Financial Risk Modelling and Analysis

金融风险建模与分析

• 批准号: RGPIN-2020-04782
• 负责人: Makarov, Roman
• 金额: $ 1.31万
• 依托单位: Wilfrid Laurier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758546
• 关键词: Financial; Risk; Modelling; Analysis

Mathematical and statistical modelling is used to describe and analyze complex phenomena arising in nature and human societies. Stochastic processes are a perfect instrument for modelling dynamically changing systems with uncertainty and multiple degrees of freedom. Examples of such complex systems are financial markets with millions of transactions made every day. The ability to capture the behaviour of market prices more accurately and more thoroughly is of significant importance to various financial institutions such as major Canadian banks and pension funds. The development of efficient numerical and statistical methods of mathematical finance and computational implementation of solvable stochastic volatility models are the main priorities of the proposed research program. It has the following three objectives. First, we will develop a family of multi-asset pricing models with a systemic risk component and evaluate their applications in portfolio management, derivative pricing and risk assessment. The proposed models allow for dealing with financial assets that have missing and asynchronous pricing data. New assets can be easily included in the model without recalibrating parameters for other assets. Second, we will develop structural models of credit risk based on occupation time. In these models, the time of default is defined as the first time that the occupation time of the firm's value process below a default barrier has exceeded a given threshold. Such structural models can be used to assess the liquidation risk and allow for separating default and liquidation events. Our primary focus is on the calibration of occupation time models, their applications in the pricing of financial risk instruments such as credit default swaps and contingent capital bonds. Additionally, we will evaluate the application of spectral expansions for pricing occupation time derivatives. Third, we will develop statistical models for financial news analysis and integrate them with asset price models. One objective is to uncover how financial news and other announcements can affect the stock market, whether that be changes in the price of a stock or changes in the daily volume. Additionally, we will develop a news-driven model of the stock market by combining the classification of textual financial news algorithm with asset price models. The results of this research program will help us better understand how to model financial markets and assess risks when data are incomplete, as well as how to extract useful information from unstructured business data.

数学和统计建模用于描述和分析自然界和人类社会中引起的复杂现象。随机过程是建模具有不确定性和多个自由度的动态变化系统的理想工具。这种复杂系统的例子是金融市场，每天进行数百万笔交易。对于各种金融机构（例如加拿大大银行和养老基金），更准确，更彻底地捕获市场价格行为的能力至关重要。拟议的研究计划的主要优先事项是数学金融和计算实施的有效数值和统计方法的发展。它具有以下三个目标。首先，我们将开发一个具有系统性风险组成部分的多资产定价模型家族，并评估其在投资组合管理，衍生品定价和风险评估中的应用。提出的模型允许处理缺少和异步定价数据的金融资产。新资产可以轻松地包括在模型中，而无需重新校准其他资产的参数。其次，我们将根据职业时间开发信用风险的结构模型。在这些模型中，违约时间被定义为第一次将公司价值过程的职业时间低于默认的障碍超过给定的阈值。这种结构模型可用于评估清算风险，并允许分开默认事件和清算事件。我们的主要重点是对职业时间模型的校准，它们在金融风险工具（例如信用违约掉期和或有资本债券）中的应用中的应用。此外，我们将评估光谱扩展在定价占用时间导数中的应用。第三，我们将开发用于金融新闻分析的统计模型，并将其与资产价格模型集成在一起。一个目的是揭示金融新闻和其他公告如何影响股票市场，无论是股票价格的变化还是每日数量的变化。此外，我们将通过将文本金融新闻算法的分类与资产价格模型相结合，开发以股票市场的新闻驱动模型。该研究计划的结果将有助于我们更好地了解如何在数据不完整时对金融市场进行建模并评估风险，以及如何从非结构化业务数据中提取有用的信息。