|
on Market Microstructure |
| By: | Sebastian Herrmann; Johannes Muhle-Karbe; Dapeng Shang; Chen Yang |
| Abstract: | We study Nash equilibria for inventory-averse high-frequency traders (HFTs), who trade to exploit information about future price changes. For discrete trading rounds, the HFTs' optimal trading strategies and their equilibrium price impact are described by a system of nonlinear equations; explicit solutions obtain around the continuous-time limit. Unlike in the risk-neutral case, the optimal inventories become mean-reverting and vanish as the number of trading rounds becomes large. In contrast, the HFTs' risk-adjusted profits and the equilibrium price impact converge to their risk-neutral counterparts. Compared to a social-planner solution for cooperative HFTs, Nash competition leads to excess trading, so that marginal transaction taxes in fact decrease market liquidity. |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1808.05169&r=mst |
| By: | Taiga Saito (Graduate School of Economics, University of Tokyo); Takanori Adachi (Graduate School of Business Administration Tokyo Metropolitan University); Teruo Nakatsuma (Department of Economics, Keio University); Akihiko Takahashi (Graduate School of Economics, University of Tokyo); Hiroshi Tsuda (Department of Mathematical Sciences, Doshisha University); Naoyuki Yoshino (Financial Services Agency, Government of Japan. ADBI Institute) |
| Abstract: | In this study, we investigate ordering patterns of different types of market participants in Tokyo Stock Exchange (TSE) by examining order records of the listed stocks. Firstly, we categorize the virtual servers in the trading system of TSE, each of which is linked to a single trading participant, by the ratio of cancellation and execution in the order placement as well as the number of executions at the opening of the afternoon session. Then, we analyze ordering patterns of the servers in the categories in short intervals for the top 10 highest trading volume stocks. By classifying the intervals into four cases by returns, we observe how different types of market participants submit or execute orders in the market situations. Moreover, we investigate the shares of the executed volumes for the different types of servers in the swings and roundabouts of the Nikkei 225 index, which were observed in September in 2015. The main findings of this study are as follows: Server type A, which supposedly includes non-market making proprietary traders with high-speed algorithmic strategies, executes and places orders along with the direction of the market. The shares of the execution and order volumes along with the market direction increase when the stock price moves sharply. Server type B, which presumably includes servers employing a market making strategy with high cancellation and low execution ratio, shifts its market making price ranges in the rapid price movements. We observe that passive servers in Server type B have a large share and buy at low levels in the price falls. Also, Server type B, as well as Server type A, makes profit in the price falling days and particularly, the aggressive servers in the server type make most of the profit. Server type C, which is assumed to include servers receiving orders from small investors, constantly has a large share of execution and order volume. |
| Date: | 2018–06 |
| URL: | http://d.repec.org/n?u=RePEc:cfi:fseres:cf438&r=mst |
| By: | Ben Hambly; Jasdeep Kalsi; James Newbury |
| Abstract: | Motivated by a zero-intelligence approach, the aim of this paper is to connect the microscopic (discrete price and volume), mesoscopic (discrete price and continuous volume) and macroscopic (continuous price and volume) frameworks for the modelling of limit order books, with a view to providing a natural probabilistic description of their behaviour in a high to ultra high-frequency setting. Starting with a microscopic framework, we first examine the limiting behaviour of the order book process when order arrival and cancellation rates are sent to infinity and when volumes are considered to be of infinitesimal size. We then consider the transition between this mesoscopic model and a macroscopic model for the limit order book, obtained by letting the tick size tend to zero. The macroscopic limit can then be described using reflected SPDEs which typically arise in stochastic interface models. We then use financial data to discuss a possible calibration procedure for the model and illustrate numerically how it can reproduce observed behaviour of prices. This could then be used as a market simulator for short-term price prediction or for testing optimal execution strategies. |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1808.07107&r=mst |
| By: | Selma Boussetta; Laurance Lescourret; Sophie Moinas |
