New Economics Papers
on Market Microstructure
Issue of 2014‒06‒28
seven papers chosen by
Thanos Verousis

  1. Survival Models for the Duration of Bid-Ask Spread Deviations By Efstathios Panayi; Gareth Peters
  2. The impact of information flow and trading activity on gold and oil futures volatility By Adam Clements; Neda Todorova
  3. Mandatory portfolio disclosure, stock liquidity, and mutual fund performance By Agarwal, Vikas; Mullally, Kevin Andrew; Tang, Yuehua; Yang, Baozhong
  4. To disclose or not to disclose: Transparency and liquidity in the structured product market By Friewald, Nils; Jankowitsch, Rainer; Subrahmanyam, Marti G.
  5. Liquidity commonality does not imply liquidity resilience commonality: A functional characterisation for ultra-high frequency cross-sectional LOB data By Efstathios Panayi; Gareth Peters; Ioannis Kosmidis
  6. Using an Artificial Financial Market for studying a Cryptocurrency Market By Luisanna Cocco; Giulio Concas; Michele Marchesi
  7. The role in index jumps and cojumps in forecasting stock index volatility: Evidence from the Dow Jones index By Adam Clements; Yin Liao

  1. By: Efstathios Panayi; Gareth Peters
    Abstract: Many commonly used liquidity measures are based on snapshots of the state of the limit order book (LOB) and can thus only provide information about instantaneous liquidity, and not regarding the local liquidity regime. However, trading in the LOB is characterised by many intra-day liquidity shocks, where the LOB generally recovers after a short period of time. In this paper, we capture this dynamic aspect of liquidity using a survival regression framework, where the variable of interest is the duration of the deviations of the spread from a pre-specified level. We explore a large number of model structures using a branch-and-bound subset selection algorithm and illustrate the explanatory performance of our model.
    Date: 2014–06
  2. By: Adam Clements (QUT); Neda Todorova (GU)
    Abstract: There is a long history of research into the impact of trading activity and information on financial market volatility. Based on 10 years of unique data on news items relating to gold and crude oil broadcast over the Reuters network, this study has two objectives. It investigates the impact of shocks in trading activity and traders positions which are unrelated to information flows on realized volatility. Additionally, the extent to which the volume of the information flow as well as the sentiment inherent in the news affects volatility is also examined. Both the sentiment and rate of news flow are found to influence volatility, with unexpected positive shocks to the rate of news arrival, and negative shocks to the sentiment of news flow exhibiting the largest impacts. While volatility is also related to measures of trading activity, their influence decreases after news is accounted for indicating that a non-negligible component of trading is in response to public news flow. After controlling for the level of trading activity and news flow, the net positions of the various types of traders play no role, implying that no single group of traders lead to these markets being more volatile.
    Keywords: Information flow; Volatility; Oil futures; Gold futures; Trading activity.
    JEL: C22 G10 G13 G14
    Date: 2014–06–17
  3. By: Agarwal, Vikas; Mullally, Kevin Andrew; Tang, Yuehua; Yang, Baozhong
    Abstract: We examine the impact of mandatory portfolio disclosure by mutual funds on stock liquidity and fund performance. We develop a model of informed trading with disclosure and test its predictions using the SEC regulation in May 2004 requiring more frequent disclosure. Stocks with higher fund ownership, especially those held by more informed funds or subject to greater information asymmetry, experience larger increases in liquidity after the regulation change. More informed funds, especially those holding stocks with greater information asymmetry, experience greater performance deterioration after the regulation change. Overall, mandatory disclosure improves stock liquidity but imposes costs on informed investors. --
    Date: 2014
  4. By: Friewald, Nils; Jankowitsch, Rainer; Subrahmanyam, Marti G.
    Abstract: We use a unique data set from the Trade Reporting and Compliance Engine (TRACE) to study liquidity e ffects in the US structured product market. Our main contribution is the analysis of the relation between the accuracy in measuring liquidity and the potential degree of disclosure. Having access to all relevant trading information, we provide evidence that transaction cost measures that use dealer specific information such as trader identity and trade direction can be efficiently proxied by measures that use less detailed information. This finding is important for all market participants in the context of OTC markets, as it fosters our understanding of the information contained in transaction data. Thus, our results provide guidance for improving transparency while maintaining trader confidentiality. In addition, we analyze liquidity in the structured product market in general and show that securities that are mainly institutionally traded, guaranteed by a federal authority, or have low credit risk, tend to be more liquid. --
    Keywords: liquidity,structured products,OTC markets,transparency,TRACE
    JEL: G12 G14
    Date: 2014
  5. By: Efstathios Panayi; Gareth Peters; Ioannis Kosmidis
    Abstract: We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of explanatory power of the dominant modes of variation of liquidity (extracted through Principal Component Analysis) fails to account for heavy tailed features in the data, thus producing potentially misleading results. We employ Independent Component Analysis, which both decorrelates the liquidity measures in the asset cross-section, but also reduces higher-order statistical dependencies. To measure commonality in liquidity resilience, we utilise a novel characterisation as the time required for return to a threshold liquidity level. This reflects a dimension of liquidity that is not captured by the majority of liquidity measures and has important ramifications for understanding supply and demand pressures for market makers in electronic exchanges, as well as regulators and HFTs. When the metric is mapped out across a range of thresholds, it produces the daily Liquidity Resilience Profile (LRP) for a given asset. This daily summary of liquidity resilience behaviour from the vast LOB dataset is then amenable to a functional data representation. This enables the comparison of liquidity resilience in the asset cross-section via functional linear sub-space decompositions and functional regression. The functional regression results presented here suggest that market factors for liquidity resilience (as extracted through functional principal components analysis) can explain between 10 and 40% of the variation in liquidity resilience at low liquidity thresholds, but are less explanatory at more extreme levels, where individual asset factors take effect.
    Date: 2014–06
  6. By: Luisanna Cocco; Giulio Concas; Michele Marchesi
    Abstract: This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed with a finite amount of crypto and/or fiat cash and issues buy and sell orders, according to her strategy and resources. The number of Bitcoins increases over time with a rate proportional to the real one, even if the mining process is not explicitly modelled. The model proposed is able to reproduce some of the real statistical properties of the price absolute returns observed in the Bitcoin real market. In particular, it is able to reproduce the autocorrelation of the absolute returns, and their cumulative distribution function. The simulator has been implemented using object-oriented technology, and could be considered a valid starting point to study and analyse the cryptocurrency market and its future evolutions.
    Date: 2014–06
  7. By: Adam Clements (QUT); Yin Liao (QUT)
    Abstract: Modeling and forecasting realized volatility is of paramount importance. Previous studies have examined the role of both the continuous and jump components of volatility in forecasting. This paper considers how to use index level jumps and cojumps across index constituents for forecasting index level volatility. In combination with the magnitude of past index jumps, the intensity of both index jumps and cojumps are examined. Estimated jump intensity from a point process model is used within a forecasting regression framework. Even in the presence of the diffusive part of total volatility, and past jump size, intensity of both index and cojumps are found to significantly improve forecast accuracy. An important contribution is that information relating to the behaviour of underlying constituent stocks is useful for forecasting index level behaviour. Improvements in forecast performance are particularly apparent on the days when jumps or cojumps occur, or when markets are turbulent.
    Keywords: Realized volatility; diffusion; jumps; point process; Hawkes process; forecasting
    JEL: C22 G00
    Date: 2014–06–17

This issue is ©2014 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.