledger technologies in securities post-trading - Revolution or evolution? By Pinna, Andrea; Ruttenberg, Wiebe

  1. By: Schlepper, Kathi
    Abstract: In this work, I study the impact of high-frequency trading (HFT) on price discovery and volatility in the Bund futures market. Using a new dataset based on microseconds, the focus of the study is on the reaction of high-frequency traders (HFTs) to major macroeconomic news events. I show that through their fast and strong reaction to news, HFTs contribute more to price discovery compared to Non-HFTs, but also add a higher share to noise than to permanent volatility. Moreover, I find evidence that HFTs tend to supply less liquidity after an unexpected rise in market volatility and prior to upcoming macroeconomic news events. These findings suggest that in times of high market stress, HFT behavior may exacerbate intraday price volatility and amplify the risk of market disruptions in fixed income markets.
    Keywords: High-Frequency Trading,Price Discovery,Volatility
    JEL: G10 G12 G14
    Date: 2016
  2. By: Andreou, Elena
    Abstract: Many empirical studies link mixed data frequency variables such as low frequency macroeconomic or Â…nancial variables with high frequency Â…financial indicatorsÂ’ volatilities, especially within a predictive regression model context. The objective of this paper is threefold: First, we relate the standard Least Squares (LS) regression model with high frequency volatility predictors, with the corresponding Mixed Data Sampling Nonlinear LS (MIDAS-NLS) regression model (Ghysels et al., 2005, 2006), and evaluate the properties of the regression estimators of these models. We also consider alternative high frequency volatility measures as well as various continuous time models using their corresponding relevant higher-order moments to further analyze the properties of these estimators. Second, we derive the relative MSE efficiency of the slope estimator in the standard LS and MIDAS regressions, we provide conditions for relative efficiency and present the numerical results for different continuous time models. Third, we extend the analysis of the bias of the slope estimator in standard LS regressions with alternative realized measures of risk such as the Realized Covariance, Realized Beta and the Realized Skewness when the true DGP is a MIDAS model.
    Keywords: bias; efficiency; high-frequency volatility estimators; MIDAS regression model
    JEL: C22 C53 G22
    Date: 2016–06
  3. By: Laurence Levin; Matthew S. Lewis; Frank A. Wolak
    Abstract: Daily city-level expenditures and prices are used to estimate the price responsiveness of gasoline demand in the U.S. Using a frequency of purchase model that explicitly acknowledges the distinction between gasoline demand and gasoline expenditures, we consistently find the price elasticity of demand to be an order of magnitude larger than estimates from recent studies using more aggregated data. We demonstrate directly that higher levels of spatial and temporal aggregation generate increasingly inelastic demand estimates, and then perform a decomposition to examine the relative importance of several different sources of bias likely to arise in more aggregated studies.
    JEL: L91
    Date: 2016–06
  4. By: Beatrice Acciaio; Martin Larsson; Walter Schachermayer
    Abstract: Semi-static trading strategies make frequent appearances in mathematical finance, where dynamic trading in a liquid asset is combined with static buy-and-hold positions in options on that asset. We show that the space of outcomes of such strategies can have very poor closure properties when all European options for a fixed date $T$ are available for static trading. This causes problems for optimal investment, and stands in sharp contrast to the purely dynamic case classically considered in mathematical finance.
    Date: 2016–06
  5. By: Kurz-Kim, Jeong-Ryeol
    Abstract: Using a simple sign test, we report new empirical evidence, taken from both the US and the German stock markets, showing that trading behavior substantially changed around Black Monday in 1987. It turned out that before Black Monday investors behaved more as in the momentum strategy; and after Black Monday more as in the contrarian strategy. We argue that crashes, in general, themselves are merely a manifestation of uncertainty on stock markets and the high uncertainty due to globalization is mainly responsible for this change.
    Keywords: Trading behavior,Momentum,Contrarian,Black Monday,Globalization,Uncertainty
    JEL: C12 G02 G11
    Date: 2016
  6. By: Pinna, Andrea; Ruttenberg, Wiebe
    Abstract: Over the last decade, information technology has contributed significantly to the evolution of financial markets, without, however, revolutionising the way in which financial institutions interact with one another. This may be about to change, as some market players are now predicting that new database technologies, such as blockchain and other distributed ledger technologies (DLTs), could be the source of an imminent revolution. This paper analyses the main features of DLTs that could influence their potential adoption by financial institutions and discusses how the use of these technologies could affect the European post-trade market for securities. The original protocol underlying DLTs has its roots in the anarchic world of virtual currencies, which operate outside the conventional financial system. The public debate on DLTs has also been very much focused on the revolutionary potential of the technology. This paper concludes that, irrespective of the technology used and the market players involved, certain processes that feature in the post-trade market for securities will still need to be performed by institutions. DLTs could, however, stimulate a reorganisation of financial markets, which could in turn: (i) reduce reconciliation costs, (ii) streamline the post-trade value chain, and (iii) allow more efficient use to be made of collateral and regulatory capital. It should, nevertheless, be remembered that research into DLTs and their uses is at an early stage. The scope for financial institutions to adopt DLTs and their potential impact on mainstream financial markets are still unclear. This paper discusses three potential models of how market players could adopt DLTs for performing core post-trade functions. The DLT could be adopted either: (i) in clusters, (ii) collectively, or (iii) peer to peer. The evaluation of the three adoption models assumes that they are all equally compatible with the regulatory framework. It shows that, assuming this to be the case, they would each have different advantages and costs. JEL Classification: G21, G23, L15, O33
    Keywords: Bitcoin, blockchain, clearing, distributed ledger technologies, financial market infrastructures, fintech, settlement
    Date: 2016–04

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