Statistical Data Analysis

2023/2024

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Международный институт экономики и финансов

Статус:

Курс по выбору

Когда читается:

3-й курс, 3, 4 модуль

Преподаватель

Люлько Ярослав Александрович

Course Syllabus

Abstract

Statistical data analysis is a one-semester course which is taught for 3rd-year ICEF BSc studentsat modules 3, 4. The course focuses on practical aspects and applications of probabilitytheory, statistics and mathematical finance. The first part of the course covers classic mathematicalmodels of financial markets, pricing of derivatives via analytical method, partial differentialequations (PDE) and Monte-Carlo simulations. Second part of the course focuses in recent developmentsand trends such as basics of machine learning, neural networks and blockchain. Inclassactivity and problem solving are primarily done by programming in Python, special attentionwill be paid to developing object-oriented approach (OOP). Problem solving sessions willalso be included to work out theoretical material from lectures.

Learning Objectives

• Familiarise students with contemporary data analysis methods and tools.
• Give introduction to modern areas where statistical analysis can be applied.
• Learn how to use programming language (Python) to analyse the data.
• Give necessary knowledge to allow students to further study statistical methods which are available at modern software including reading relevant documentation, extra materials (books, articles) and applying new methods of data analysis.
• Give necessary coding skills to allow students to further enhance programming skills by studying advanced techniques which allow to write more effective and professional code meeting international coding standards.

Expected Learning Outcomes

• Be able to estimate probability density function via kernel methods.
• Be able to use splines for discount curve construction.
• Understand OOP basic principles: polymorphism, inheritance, encapsulation.
• Be able to solve programming problem via OOP, write necessary classes and functionality.
• Be able to choose the best design for specific problem, design classes and their inheritance.
• Understand how stochastic processes can be used to model prices and returns of financial assets.
• Understand Binomial, Bachelier and Black-Scholes models.
• Know main methods of financial instruments pricing: analytic, PDE, Monte-Carlo.
• Be able to design and develop representation of financial instrument via OOP approach.
• Be able to write tests, understand the difference between unit and regression tests.
• Know basic machine learning algorithms, how statistical methods can be used in machine learning.
• Know common functionality and design features which various APIs share.

Course Contents

• Python essentials
• Calibration of parameters in financial modelling
• Monte-Carlo methods for finance
• Principles of Object-oriented programming (OOP)
• Models for evolution of prices of financial instruments
• Methods of pricing of financial instruments
• Comprehensive data analysis
• Artificial intelligence (AI), basics of neural networks
• Application public interfaces (API)

Assessment Elements

• Final exam
• Midterm test
• Home assignments
• Class activity

Interim Assessment

• 2023/2024 4th module
  0.12 * Class activity + 0.4 * Final exam + 0.2 * Home assignments + 0.28 * Midterm test

Bibliography

Recommended Core Bibliography

• Hull, J. (2017). Fundamentals of Futures and Options Markets, Global Edition (Vol. Eighth edition). Boston: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419711
• Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017

Recommended Additional Bibliography

• The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017

Presentation

• Syllabus

Authors

• LYULKO YAROSLAV ALEKSANDROVICH