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

在金融市场上，买卖订单被汇总到限价订单簿（lob）中。每个资产都有自己的LOB。我们的研究将是第一个将股票LOB中的信息与衍生期权合约LOB中的匹配信息结合起来的项目。这些衍生品的价格取决于股票价格，它们随时间的变化（称为波动率）和所有交易者都知道的其他合约输入。我们将使用实证和数学方法来调查综合股票和衍生品lob提供的大量信息。这些信息将被处理，以衡量和预测与波动性、流动性和价格跃升相关的风险。预计市场参与者、监管机构、金融交易所、聘用研究团队的金融机构和数据供应商都会对研究结果感兴趣。我们将调查发布的限价单（即买入或卖出的报价）如何影响波动性，以及如何使用它们来衡量当前和未来的波动性水平。衍生品价格明确提供波动率预期（称为隐含波动率），我们将把这些与直接从股票价格变化中获得的估计进行比较。我们将发现信息如何从期权lob传递到股票lob（反之亦然），从而确定最新的波动率预期来源。之前的研究使用交易价格和最佳买卖价格；我们将通过使用提供更多信息的完整lob进行创新。市场的流动性取决于供给和需求，这是由lob揭示的。每只股票都有许多衍生品合约，其中一些合约的流动性相对较低。我们将通过评估与合约条款（如行权价格和到期日）相关的流动性，为期权市场的微观结构提供新的见解。这将使我们能够找到将隐含波动率合并为代表性波动率指数的稳健方法。我们将确定价格暴涨的时间段，这些时间段的价格变化与正常时期相比非常大。然后，我们将测试使用股票和衍生lob来预测跳跃发生的方法。我们还将对跳跃期间不同订单类型之间的动态交互进行建模。我们的研究能否成功，取决于能否获得频繁记录的价格信息。我们将使用数据库记录lob的所有添加和删除，并与非常精确的时间戳相匹配。对于股票，我们将使用LOBSTER数据库，该数据库根据纳斯达克价格构建lob。对于衍生品，我们将使用期权价格报告管理局（OPRA）数据库。我们的研究首次结合并调查了这些独立lob来源中的信息。

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.