nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒12‒04
seven papers chosen by
Thanos Verousis


  1. Bond Liquidity at the Oslo Stock Exchange By Ødegaard, Bernt Arne
  2. Informed Trading in Oil-Futures Market By Rousse, Olivier; Sévi, Benoît
  3. Strategic Fragmented Markets By Cecilia Parlatore; Ana Babus
  4. Auctions for Intraday -Trading Impacts on efficient power markets and secure system operation By Neuhoff, Karsten; Richstein, Jörn; May, Nils
  5. "Risks and Returns of Trades: A case of Mitsubishi Corporation in the Prewar Period " (in Japanese) By Tetsuji Okazaki
  6. Institutional Herding and Its Price Impact : Evidence from the Corporate Bond Market By Fang Cai; Song Han; Dan Li; Yi Li
  7. A coupled component GARCH model for intraday and overnight volatility By Linton, O.; Wu, J.

  1. By: Ødegaard, Bernt Arne (UiS)
    Abstract: We characterize the liquidity of bond trading at the Oslo Stock Exchange (OSE). We use the complete history of bond prices quoted at the OSE from 1990 to 2015. We first characterize the market place, summarize trading grouped by type of issuers. The OSE can be characterized as a market place with a few bonds traded often, the rest traded seldom. The active bonds are Treasury securities, which typically trade on a daily basis. A second category of active bonds are \emph{covered bonds}, a type of bond introduced as recent as 2008 (in the wake of the financial crisis). The remainder of bonds at the OSE are traded seldom. The activity of the bond market at the OSE has increased markedly in the post-2008 period. While Treasury securities remain the most active class, covered bonds has seen a marked increase in liquidity. We also see an increase in activity for the other bond groups. The number of bonds listed has doubled in the last ten years, with financial and industrial issuers increasing the most. The market had more than 3000 different bond issues active in the last five years. However, only half of these bonds trade more than five times a year. The second part of the paper investigates the feasibility of measuring liquidity in the Norwegian bond market. Is it possible to construct liquidity measures that are informative about the state of the Norwegian financial market? We calculate three different measures that can be calculated from daily data: Bid/Ask Spreads, the Amihud [2002] ILLIQ measure, and the Corwin and Schultz [2012] spread estimate from high/low prices. Except for Treasuries, the liquidity measures are hard to calculate due to limited trading interest. Of the three liquidity measures, the Corwin and Schultz measure seem to be the preferred, although the measures are clearly correlated. All measures show that aggregate bond market liquidity covary with slowdowns in the Norwegian economy, with liquidity worsening (trading costs/spreads increasing) around such events as the 1992 Banking Crisis and the 2008 Financial Crisis. We also compare estimates of trading costs for various types of bonds with equities, and find that the most expensive to trade is equities. Trading costs for corporate bonds are lower than equities, but higher than Treasury bonds, which is the category with lowest estimated transaction costs. This is contrary to the evidence from the US, and most European bond markets, where estimates of transaction costs for corporate bonds are much higher than trading costs for equities.
    Keywords: Bond Markets; Liquidity; Trading Costs; Oslo Stock Exchange
    JEL: G10 G20
    Date: 2016–11–23
    URL: http://d.repec.org/n?u=RePEc:hhs:stavef:2016_016&r=mst
  2. By: Rousse, Olivier; Sévi, Benoît
    Abstract: The weekly release of the U.S. inventory level by the DOE-EIA is known as the market mover in the U.S. oil futures market and to be a significant piece of information for all world oil markets in which the WTI is a price benchmark. We uncover suspicious trading patterns in the WTI futures markets in days when the inventory level is released that are higher than economists’ forecasts: there are significantly more orders initiated by buyers in the two hours preceding the official release of the inventory level. We also show a clear drop in the average price of -0.25% ahead of the news release. This is consistent with informed trading. We also provide evidence of an asymmetric response of the oil price to the news, and highlight an over-reaction that is partly compensated in the hours following the announcement.
    Keywords: Insider Trading, WTI Crude Oil Futures, Intraday Data, Inventory Release, Financial Economics, G13, G14, Q4,
    Date: 2016–11–23
    URL: http://d.repec.org/n?u=RePEc:ags:feemes:249788&r=mst
  3. By: Cecilia Parlatore (New York University Stern); Ana Babus (Chicago FED)
