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on Market Microstructure |
| By: | Adrian, Tobias; Capponi, Agostino; Vogt, Erik; Zhang, Hongzhong |
| Abstract: | The Treasury market is increasingly intermediated by non-bank proprietary trading firms. These firms differ notably from incumbent dealers in that they tend to unwind inventories at the end of the day. To shed light on the impact these new intermediaries have on market quality, we model a market making proprietary trading firm that faces overnight inventory costs. The resulting inventory hedging demand generates rising price impact and widening bid-ask spreads as the end of the trading day approaches. These predictions are borne out in the U.S. Treasury data. |
| Keywords: | Financial Intermediation; market liquidity; market making; Market microstructure theory |
| JEL: | G01 G12 G17 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:12245&r=mst |
| By: | Adrian, Tobias; Fleming, Michael J; Shachar, Or; Vogt, Erik |
| Abstract: | This paper examines market liquidity in the post-crisis era in light of concerns that regulatory changes might have reduced dealers' ability and willingness to make markets. We begin with a discussion of the broader trading environment, including an overview of regulations and their potential effects on dealer balance sheets and market making, but also considering additional drivers of market liquidity. We document a stagnation of dealer balance sheets after the financial crisis of 2007-09, which occurred concurrently with dealer balance sheet deleveraging. However, using high-frequency trade and quote data for U.S. Treasuries and corporate bonds, we find only limited evidence of a deterioration in market liquidity. |
| Keywords: | corporate bonds; liquidity; market making; regulation; Treasury securities |
| JEL: | G12 G21 G28 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:12248&r=mst |
| By: | Worapree Maneesoonthorn; Gael M. Martin; Catherine S. Forbes |
| Abstract: | This paper provides an extensive evaluation of high frequency jump tests and measures, in the context of dynamic models for asset price jumps. Specifically, we investigate: i) the power of alternative tests to detect individual price jumps, including in the presence of volatility jumps; ii) the frequency with which sequences of dynamic jumps are identified; iii) the accuracy with which the magnitude and sign of sequential jumps are estimated; and iv) the robustness of inference about dynamic jumps to test and measure design. Substantial differences are discerned in the performance of alternative methods in certain dimensions, with inference being sensitive to these differences in some cases. Accounting for measurement error when using measures constructed from high frequency data to conduct inference on dynamic jump models would appear to be advisable. |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1708.09520&r=mst |
| By: | Kestutis Baltakys; Juho Kanniainen; Frank Emmert-Streib |
| Abstract: | Investor trading networks are gaining rapid interest in financial market studies. In this paper, we propose three improvements for investor trading network analyses: investor categorization, transaction bootstrapping and information aggregation. Each of these components can be used individually or in combination. We introduce a tractable multilayer aggregation procedure to summarize security-wise and time-wise information integration of investor category trading networks. As an application, we analyze the unique dataset of Finnish shareholders throughout 2004-2009. We find that households play a central role in investor networks, having the most synchronized trading. Furthermore, we observe that the window size used for averaging has a substantial effect on the number of inferred relationships. However, the relative node centrality in the networks is rather stable. We would like to note that the use of our proposed aggregation framework is not limited to the field of investor trading networks. It can be used for different non-financial applications, with both observable and inferred relationships, that span over a number of different information layers. |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1708.09850&r=mst |