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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-financial-econometrics-for-time-series-analysis/
课程评论：没有评论
课程名称：掌握金融经济计量学与时间序列分析 课程概述：踏上金融经济计量学与时间序列分析的学习之旅。本课程将全面提升您在金融经济计量学方面的技能，特别注重时间序列分析的复杂性。您将深入探索理论基础与实际应用，同时掌握Excel的强大功能。 课程内容摘要： 1. **基础知识：构建您的统计工具箱** - 数据获取：学习如何寻找优质的金融数据来源，如Kaggle和直接交易所，同时利用课程提供的数据保证学习的连贯性。 - 统计基础：掌握均值、方差、标准差等核心统计量及其在解读数据分布中的应用。 - 概率分布：了解概率密度函数和累积分布函数，熟练掌握离散与连续数据的区别，并使用直方图和累积和表示概率。 - 随机变量：深入了解随机变量及其与概率函数的关系。 2. **实操数据精通：将原始数据转化为洞察** - 实证与理论：从真实数据构建实证PDF和CDF，并与理论分布进行比较。 - QQ图：运用QQ图进行视觉比较，加深对金融收益分布的理解。 - 数据转换：掌握计算对数收益和标准化数据的基本技巧。 3. **统计建模：揭示内在模式** - 正态分布：深入正态分布的性质，学习如何将其拟合到数据中。 - 混合密度：探索混合密度，学习组合多个密度函数。 - 线性回归：了解简单和多重线性回归的基本概念，并通过普通最小二乘法（OLS）计算参数。 - 假设检验：掌握假设检验的基本框架及使用t检验和p值判断结果显著性。 - 最大似然估计（MLE）：学习MLE的概念及其在模型系数估计中的应用。 4. **多元分析：探索高维关系** - 双变量联合概率密度函数：学习将两个正态分布结合起来。 - 轴法：了解和应用高斯copula来建模随机变量间的依赖结构。 5. **高级时间序列概念：掌握细微差别** - 平稳性：阐明严格和平稳的概念，为坚实的时间序列建模奠定基础。 - 单根检验：了解单根与平稳性之间的关系，通过生成平稳和非平稳数据来加深理解。 - 自相关和偏自相关：运用自相关函数（ACF）和偏自相关函数（PACF）分析时间序列数据。 6. **实践实施：将理论付诸实践** - Excel作为工具：利用Excel实现所有模型和计算。 - 软件素养：养成对软件假设的批判性思维，确保结果的准确性和可靠性。 - 模板和示例：提供模板以指导每个步骤，提升理解。 通过这一全面的学习旅程，您将具备优秀的理论知识与实践技能，能够在金融经济计量学与金融时间序列数据分析中脱颖而出。准备好将数据转化为可行性洞察吧！
Embark on a Journey into Financial Econometrics and Time Series AnalysisThis comprehensive learning experience will equip you with the skills to master financial econometrics, with a particular emphasis on the intricacies of time series analysis. Get ready to delve into both the theoretical underpinnings and practical applications, all while wielding the power of Excel.Here's a glimpse into the terrain we'll explore:1. Foundations: Building Your Statistical ArsenalData Acquisition: Begin your journey by discovering prime sources for financial data, such as Kaggle and direct exchanges. While you'll have access to diverse sources, we'll primarily use the provided course data to ensure a smooth, consistent learning experience.Statistical Essentials: Grasp the core statistical measures-mean, variance, standard deviation-and unlock their power in deciphering data distributions. The exploration will extend to central moments, including the intriguing skewness and kurtosis.Probability Distributions: Dive into the world of probability density functions (PDFs) and cumulative distribution functions (CDFs). Discover the nuances between discrete and continuous data, and master the art of representing probabilities using histograms and cumulative sums. We'll also uncover the secrets of the ubiquitous normal distribution.Random Variables: Unravel the concept of random variables and their intimate relationship with probability functions.2. Hands-On Data Mastery: Transforming Raw Data into InsightsEmpirical vs. Theoretical: Construct empirical PDFs and CDFs from real-world data and engage in a fascinating comparison with theoretical distributions, like the elegant normal and the robust Student's T. This hands-on experience will involve sorting returns, standardizing data, and scaling empirical PDFs using Z-scores.QQ Plots: Master the art of visual comparison using QQ plots, pitting empirical distributions against their theoretical counterparts. Quantiles will become your new best friends as you gain deeper insights into the distribution of financial returns.Data Transformation: Equip yourself with essential data transformation techniques. Learn to calculate log returns and standardise your data, preparing it for rigorous analysis.3. Statistical Modelling: Unveiling the Patterns WithinThe Normal Distribution: Delve deeper into the fascinating properties of the normal distribution, examining it both as a density function and a cumulative distribution. Discover how to expertly fit this fundamental distribution to your data.Mixture Densities: Expand your modelling toolkit by exploring mixture densities. Learn to blend