nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒07‒23
five papers chosen by
Thanos Verousis


  1. Optimal Liquidation Strategy Across Multiple Exchanges under a Jump-Diffusion Fast Mean-Reverting Model By Qing-Qing Yang; Wai-Ki Ching; Jia-Wen Gu; Tak-Kuen Siu
  2. A Principal-Agent Model of Trading Under Market Impact -Crossing networks interacting with dealer markets- By Jana Bielagk; Ulrich Horst; Santiago Moreno--Bromberg
  3. Dealer Networks By Dan LI; Norman SCHUERHOFF
  4. Trading with Small Price Impact By Ludovic MOREAU; Johannes MUHLE-KARBE; Halil Mete SONER
  5. Commonality in Liquidity and Real Estate Securities By Martin HOESLI; Anjeza KADILLI; Kustrim REKA

  1. By: Qing-Qing Yang; Wai-Ki Ching; Jia-Wen Gu; Tak-Kuen Siu
    Abstract: The appearance of new trading destinations facilitates trading the same financial instrument simultaneously in different venues. To execute a large order, market participants may need to make decisions about how to split the order across multiple venues and at what prices to post the limit orders during the trading horizon to control the overall trade off between market impact and market risk. The decisions are influenced by traders' risk aversions and the micro-structural market impact. We adopt a similar quantitative model framework as in Avellaneda and Stoikov (2008) to study the optimal liquidation problem with limit and market orders across multiple venues. A two-point jump-diffusion model with fast mean-reverting stochastic volatility is employed to describe the dynamics of the underlying stock price. In the case of a single trading venue, we derive an optimal split between market and limit orders as well as the optimal quoting strategy for the orders posted to the limit order book. For the general case of multiple exchanges, we derive an optimal order allocation strategy characterized by different rebate rates, execution risks and micro-structural features.
    Date: 2016–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1607.04553&r=mst
  2. By: Jana Bielagk; Ulrich Horst; Santiago Moreno--Bromberg
    Abstract: We use a principal-agent model to analyze the structure of a book-driven dealer market when the dealer faces competition from a crossing network or dark pool. The agents are privately informed about their types (e.g. their portfolios), which is something that the dealer must take into account when engaging his counterparties. Instead of trading with the dealer, the agents may chose to trade in a crossing network. We show that the presence of such a network results in more types being serviced by the dealer and that, under certain conditions and due to reduced adverse selection effects, the book's spread shrinks. We allow for the pricing on the dealer market to determine the structure of the crossing network and show that the same conditions that lead to a reduction of the spread imply the existence of an equilibrium book/crossing network pair.
    Date: 2016–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1607.04047&r=mst
  3. By: Dan LI (Federal Reserve Board); Norman SCHUERHOFF (University of Lausanne and Swiss Finance Institute and CEPR)
    Abstract: Dealers in over-the-counter securities form networks to mitigate search frictions. The audit trail for municipal bonds shows the dealer network has a core-periphery structure. Central dealers are more efficient at matching buyers and sellers than peripheral dealers, which shortens intermediation chains and speeds up trading. Investors face a tradeoff between execution speed and cost. Central dealers provide immediacy by pre-arranging fewer trades and holding larger inventory. However, trading costs increase strongly with dealer centrality. Investors with strong liquidity need trade with central dealers and at times of market-wide illiquidity. Central dealers thus serve as liquidity providers of last resort.
    Keywords: Municipal bonds, over-the-counter financial market, trading cost, liquidity, immediacy, transparency, decentralization, market quality, network analysis
    JEL: G12 G14 G24
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp1450&r=mst
  4. By: Ludovic MOREAU (ETH Zurich); Johannes MUHLE-KARBE (ETH Zurich and Swiss Finance Institute); Halil Mete SONER (ETH Zurich and Swiss Finance Institute)
    Abstract: An investor trades a safe and several risky assets with linear price impact to maximize expected utility from terminal wealth. In the limit for small impact costs, we explicitly determine the optimal policy and welfare, in a general Markovian setting allowing for stochastic market, cost, and preference parameters. These results shed light on the general structure of the problem at hand, and also unveil close connections to optimal execution problems and to other market frictions such as proportional and fixed transaction costs.
    Keywords: price impact, portfolio choice, asymptotics, homogenization
    JEL: G11
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp1417&r=mst
  5. By: Martin HOESLI (University of Geneva, University of Aberdeen, and Kedge Business School); Anjeza KADILLI (University of Geneva); Kustrim REKA (University of Geneva)
    Abstract: We conduct an empirical investigation of the pricing and economic sources of commonality in liquidity in the U.S. REIT market. Taking advantage of the specific characteristics of REITs, we analyze three types of commonality in liquidity: within-asset commonality, cross-asset commonality (with the stock market), and commonality with the underlying property market. We find evidence that the three types of commonality in liquidity are priced in REIT returns but only during bad market conditions. We also find that using a linear approach, rather than a conditional, would have underestimated the role of commonality in liquidity risk. This explains (at least partly) the small impact of commonality on asset prices documented in the extant literature. Finally, our analysis of the determinants of commonality in liquidity favors a demand-side explanation.
    Keywords: Real Estate Securities; REITs; Commonality in Liquidity; Liquidity Risk; Asset Pricing; Threshold Regression; Panel Data
    JEL: G12 G01 G02
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp1430&r=mst

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