    Abstract: We propose a theory of fragmentation in asset markets. We develop a model of market formation in which investors with heterogeneous valuations trade an asset strategically. Investors choose a dealer with whom to trade. After the market structure is decided, trade takes place sequentially. First, each dealer and his investors trade strategically in a local market. Second, dealers participate in a strategic inter-dealer market. Markets are fragmented when there are multiple active dealers. In contrast, the market is centralized if all investors choose to trade with the same dealer. In equilibrium, market fragmentation depends on the dispersion of the investors' valuations for the asset and on the dealers' opportunities to intermediate through the inter-dealer market. Increasing the number of market participants in the local market always decreases the investors' price impact and, thus, their cost of trading. At the same time, it also decreases the investors' gains from trade when their valuations are less dispersed. This second effect dominates when the dealers' willingness to intermediate is low. We show that investors choose to trade in fragmented markets when their valuations are highly correlated and when intermediation is limited. We compare investors' and dealers' welfare in fragmented and centralized markets.
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:red:sed016:1582&r=mst
  4. By: Neuhoff, Karsten; Richstein, Jörn; May, Nils
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:zbw:esrepo:148282&r=mst
  5. By: Tetsuji Okazaki (Faculty of Economics, University of Tokyo)
    Abstract: This paper explores the modes of trading of a trading company and their implications on the risks and returns of trades, focusing on Mitsubishi Corporation in the 1920s. Mitsubishi employed two modes of trades, i.e. proprietary trading and consignment trading, and the former trades accounted for 43.8% of the total trades in 1928. From the original account book of Mitsubishi, I compiled a dataset containing the information of sales and margins at the individual transaction-level. It is revealed that there is substantial difference in the distributions of margin rates between proprietary trading and consignment trading. The distribution of margin rates of the proprietary trading has fat tails both on the left and right sides, and the average margin rate is significantly higher than that of consignment trades. As expected, proprietary trading yielded high risk and high return. The findings of this paper suggests further issues to be explored, including the choice of modes of trading by Mitsubishi and the mechanisms that Mitsubishi controlled the risk accompanying transactions by its own account.
    Date: 2016–11
    URL: http://d.repec.org/n?u=RePEc:tky:jseres:2015cj282&r=mst
  6. By: Fang Cai; Song Han; Dan Li; Yi Li
    Abstract: Among growing concerns about potential financial stability risks posed by the asset management industry, herding has been considered as an important risk amplification channel. In this paper, we examine the extent to which institutional investors herd in their trading of U.S. corporate bonds and quantify the price impact of such herding behavior. We find that, relative to what is documented for the equity market, the level of institutional herding is much higher in the corporate bond market, particularly among speculative-grade bonds. In addition, mutual funds have become increasingly likely to herd when they sell, a trend not observed among insurance companies and pension funds. We also show that bond investors herd not only within a quarter, but also over adjacent quarters. Such persistence in trading is largely driven by funds imitating the trading behavior of other funds in the previous quarter. Finally, we find that there is an asymmetry in the price impact of herding. While buy herding is associated with a permanent price impact that is consistent with price discovery, sell herding results in transitory yet significant price distortions. The price destabilizing effect of sell herding is particularly strong for high-yield bonds, small bonds, and illiquid bonds and during the recent global financial crisis.
    Keywords: Corporate Bond ; Herding ; Institutional Investors ; Liquidity ; Return Reversal
    JEL: G01 G02 G12 G14 G20
    Date: 2016–10
    URL: http://d.repec.org/n?u=RePEc:fip:fedgfe:2016-91&r=mst
  7. By: Linton, O.; Wu, J.
    Abstract: We propose a semi-parametric coupled component GARCH model for intraday and overnight volatility that allows the two periods to have different properties. To capture the very heavy tails of overnight returns, we adopt a dynamic conditional score model with t innovations. We propose a several step estimation procedure that captures the nonparametric slowly moving components by kernel estimation and the dynamic parameters by t maximum likelihood. We establish the consistency and asymptotic normality of our estimation procedures. We extend the modelling to the multivariate case. We apply our model to the study of the component stocks of the Dow Jones industrial average over the period 1991-2016. We show that actually overnight volatility has increased in importance during this period. In addition, our model provides better intraday volatility forecast since it takes account of the full dynamic consequences of the overnight shock and previous ones.
    Date: 2016–12–01
    URL: http://d.repec.org/n?u=RePEc:cam:camdae:1671&r=mst

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