multiple density functions, crafting mixed distributions that capture complex real-world scenarios.Linear Regression: Explore the world of linear regression, both simple and multiple. Understand the foundational concepts of intercepts and slopes, and master the calculation of these crucial parameters using Ordinary Least Squares (OLS).ANOVA Metrics: Get acquainted with essential ANOVA metrics: Residual Sum of Squares (RSS), Total Sum of Squares (TSS), Explained Sum of Squares (ESS), and the ever-important R-squared.Hypothesis Testing: Develop a solid grasp of hypothesis testing, framing null and alternative hypotheses with precision. Statistical tests, including t-tests and the insightful p-values, will become your trusted tools for determining the significance of your findings.Maximum Likelihood Estimation (MLE): Embrace Maximum Likelihood Estimation (MLE) as a powerful technique for estimating model coefficients. Delve into the concepts of likelihood and log-likelihood functions, and harness numerical methods to unlock their potential.Time Series Models: Enter the realm of time series with Autoregressive (AR), Moving Average (MA), and ARMA models. Decode their components and master their estimation. We'll also touch upon the versatile ARIMA models.4. Multivariate Analysis: Exploring Relationships in Higher DimensionsBivariate Joint PDFs: Venture into the realm of bivariate joint probability density functions. Learn to combine two normal distributions, understanding the crucial role of correlation in shaping their joint behaviour.Copulas: Discover the power of copulas in modelling the intricate dependency structures between random variables. The Gaussian copula will be a key focus, and you'll learn how to calculate copula density using empirical CDFs.5. Advanced Time Series Concepts: Mastering the NuancesStationarity: Unpack the concept of stationarity, both strict and weak. This understanding is the bedrock of robust time series modelling, and you'll see why using stationary data is so critical.Unit Roots: Confront the concept of unit roots and their relationship to stationarity. Experiment by generating both stationary and non-stationary data to solidify your understanding.ACF and PACF: Harness the power of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to dissect and analyse your time series data.Dickey-Fuller Tests: Learn to deploy the Dickey-Fuller and Augmented Dickey-Fuller (ADF) tests, essential tools for rigorously assessing stationarity.Cointegration and the Engel-Granger Test: Unlock the secrets of cointegration and use the Engel-Granger test to reveal if two time series share a long-run equilibrium relationship.Error Correction Model (ECM): Dive into the Error Correction Model (ECM), a powerful tool that integrates both the short-term and long-term dynamics of cointegrated time series.Volatility Modelling: Explore the dynamic world of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, specifically designed for modelling volatility. We'll also venture into Asymmetric GARCH (AGARCH) models for a more nuanced approach.6. Practical Implementation: Putting Theory into Action with ExcelExcel as Your Tool: Leverage the familiar power of Excel to implement all the models and calculations we'll explore.Software Savvy: Develop a critical eye for understanding software assumptions. Learn to meticulously verify your results, ensuring accuracy and reliability.Templates and Examples: Benefit from provided templates designed to guide you through each step, and compare your work against completed examples for enhanced understanding.This comprehensive learning journey will empower you with both the theoretical knowledge and the practical skills needed to excel in financial econometrics and the analysis of financial time series data. Get ready to transform data into actionable insights!