| Abstract: | Liquidity issues in financial markets arise because of two main factors: asymmetric information and cost of market participation. To alleviate these frictions, several exchanges start with a pre-opening period characterized by the accumulation of orders and the absence of trading. What is the role of Euronext’s pre-opening mechanism in the price discovery and liquidity formation of the exchange itself versus two other competing venues deprived of such a mechanism, namely BATS and Chi-X? EconPol expert Sophie Moinas (TSE) and her co-authors find evidence that tentative clearing prices set during the pre-opening period contribute to discover opening price; and that tentative clearing volume is positively correlated with liquidity across all three platforms. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:ces:econwp:_12&r=mst |
| By: | Taiga Saito (Graduate School of Economics, The University of Tokyo); Akihiko Takahashi (Graduate School of Economics, The University of Tokyo) |
| Abstract: | This paper presents an application of a linear quadratic stochastic differential game to a model in finance, which describes trading behaviors of different types of players in a high frequency stock market. Stability of the high frequency market is a central issue for financial markets. Building a model that expresses the trading behaviors of the different types of players and the price actions in turmoil is important to set regulations to maintain fair markets. Firstly, we represent trading behaviors of the three types of players, algorithmic traders, general traders, and market makers as well as the mid-price process of a risky asset by a linear quadratic stochastic differential game. Secondly, we obtain a Nash equilibrium for open loop admissible strategies by solving a forward-backward stochastic differential equation (FBSDE) derived from the stochastic maximum principle. Finally, we present numerical examples of the Nash equilibrium for open loop admissible strategies and the corresponding price action of the risky asset, which agree with the empirical findings on trading behaviors of players in high frequency markets. This model can be used to investigate the impact of regulation changes on the market stability as well as trading strategies of the players. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:cfi:fseres:cf437&r=mst |
| By: | Max Ole Liemen (Universität Hamburg); Michel van der Wel (Erasmus University Rotterdam); Olaf Posch (Universität Hamburg) |
| Abstract: | In this paper we show how high-frequency financial data can be used in a combined macro-finance framework to estimate the underlying structural parameters. Our formulation of the model allows for substituting macro variables by asset prices in a way that enables casting the relevant estimation equations partly (or completely) in terms of financial data. We show that using only financial data allows for identification of the majority of the relevant parameters. Adding macro data allows for identification of all parameters. In our simulation study, we find that it also improves the accuracy of the parameter estimates. In the empirical application we use interest rate, macro, and S&P500 stock index data, and compare the results using different combinations of macro and financial variables. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed018:1049&r=mst |
| By: | Klova, Valeriia (University of Stavanger); Odegaard, Bernt Arne (University of Stavanger) |
| Abstract: | Equity trading costs have been argued to have fallen to extreme lows following the introduction of automated trading. The justification is a fall in estimates of spreads, such as closing and effective spreads. A spread is however measured conditional on an expected transaction size. If transaction sizes falls, spreads fall, without necessarily implying a lowering in trading costs. We argue that much of the fall in spreads is driven by the fall in transaction size following the automation of trading. Alternative estimators of transaction costs less sensitive to trade size, such as the the Lesmond, Ogden and Trzcinka (1999), Corwin and Schultz (2012) and Abdi and Ranaldo (2017) estimators, show much less of a downward trend. Using transaction by transaction data for the Norwegian equity market for the period 1999 to 2016, we show that the lowering of spreads is driven by the decline in order sizes. |
| Keywords: | Equity Trading Costs; Spread; High/Low Estimator |
| JEL: | G10 G12 G20 G23 |
| Date: | 2018–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:stavef:2018_004&r=mst |
| By: | Karnaukh, Nina (OH State U) |
| Abstract: | I find that the U.S. dollar appreciates over the two-day period before contractionary monetary policy decisions at scheduled Federal Open Market Committee (FOMC) meetings and depreciates over the two-day period before expansionary monetary policy decisions. The federal funds futures rate forecasts these dollar movements with a 22% R^{2}. A high federal funds futures spread three days in advance of an FOMC meeting not only predicts the target rate rise, but also predicts a rise in the dollar over the subsequent two-day period. A simple trading strategy, which exploits this predictability, exhibits a 0.93 Sharpe ratio. My findings imply that information about monetary policy changes is reflected first in the fixed income markets, and only later becomes reflected in currency markets. |
| JEL: | E52 F31 G12 G17 |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:ohidic:2018-14&r=mst |