nep-mst New Economics Papers
on Market Microstructure
Issue of 2011‒06‒04
five papers chosen by
Thanos Verousis
Swansea University

  1. Where is the value in high frequency trading? By Álvaro Cartea; José Penalva
  2. Fact or friction: Jumps at ultra high frequency By Kim Christensen; Roel Oomen; Mark Podolskij
  3. Liquidity Constraints and High Electricity Use By Brutscher, P.
  4. Pricing, liquidity and the control of dynamic systems in finance and economics By Willis, Geoff
  5. Trading Dynamics with Adverse Selection and Search: Market Freeze, Intervention and Recovery By Jonathan Chiu; Thorsten Koeppl

  1. By: Álvaro Cartea (Universidad Carlos III de Madrid); José Penalva (Banco de España)
    Abstract: We analyze the impact of high frequency trading in financial markets based on a model with three types of traders: liquidity traders, market makers, and high frequency traders. Our four main findings are: i) The price impact of the liquidity trades is higher in the presence of the high frequency trader and is increasing with the size of the trade. In particular, we show that the high frequency trader reduces (increases) the prices that liquidity traders receive when selling (buying) their equity holdings. ii) Although market makers also lose revenue to the high frequency trader in every trade, they are compensated for these losses by a higher liquidity discount. iii) High frequency trading increases the volatility of prices. iv) The volume of trades doubles as the high frequency trader intermediates all trades between the liquidity traders and market makers. This additional volume is a consequence of trades which are carefully tailored for surplus extraction and are neither driven by fundamentals nor is it noise trading. In equilibrium, high frequency trading and traditional market making coexist as competition drives down the profits for new high frequency traders while the presence of high frequency traders does not drive out traditional market makers.
    Keywords: high frequency traders, high frequency trading, flash trading, liquidity traders, institutional investors, market microstructure
    JEL: G12 G13 G14 G28
    Date: 2011–05
  2. By: Kim Christensen (Aarhus University and CREATES); Roel Oomen (Deutsche Bank, London); Mark Podolskij (University of Heidelberg and CREATES)
    Abstract: In this paper, we demonstrate that jumps in financial asset prices are not nearly as common as generally thought, and that they account for only a very small proportion of total return variation. We base our investigation on an extensive set of ultra high-frequency equity and foreign exchange rate data recorded at milli-second precision, allowing us to view the price evolution at a microscopic level. We show that both in theory and practice, traditional measures of jump variation based on low-frequency tick data tend to spuriously attribute a burst of volatility to the jump component thereby severely overstating the true variation coming from jumps. Indeed, our estimates based on tick data suggest that the jump variation is an order of magnitude smaller. This finding has a number of important implications for asset pricing and risk management and we illustrate this with a delta hedging example of an option trader that is short gamma. Our econometric analysis is build around a pre-averaging theory that allows us to work at the highest available frequency, where the data are polluted bymicrostructure noise. We extend the theory in a number of directions important for jump estimation and testing. This also reveals that pre-averaging has a built-in robustness property to outliers in high-frequency data, and allows us to show that some of the few remaining jumps at tick frequency are in fact induced by data-cleaning routines aimed at removing the outliers.
    Keywords: jump variation, high-frequency data, market microstructure noise, pre-averaging, realised variation, outliers.
    JEL: C10 C80
    Date: 2011–05–26
  3. By: Brutscher, P.
    JEL: D12 D14 Q40
    Date: 2011–02–07
  4. By: Willis, Geoff
    Abstract: The paper discusses various practical consequences of treating economics and finance as an inherently dynamic and chaotic system. On the theoretical side this looks at the general applicability of the market-making pricing approach to economics in general. The paper also discuses the consequences of the endogenous creation of liquidity and the role of liquidity as a state variable. On the practical side, proposals are made for reducing chaotic behaviour in both housing markets and stock markets.
    Keywords: dynamic; chaotic; liquidity; market-microstructure; post-keynesian
    JEL: D53 G1 D40
    Date: 2011–05–26
  5. By: Jonathan Chiu (Bank of Canada); Thorsten Koeppl (Queen's University)
    Abstract: We study the trading dynamics in an asset market where the quality of assets is private information of the owner and finding a counterparty takes time. When trading of a financial asset ceases in equilibrium as a response to an adverse shock to asset quality, a large player can resurrect the market by purchasing bad assets which involves financial losses. The equilibrium response to such a policy is intricate as it creates an announcement effect: a mere announcement of intervening at a later point in time can cause markets to function again. This effect leads to a gradual recovery in trading volume, with asset prices converging non-monotonically to their normal values. The optimal policy is to intervene immediately at a minimal scale when markets are deemed important and losses are small. As losses increase and the importance of the market declines, the optimal intervention is delayed and it can be desirable to rely more on the announcement effect by increasing the size of the intervention. Search frictions are important for all these results. They compound adverse selection, making a market more fragile with respect to a classic lemons problem. They dampen the announcement effect and cause the optimal policy to be more aggressive, leading to an earlier intervention at a larger scale.
    Keywords: Trading Dynamics, Adverse Selection, Search, Intervention in Asset Markets, Announcement Effect
    JEL: G1 E6
    Date: 2011–04

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