Python Implementation for "Financial Time Series" by Ruey S. Tsay

Explore "Financial Time Series" on Amazon.

1. Introduction

Inthe contemporary financial world, Python has emerged as a leading tool for financial analysis. During my undergraduate studies, I delved into financial analysis through Ruey S. Tsay's insightful book. However, I noticed a significant gap: there was a lack of Python-based resources and examples complementing Tsay's teachings. Recognizing this, I decided to create this GitHub repository. It's designed to bridge this gap by providing Python code examples and resources, making Tsay's concepts more accessible to everyone studying his book.

Before you are dive into the coding you need to download files in RUEY S. TSAY's Website

2. Progress

• Chapter 1 FINANCIAL TIME SERIES AND THEIR CHARACTERISTICS

  • 1_Demonstration_1.ipynb
  • 1_Exercise_1.ipynb
  • 1_Exercise_2.ipynb
  • 1_Exercise_3.ipynb
  • 1_Exercise_4.ipynb
  • 1_Exercise_5.ipynb

• Chapter 2 LINEAR TIME SERIES ANALYSIS AND ITS APPLICATIONS

  • 2_Demonstration_1.ipynb - page. 35
  • 2_Demonstration_2.ipynb - page. 45
  • 2_Demonstration_3.ipynb - page. 49
  • 2_Demonstration_4.ipynb - page. 51
  • 2_Demonstration_5.ipynb - page. 78
  • 2_Demonstration_6.ipynb - page. 80
  • 2_Demonstration_7.ipynb - page. 88
  • 2_Demonstration_8.ipynb - page. 95

• Chapter 3 CONDITIONAL HETEROSCEDASTIC MODELS

• Chapter 4 NONLINEAR MODELS AND THEIR APPLICATIONS

• Chapter 5 HIGH-FREQUENCY DATA ANALYSIS AND MARKET MICROSTRUCTURE

• Chapter 6 CONTINUOUS-TIME MODELS AND THEIR APPLICATIONS

• Chapter 7 EXTREME VALUES, QUANTILE ESTIMATION, AND VALUE AT RISK

• Chapter 8 MULTIVARIATE TIME SERIES ANALYSIS AND ITS APPLICATIONS

• Chapter 9 PRINCIPAL COMPONENT ANALYSIS AND FACTOR MODELS

• Chapter 10 MULTIVARIATE VOLATILITY MODELS AND THEIR APPLICATIONS

• Chapter 11 STATE-SPACE MODELS AND KALMAN FILTER

• Chapter 12 MARKOV CHAIN MONTE CARLO METHODS WITH APPLICATIONS