Solvable and Other Stochastic Models for Risk Modeling and Asset Pricing in Quantitative Finance

定量金融中风险建模和资产定价的可解模型和其他随机模型

• 批准号: RGPIN-2018-06176
• 负责人: Campolieti, Giuseppe
• 金额: $ 1.17万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714140
• 关键词: Solvable; Other; Stochastic; Models; Risk

Stochastic modelling is fundamental to financial mathematics, which is a core discipline in both finance and mathematics. Stochastic processes provide a natural setting for quantifying financial risk in all sectors of the economy. Financial derivatives are contingent claims that are heavily traded in all sectors of the economy. An important area of financial mathematics remains the development and application of realistic and tractable stochastic models for describing risk factors and the implementation of these models in the valuation and hedging of derivative contracts. Many financial derivatives are also inherently path-dependent. Such complex financial instruments, with built-in path dependencies, are useful for mitigating specific risks. Moreover, certain types of path dependencies of an asset can play a key role in linking option pricing with the credit risk associated to a given financial firm. For example, path-dependent quantities such as the first passage time of an asset to a critical level or the occupation time of the asset below a given critical level, as well as variations of such quantities, are useful for valuing financial contracts subject to default risk. My research will focus on further new developments of tractable stochastic models and implementing the models to various important problems in quantitative finance involving risk modelling, derivative pricing and hedging, and model calibration to historical market data. A main part of my research will continue to exploit and build upon my previous advancements on so-called solvable models with current and new areas of application that will also link option pricing and credit risk. This proposal will lead to new models and efficient numerical algorithms for financial applications employing such models. Another component of my research will exploit Monte Carlo simulation algorithms. Different approaches will be used to develop tractable realistic models. One approach will attempt to extend the mathematical methodology that we have already successfully developed for single-asset solvable models into the multi-asset domain. The other methodology will exploit the use of copulas in constructing tractable multi-asset models that will take into account some real market observed effects. The actual implementation of these models will lead to efficient algorithms for model calibration and pricing and hedging multi-asset financial products. Some of the algorithms will also exploit the use of high-performance computing software.

随机建模是金融数学的基础，金融数学是金融和数学的核心学科。随机过程提供了一个自然的设置量化金融风险在所有经济部门。金融衍生品是一种或有债权，在所有经济部门都有大量交易。金融数学的一个重要领域仍然是发展和应用现实的和易处理的随机模型来描述风险因素，并将这些模型应用于衍生品合约的估值和对冲。许多金融衍生品也具有内在的路径依赖性。这种复杂的金融工具具有内在的路径依赖性，有助于减轻特定风险。此外，资产的某些类型的路径依赖性可以在将期权定价与特定金融公司的信用风险联系起来方面发挥关键作用。例如，资产首次达到临界水平的时间或资产低于给定临界水平的占用时间等路径相关量，以及此类量的变化，对于评估有违约风险的金融合同很有用。我的研究将集中在进一步的新发展，易处理的随机模型和实施模型的各种重要问题，在定量金融涉及风险建模，衍生品定价和对冲，模型校准的历史市场数据。我研究的主要部分将继续利用和建立在我之前在所谓的可解模型方面取得的进展，以及当前和新的应用领域，这些应用领域还将链接期权定价和信用风险。这一建议将导致新的模型和有效的数值算法的金融应用，采用这种模式。我的研究的另一个组成部分将利用蒙特卡罗模拟算法。将使用不同的方法来开发易于处理的现实模型。一种方法是尝试将我们已经成功开发的单资产可解模型的数学方法扩展到多资产领域。另一种方法将利用copula函数构建易于处理的多资产模型，该模型将考虑到一些真实的市场观察到的影响。这些模型的实际实施将导致有效的算法模型校准和定价和对冲多资产金融产品。一些算法还将利用高性能计算软件的使用。