nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒09‒14
seven papers chosen by
Thanos Verousis

  1. Price Discovery for Options By Semyon Malamud; Michael Tseng; Yuan Zhang
  2. Image Processing Tools for Financial Time Series Classification By Bairui Du; Paolo Barucca
  3. Adaptive trading strategies across liquidity pools By Bastien Baldacci; Iuliia Manziuk
  4. Analysis of the effect of restrictions on net short positions on Spanish shares between March and May 2020 By Ramiro Losada; Albert Martinez
  5. On the Efficiency of Foreign Exchange Markets in times of the COVID-19 Pandemic By Aslam, Faheem; Aziz, Saqib; Nguyen, Duc Khuong; Mughal, Khurram S.; Khan, Maaz
  6. How Does the Liquidity of New Treasury Securities Evolve? By Michael J. Fleming
  7. Fixed Income Market Structure: Treasuries vs. Agency MBS By James Collin Harkrader; Michael Puglia

  1. By: Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Michael Tseng (University of Central Florida); Yuan Zhang (Ecole Polytechnique Fédérale de Lausanne)
    Abstract: We consider a market where traders have asymmetric information regarding the distribution of asset return and study price discovery of derivatives. The informed trader has private information regarding arbitrary higher moments of asset return, such as volatility or skewness, and exploits her private information by trading a complete menu of options. The equilibrium trading strategies of the informed agent in our model reflect those used by traders in the market when trying to exploit higher order moment information, such as the volatility straddle.
    Keywords: Options, Price Discovery, Volatility Straddle
    JEL: G14
    Date: 2020–08
  2. By: Bairui Du; Paolo Barucca
    Abstract: Time series prediction is a challenge for many complex systems, yet in finance predictions are hindered by the very nature of how financial markets work. In efficient markets, the opportunities for stock price predictions leading to profitable trades are supposed to rapidly disappear. In the growing industry of high-frequency trading, the competition over extracting predictions on stock prices from the increasing amount of available information for performing profitable trades is becoming more and more severe. With the development of big data analysis and advanced deep learning methodologies, traders hope to fruitfully analyse market information, e.g. price time series, through machine learning. Spot prices of stocks provide a simple snapshot representation of a financial market. Stock prices fluctuate over time, affected by numerous factors, and the prediction of their changes is at the core of both long-term and short-term financial investing. The collective patterns of price movements are generally referred to as market states. As a paramount example, when stock prices follow an upward trend, it is called a bull market, and when stock prices follow a downward trend is called a bear market
    Date: 2020–08
  3. By: Bastien Baldacci; Iuliia Manziuk
    Abstract: In this article, we provide a flexible framework for optimal trading in an asset listed on different venues. We take into account the dependencies between the imbalance and spread of the venues, and allow for partial execution of limit orders at different limits as well as market orders. We present a Bayesian update of the model parameters to take into account possibly changing market conditions and propose extensions to include short/long trading signals, market impact or hidden liquidity. To solve the stochastic control problem of the trader we apply the finite difference method and also develop a deep reinforcement learning algorithm allowing to consider more complex settings.
    Date: 2020–08
  4. By: Ramiro Losada; Albert Martinez
    Abstract: This article analyzes what the cost may have been, in terms of market efficiency, of the ban on creating or increasing net short positions on the most liquid securities traded in the Spanish markets, which partially entered into force on 13 March 2020 and was then applied continuously from 17 March to 18 May. Specifically, the impact on some liquidity measures (such as the bid-ask spread, trading volume or the Amihud measure) is analyzed, as well as the impact on returns and intraday volatility of prices. Another objective is to assess whether the ban could have influenced the credit risk of financial and non-financial issuers whose securities are listed on equity markets. To perform the analysis, a study was made of variables related to the returns, vola¬tilities and liquidity measures of the shares listed on the stock exchanges that made up the Ibex 35 index in Spain and those that form part of the German Dax 30. The German index was chosen for this analysis, firstly, because its financial markets regulator did not adopt the decision to restrict short trades and, secondly, because the trends marked by prices, volatilities and liquidity measures during the period prior to the implementation of the measure in Spain were similar in the financial markets of both countries.From both the descriptive and econometric analyses it can be deduced that the securities included in the ban experienced a larger drop in liquidity (as measured by the bid-ask spread) compared to the unrestricted scenario, an impact which persisted when the ban was lifted, albeit to a lesser degree. However, there is no evidence of other effects derived from the ban on other relevant variables as the trading volumes, the price evolution, volatility, market depth or the issuers´credit spreads.
    Date: 2020
  5. By: Aslam, Faheem; Aziz, Saqib; Nguyen, Duc Khuong; Mughal, Khurram S.; Khan, Maaz
    Abstract: We employ multifractal detrended fluctuation analysis (MF-DFA) to provide the first look at the efficiency of forex markets during the initial period of ongoing COVID-19 pandemic, which has disrupted the financial markets globally. We use high frequency (5-min interval) data of six major currencies traded in the forex market for the period from 01 October 2019 to 31 March 2020. Prior to the application of MF-DFA, we examine the inner dynamics of multifractality using seasonal-trend decompositions using loess (STL) method. Overall, the results confirm the presence of multifractality in forex markets, which demonstrates, in particular: (i) a decline in the efficiency of forex markets during the period of COVID-19 outbreak, and (ii) the heterogeneity in the effects on the strength of multifractality of exchange rate returns under investigation. The largest effect is observed in the case of AUD as it shows the highest (lowest) efficiency before (during) COVID-19 assessed in terms of low (high) multifractality. During COVID-19 period, CAD and CHF exhibit the highest efficiency. Our findings may help policymakers in shaping a comprehensive response to improve the forex market efficiency during such a black swan event.
    Keywords: COVID-19 pandemic; forex market; MF-DFA; high frequency; efficiency
    JEL: C10 C32 G10 G15
    Date: 2020–05
  6. By: Michael J. Fleming
    Abstract: In a recent Liberty Street Economics post, we showed that the newly reintroduced 20-year bond trades less than other on-the-run Treasury securities and has similar liquidity to that of the more interest‑rate‑sensitive 30-year bond. Is it common for newly introduced securities to trade less and with higher transaction costs, and how does security trading behavior change over time? In this post, we look back at how liquidity evolved for earlier reintroductions of Treasury securities so as to gain insight into how liquidity might evolve for the new 20-year bond.
    Keywords: Treasury; liquidity; reintroduction
    JEL: G1
    Date: 2020–08–26
  7. By: James Collin Harkrader; Michael Puglia
    Abstract: This FEDS Note analyzes the structure of the agency mortgage-backed securities (MBS) market through the lens of the TRACE Treasury data initiative, which is a significant component of a broader inter-agency effort to enhance understanding and transparency of the Treasury securities market. As in several previous FEDS Notes describing the Treasury cash market structure, this note uses transactions reported to the Financial Industry Regulatory Authority (FINRA)'s Trade Reporting and Compliance Engine (TRACE) to examine aggregate trading volumes in the agency MBS market across venues, security types and participants. We show how agency MBS provide a useful counterfactual to cash Treasuries when analyzing the evolution of Treasury cash market structure and its implications for liquidity. We provide evidence that the participation of Principal Trading Firms (PTFs) in Treasury markets has caused the overall volume of intermediation to rise there, particularly in the interdealer broker (IDB) venue. We also find that, relative to Treasury markets, intermediation in the agency MBS market is concentrated among fewer firms, and in particular the primary dealers, suggesting that PTF participation in Treasury markets has diversified intermediation in the IDB venue across a larger number of firms.

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