FE 570: Market Microstructure
Spring 2021: Group Project
Introduction
The course project is a team project, and you will work in teams of 3-4 people. The deliverable of the project is a report (∼ 5 pages, not including code), containing:
1. Expository part: explain the project topic and theoretical background
2. Data: describe the source and structure of data, and how it is analyzed and processed.
3. Results obtained. Describe the results.
Important Dates
Milestone
Team Formation
Data
12-Mar
Project Proposal
26-Mar
Final Report
Project Presentation
4-May
4-May [in class]
Deliverables
Submit as text on Canvas. Include the names for
the members of the team.
One-page summary of proposed project. Submit
in Canvas. One per group.
Submit in Canvas. One per group.
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Project List
Choose one project from this list.
Project 1. SHIFT project. Build a trading system in SHIFT using a High Frequency
Trading strategy of one of these types: i) market maker (buy low, sell high), ii) liquidity
trader (place limit orders to gain rebates, trade at market when profitable), iii) arbitrage
trader (using pair trading). You can also follow Technical Analysis strategies.
Project 2. Empirical study of microstructure data: liquidity measures and market impact. Using the trade and quote (TAQ) data for a stock of your choice for one hour from
Refinitiv, compute and compare the different liquidity measures discussed in class: bid-ask
spread measures (Roll, effective, realized). Study the market impact observed as a result of
trading activity. Discuss the results.
This project can be done also with the TAQ file for JPM stock on 13-Jan-21 constructed
in HW1.
Project 3. Modeling the order flow. Same as in Project 2 but study the autocorrelation
of the order flow. First determine the trade signs using the Lee-Ready algorithm, and then
compute their autocorrelation. Build a model for the trade signs, and use it to forecast the
probability of a next buy/sell order. Compare with data.
Project 4. Multi-period Kyle model. We study in class the one-period of the Kyle model.
This model has been also extended to a multi-period framework. Simulate the multi-period
Kyle model and study the behavior of the model as the number of simulations in a given
period increases. What is the market impact in this model?
Project 5. Glosten-Milgrom model. Run simulations of the GM model and study the
approach to the equilibrium, the volatility of the quotes and trades prices as the model
parameters are changed. Study the breakdown of the market in the Glosten-Milgrom model
when the number of informed traders becomes too large.
Project 6. ZMA two-scale realized volatility estimator. Explain the main idea of the
two-scale realized volatility estimator method of Zhang-Mykland-Ait-Sahalia, and use it to
estimate the realized volatility on a microstructure data sample.
Project 7. PIN model. Implement and calibrate the PIN model (Probability of INformed
Trading model). This is an improved version of the Glosten-Milgrom model which takes into
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account the random arrival of information. There are several R code implementations of this
model: P. Zagaglia, PIN: Measuring Asymmetric Information in Financial Markets with R
https://journal.r-project.org/archive/2013/RJ-2013-008/RJ-2013-008.pdf
and InfoTrad, a package described by D. Celik and M. Tinic in
https://journal.r-project.org/archive/2018/RJ-2018-013/RJ-2018-013.pdf
Project 8. Accuracy of the trade sign rules. Using the provided R implementation of
the Santa Fe model, study the accuracy of the tick rule and Lee-Ready rule on the simulated
data. In this model we know the buy/sell type of the orders, so we can test these trading
sign prediction rule. Which of the 4 parameters of the model is (are) the most important
determinant(s) of the shape?
Project 9. Zero Intelligence Model with alternative order flow specifications. Study the
Santa Fe model with alternative arrival processes for the limit and market orders. In the
classical Santa Fe model orders arrive like a Poisson process (exponentially distributed arrival
time), but other distributions are possible, such as constant arrival rate, uniform distributed
arrival rate or Erlang (sum of n iid exponentials).
Project 10. Garman model. The Garman model is one of the simplest models for a
market dealer making binding bid/ask quotes b/a. In this model we have only market orders.
In class we discuss the conditions for ruin and solvency of the dealer in this model. Build
simulations of the trade flow and test the behavior of the model under solvency and ruin
scenarios.
Hint: Simulate orders arrivals as Poisson processes with rates λb (buy orders) and λa
(ask orders). Assume a, b quotes and study the evolution of the number of shares held by
the dealer and his/her capital in time.
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