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

买入和卖出订单在金融市场上汇总到限价订单簿（LOB）中。每个资产都有自己的LOB。我们的研究将是第一个项目，联合收割机的信息结合在一个股票的LOB与匹配信息的LOB的衍生期权合约。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）和所有交易者已知的其他合同输入。我们将使用经验和数学方法来研究综合股票和衍生工具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，流动性和价格跳跃相关的风险。研究结果将引起市场参与者、监管机构、金融交易所、金融机构、研究团队和数据供应商的兴趣。我们将研究限价单（即买入或卖出的报价）如何影响波动性，以及如何利用限价单来衡量当前和未来的波动性水平。衍生品价格明确地提供了波动率预期（称为隐含波动率），我们将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性预期来源。以前的研究使用了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。每只股票都有许多衍生品合约，其中一些的流动性相对较低。我们将通过评估与合约条款（如执行价格和到期日）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稳健的方法，将联合收割机隐含波动率结合到有代表性的波动率指数中。我们将确定价格跳跃发生的时间段，这些时间段的价格变化与正常时间段相比非常大。然后，我们将测试使用股票和衍生品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。我们研究的成功取决于对频繁记录的价格信息的访问。我们将使用数据库来记录LOB中的所有添加和删除，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，该数据库从NASDAQ价格构建LOB。对于衍生品，我们将使用期权价格报告局（OPRA）数据库。我们的研究是第一个联合收割机和调查的信息，在这些单独的来源的LOB。

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.