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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/financial-engineering-optimizationmethods
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课程名称：资产管理中的优化方法 概述：本课程聚焦于优化方法在投资组合构建和风险管理中的应用。第一模块讨论了在无套利环境下，通过均值-方差分析和资本资产定价模型（CAPM）进行投资组合的构建，并演示了证券市场线和夏普最优投资组合的应用。第二模块涵盖了在现实环境中实施均值-方差技术所面临的困难以及应对这些困难的潜在方法。我们将介绍风险度量的价值-at-risk（VaR）和条件风险值（CVaR），以及在交易和资产管理中扮演重要角色的交易所交易基金（ETFs）。还将讨论典型统计偏差及其潜在原因，以便在完成实际统计估计时获得更好的结果。最终模块直接关注现实中的交易成本建模，包括基本市场微观结构、订单簿、买卖价差、流动性测量及其对交易成本的影响。随后，我们通过考虑交易成本来丰富均值-方差投资组合策略。 课程大纲： 1. 课程概述 2. 均值-方差分析与CAPM：介绍均值-方差分析和资本资产定价模型，构建最优投资组合，利用Excel实现均值-方差优化，计算夏普比率等。 3. 实施均值-方差的实际问题：探讨实施均值-方差的困难，介绍VaR和CVaR作为风险度量，以及ETFs的作用和常见统计偏差。 4. 金融工程的其他应用：学习市场微观结构及其对交易成本的影响，并在均值-方差策略中考虑交易成本。 通过本课程，学员将更好地应对现实中的投资问题，并提升在资产管理领域的实战能力。
Name:Course Overview
Description:
Name:Mean-Variance Analysis and CAPM
Description:In this module, we will cover topics related to Mean-Variance Analysis and Capital Asset Pricing Model (CAPM), which is a fundamental theory in portfolio selection. CAPM can be used to price risky assets in the market. We will start by utilizing Mean-Variance Analysis to construct an optimal portfolio in an arbitrage-free market. Then we will introduce the efficient frontier and capital market line. Finally, we use excel to implement Mean-Variance optimization and construct a portfolio with the highest Sharpe ratio. In practice, Mean-Variance Analysis and CAPM can also be extended in other pricing techniques such as factor model. In the assignment, you will be required to apply Mean-Variance Analysis to do portfolio selection, Sharpe ratio computation, and risky asset pricing, etc.
Name:Assignment week
Description:
Name:Practical Issues in Implementing Mean Variance
Description:In this module, we show the difficulties in implementing Mean-Variance and provide possible methods to improve the estimated frontier by revising constraints and amending parameter estimation. VaR and CVaR are introduced as different measurements about risk beyond variance. In the second lesson, we will also learn common ETFs and their returns and volatility. ETFs play an important role in trading and asset management because of their features at low costs, tax efficiency, and stock-like behaviors. In the last lesson, we will introduce some facts about typical statistical biases and pitfalls, as well as underlying reasons. This can remind us to be more careful when doing the statistical estimation. If you have any questions, you should reach out to us on the discussion forum.
Name:Assignment Week
Description:
Name:Other Applications of Financial Engineering
Description:In the real world, transaction costs are charged when we buy or sell assets in the market. How to model transaction cost is a key question in portfolio execution. In this module, we learn about the basic market micro-structures, including order book, bid-ask spread, measurement of liquidity and their effects on transaction costs. Then we enrich Mean-Variance portfolio strategies by taking transaction costs into consideration. By learning this module, you will be better prepared to deal with real-world investment problems.
This course focuses on applications of optimization methods in portfolio construction and risk management. The first module discusses portfolio construction via Mean-Variance Analysis and Capital Asset Pricing Model (CAPM) in an arbitrage-free setting. Next, it demonstrates the application of the security market line and sharpe optimal portfolio in the exercises. The second module involves the difficulties in implementing Mean-Variance techniques in a real-world setting and the potential methods to deal with it. We will introduce Value at Risk (VaR) and Conditional Value at Risk (CVaR) as risk measurements, and Exchange Traded Funds (ETFs), which play an important role in trading and asset management. Typical statistical biases, pitfalls, and their underlying reasons are also discussed, in order to achieve better results when completing real statistical estimation. The final module looks directly at real-world transaction costs modeling. It includes the basic market micro-structures including order book, bid-ask spread, measurement of liquidity, and their effects on transaction costs. Then we enrich Mean-Variance portfolio strategies by considering transaction costs.
