nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒06‒14
eight papers chosen by
Thanos Verousis

  1. A Semiparametric Intraday GARCH Model By Peter Malec
  2. Endogenous Formation of Limit Order Books: Dynamics Between Trades By Roman Gayduk; Sergey Nadtochiy
  3. The Electronic Live Cattle Futures Market Bid Ask Spread Behaviors and Components By Shang, Quanbiao; Mallory, Mindy; Garcia, Philip
  4. How brokers can optimally plot against traders By Manuel Lafond
  5. Market structure and liquidity in the U.S. Treasury and agency mortgage-backed security (MBS) markets: Mortgage Bankers Association National Secondary Market Conference and Expo, New York City, May 2016 By Wuerffel, Nathaniel
  6. The Impact of Trading Activity in Agricultural Futures Markets By Zuppiroli, Marco; Donati, Michele; Riani, Marco; Verga, Giovanni
  7. The Effect of Investor Sentiment on Gold Market Dynamics By Mehmet Balcilar; Matteo Bonato; Riza Demirer; Rangan Gupta
  8. Extreme Returns and Intensity of Trading By Gloria Gonzalez-Rivera; Wei Lin

  1. By: Peter Malec
    Abstract: We propose a multiplicative component model for intraday volatility. The model consists of a seasonality factor, as well as a semiparametric and parametric component. The former captures the well-documented intraday seasonality of volatility, while the latter two account for the impact of the state of the limit order book, utilizing an additive structure, and fluctuations around this state by means of a unit GARCH specification. The model is estimated by a simple and easy-to-implement approach, consisting of across-day-averaging, smooth-backfitting and QML steps. We derive the asymptotic properties of the three component estimators. Further, our empirical application based on high-frequency data for NASDAQ equities investigates non-linearities in the relationship between the limit order book and subsequent return volatility and underlines the usefulness of including order book variables for out-of-sample forecasting performance.
    Keywords: Intraday volatility, GARCH, smooth backfitting, additive models, limit order book.
    JEL: C14 C22 C53 C58
    Date: 2016–05–30
  2. By: Roman Gayduk; Sergey Nadtochiy
    Abstract: In this talk, we present a continuous time extension of the framework for modeling market microstructure, developed in our previous work. We use this extension to model the shape and dynamics of the Limit Order Book (LOB) between two consecutive trades. In this model, the LOB arises as an outcome of an equilibrium between multiple agents who have different beliefs about the future demand for the asset. These beliefs may change according to the information observed by the agents (e.g. represented by a relevant stochastic factor), implying a change in the shape of the LOB. This model is consistent with the empirical observation that most changes in the LOB are not due to trades. More importantly, it allows one to see how changing the relevant information signal affects the LOB. If the relevant signal is a function of the LOB itself, then, our approach allows one to model the "indirect" market impact (as opposed to the "direct" impact that a market order makes on the LOB, by eliminating certain limit orders instantaneously), showing how any change to the LOB causes further changes to it. On the mathematical side, we formulate the problem as a mixed control-stopping game, with a continuum of players. We manage to split the equilibrium problem into two parts, and represent one of them through a two-dimensional system of Reflected Backward Stochastic Differential Equations, and the other one with an infinite-dimensional fixed-point equation. We prove the existence of the solutions to both problems and show how they can be computed in a simple example.
    Date: 2016–05
  3. By: Shang, Quanbiao; Mallory, Mindy; Garcia, Philip
    Keywords: Futures market, bid ask spread, market microstructure, Agricultural Finance, Financial Economics, Livestock Production/Industries,
    Date: 2016
  4. By: Manuel Lafond
    Abstract: Traders buy and sell financial instruments in hopes of making profit, and brokers are responsible for the transaction. Some brokers, known as market-makers, take the position opposite to the trader's. If the trader buys, they sell; if the trader sells, they buy. Said differently, brokers make money whenever their traders lose money. From this somewhat strange mechanism emerge various conspiracy theories, notably that brokers manipulate prices in order to maximize their traders' losses. In this paper, our goal is to perform this evil task optimally. Assuming total control over the price of an asset (ignoring the usual aspects of finance such as market conditions, external influence or stochasticity), we show how in cubic time, given a set of trades specified by a stop-loss and a take-profit price, a broker can find a maximum loss price movement. We also study the same problem under a model of probabilistic trades. We finally look at the online trade setting, where broker and trader exchange turns, each trying to make a profit. We show that the best option for the trader is to never trade.
    Date: 2016–04
  5. By: Wuerffel, Nathaniel (Federal Reserve Bank of New York)
    Abstract: Remarks at the Mortgage Bankers Association National Secondary Market Conference and Expo, New York City.
    Keywords: Fixed income market liquidity; MBS markets; structural change; market evolution; Treasury Market Practices Group (TMPG); flash rally; trading; price efficiency; automated markets; bid-ask spreads; electronic trading; automated trading; liquidity illusion; phantom liquidity; public sector ownership
    Date: 2016–05–17
  6. By: Zuppiroli, Marco; Donati, Michele; Riani, Marco; Verga, Giovanni
    Abstract: The paper examines a causal link between trading activity and market factors as returns and volatility as well. The ratio of volume to open interest in futures contracts performs better than other parameters extensively adopted in literature. The reason probably depends on the daily frequency of information which gives statistical evidence to phenomena which conclude their effect in weekly intervals. The estimations for the contemporaneous model give statistical evidence of a mutual relationship between trading activity and realized volatility. The behaviour of all the twelve futures markets examined is quite similar and uniform respect to the scale of the coefficients and their temporal profile.
    Keywords: volatility, trading activity, commodity futures markets, agricultural commodities, Agricultural and Food Policy, G13, Q11, Q13,
    Date: 2015–06
  7. By: Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Turkey; Department of Economics, University of Pretoria, South Africa ; IPAG Business School, France); Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa); Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, USA.); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: This paper explores the effect of investor sentiment on the intraday dynamics in the gold market. Using a novel methodology to detect nonlinear causalities, we examine the effect of fear and excitement in the stock market on gold return and intraday volatility at alternative quantiles. While no significant sentiment effect is observed on daily gold returns, we find that sentiment drives intraday volatility in the gold market. Interestingly however, the sentiment effect is channeled via the discontinuous component of intraday volatility and more significantly at extreme quantiles, suggesting that extreme fear (excitement) contributes to positive (negative) volatility jumps in gold returns. The results suggest that measures of sentiment could be utilized to model volatility jumps in safe haven assets that are often hard to predict and have significant implications for risk management as well as the pricing of options.
    Keywords: Investor Sentiment, Gold Returns, Intraday Volatility
    JEL: C22 Q02
    Date: 2016–05
  8. By: Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside); Wei Lin
    Date: 2016–04

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