Bilateral Austria: Order Book Foundations of Price Risks and Liquidity: An Integrated Equity and Derivatives Markets Perspective

Lead Research Organisation: Lancaster University
Department Name: Accounting & Finance


Buy and sell orders are aggregated at financial markets into limit order books (LOBs). Each asset has its own LOB. Our research will be the first project to combine the information in a stock's LOB with matching information in the LOBs for derivative option contracts. These derivative prices depend on the stock price, their variability through time (called volatility) and other contract inputs known to all traders. We will use empirical and mathematical methods to investigate the vast amount of information provided by integrated stock and derivative LOBs. This information will be processed to measure and predict risks associated with volatility, liquidity and price jumps. The results are expected to be of interest to market participants, regulators, financial exchanges, financial institutions employing research teams and data vendors.

We will investigate how posted limit orders, i.e. offers to buy or to sell, contribute to volatility and how they can be used to measure current and future levels of volatility. Derivative prices explicitly provide volatility expectations (called implied volatility) and we will compare these with estimates obtained directly from changes in stock prices. We will discover how information is transmitted from option LOBs to stock LOBs (and vice versa) and thus identify the most up-to-date source of volatility expectations. Previous research has used transaction prices and the best buying and selling prices; we will innovate by using complete LOBs providing significantly more information.

The liquidity of markets depends on supply and demand, which are revealed by LOBs. Each stock has many derivative contracts, some of which have relatively low liquidity. We will provide new insights into the microstructure of option markets by evaluating liquidity related to contract terms such as exercise prices and expiry dates. This will allow us to find robust ways to combine implied volatilities into representative volatility indices. We will identify those time periods when price jumps occur, these being periods when changes in prices are very large compared with normal time periods. We will then test methods for using stock and derivative LOBs to predict the occurrence of jumps. We will also model the dynamic interactions between different order types during a jump period.

The success of our research depends on access to price information recorded very frequently. We will use databases which record all additions to and deletions from LOBs, matched with very precise timestamps. For stocks, we will use the LOBSTER database which constructs LOBs from NASDAQ prices. For derivatives, we will use the Options Price Reporting Authority (OPRA) database. Our research is the first to combine and investigate the information in these separate sources of LOBs.

Planned Impact

The planned research into the microstructure of equity and derivative markets will make an impact upon academic researchers and the finance community. This will be achieved by creating an international research team which combines and builds on proven individual expertise. This expertise applies to the analysis of very large financial datasets and relies on skills regarding data management, empirical methods, economic theory and relevant mathematical theory.

The finance community includes regulators, policy makers, financial exchanges, data vendors, banks, other financial institutions, high-frequency traders and retail investors, all of whom can benefit from the proposed research. We will investigate which sector (equity or derivatives) first reveals new information, for the first time at a high-frequency level using order books, and will also seek to identify which market features enhance fast and correct information processing. We will also investigate whether or not high-frequency traders (HFTs) and algorithmic trading destabilize markets when there are large price fluctuations.

The results of our data analysis should benefit regulators and financial exchanges. We will obtain new results about the prediction of price volatility and the frequency of price jumps by studying the information in order books. The research team will make use of existing contacts in the market microstructure division of the Bank of England, EUREX and NASDAQ to distribute the research results.

We will obtain new results about the prediction of price volatility and the frequency of price jumps by studying the information in order books. These predictions are important for risk managers and are of value for all traders. We will also obtain new insights into the construction of volatility indices, which will be relevant for data vendors and of general value for retail investors.

We will provide new insights into the microstructure of derivative markets and their links to the microstructure of corresponding equity markets. This will be made possible by building up a unique and innovative database, bringing together state-of-the-art high-frequency data from limit order books for the markets of stocks and corresponding derivatives. Matching this data on a micro-level will yield new results on transmission and spill-overs in volatility and liquidity. This information should be important for regulators and market operators. Existing contacts of some of the team members at EUREX and NASDAQ will allow for explicit transmission of knowledge.

The academic impact of the research will primarily benefit researchers in the areas of financial econometrics, market microstructure and statistics, with a secondary influence upon areas such as asset pricing, risk management and market efficiency.

This impact will be achieved by publications in leading peer-reviewed journals, by presentations at international conferences and through two meetings organized by the applicants. The first meeting will share methodology, while the second will share our research results. Participation by finance practitioners will be encouraged at our meetings.

We will contribute new results to the academic literature on derivative markets, on market microstructure, on the information content of high-frequency order books, on volatility and jump prediction and upon the estimation of implied volatility surfaces. We will innovate by being the first team to analyse limit order books for both equities and their derivatives.

The research programme will contribute significantly to the career development of early career researchers, by giving postdoctoral researchers the opportunity to acquire new skills and knowledge by working in an international team. It is intended to also use the programme to develop the skills of PhD students supervised by the research team.
Title OPRA data analyser 
Description The developed algorithm allows to efficiently process, analyse and store large amounts of financial data (high-frequency trades and quotes data) supplied as compressed .csv files. It uses parallel processing and is written in Python/bash scripting language. 
Type Of Material Data analysis technique 
Year Produced 2017 
Provided To Others? No  
Impact The developed algorithm allows to process large volumes of data reasonably fast and perform initial descriptive analysis. It is part of the project which is a first attempt to perform econometric analysis on the high-frequency trades and quotes' data supplied by the Options Price Reporting Authority. 
Title OPRA data filtering tool 
Description The tool is aimed to detect, treat and (optionally) filter several classes of irregular records from the raw intra-daily options trade and quote data reported by OPRA (Options Price Reporting Authority). The tool is implemented in Python programming language. 
Type Of Material Data analysis technique 
Year Produced 2017 
Provided To Others? No  
Impact The tool allows to prepare and clean intra-daily options trade and quote data for a subsequent analysis. In addition, it provides a statistics on a presence of non-informative, irrelevant, outlying and other types of irregular records in the raw data. The tool has been developed as a part of the project on descriptive analysis of high frequency trade and quote options data from OPRA (Options Price Reporting Authority). 
Title OPRA data selection tool 
Description The tool is aimed to efficiently extract user-specified intra-daily options trade and quote data from a large remote database and to deliver the requested data in a convenient format. The tool is implemented in Python programming language and contains unix shell fragments for communication on the server side. 
Type Of Material Data analysis technique 
Year Produced 2017 
Provided To Others? No  
Impact The tool is essential for working with OPRA (Options Price Reporting Authority) intra-daily options trade and quote data as it allows to efficiently access and prepare the required amount of data for further analysis. 
Description Bilateral Austria: Order Book Foundations of Price Risks and Liquidity: An Integrated Equity and Derivatives Markets Perspective 
Organisation University of Vienna
Country Austria 
Sector Academic/University 
PI Contribution Our grant is a Bilateral Grant together with the FWF in Austria, with the Austrian Project Number: I_2762_G27 and Prof. Dr. Nikolaus as the CI.
Collaborator Contribution As agreed in our joint proposal our partners in Vienna employed Dr. Ilya Archakov as a post-doc researcher to work on the project and they employed a technician to help with the complex data preparation.
Impact The first stage of the project, the joint data preparation, has partly been concluded and depending on the individual research question will continue throughout the project. A draft of a working paper, involving all team members, detailing the stylized facts of complex high frequency option data is currently been written. The theoretical work on research themes 1 and 2 started early on resulting in new working papers, new versions of earlier working papers and publications as listed in the publication section.
Start Year 2017