Free Download Quantitative Finance & Algorithmic Trading II – Time Series By Holczer Balazs
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Quantitative Finance & Algorithmic Trading II – Time Series
Topics covered: Random walk, autoregressive model, moving average model, ARIMA model, ARCH and GARCH models.
This course focuses on time series analysis using R as the programming language and RStudio as the development environment.
⚠️ Important: Only enroll if you have a real interest in statistics and mathematics!
The goal of the course is to build forecasting models that can predict future stock prices. You will explore the core concepts of time series analysis, including:
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White noise
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Moving average models
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Autoregressive models
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Conditional heteroskedasticity models
In the final section, you will implement a model that combines ARIMA and GARCH, developed fully from scratch. This hybrid model is designed to outperform a simple buy & hold strategy, offering a practical edge in financial forecasting.
Your Instructor
Holczer Balazs
I’m Balazs Holczer from Budapest, Hungary. My background is in physics, followed by a master’s degree in applied mathematics. Currently, I work as a simulation engineer for a multinational company.
Since my university days, I’ve been passionate about algorithms, data structures, and their implementations—especially in Java. Over time, I expanded my focus to machine learning, artificial intelligence, and numerical methods such as solving differential equations, linear algebra, interpolation, and extrapolation.
These skills play a critical role in fields like software engineering, R&D, and investment banking. I also have a strong passion for quantitative models like the Black-Scholes and Merton models, which are essential tools for quantitative analysts. In my view, mastering these methods is highly valuable for anyone serious about finance, modeling, or algorithmic trading.
Course Curriculum
- Introduction
Introduction (1:20)
Intsalling RStudio (1:02) - Time Series Analysis – Basics
Time series features (5:55)
Basic statistics I – mean and variance (7:19)
Basic statistics II – covariance (6:03)
Stationarity (6:55)
Correlogram (8:26) - Random Walk Model
White noise introduction (6:04)
Random walk introduction (6:57)
Modeling assets with random walk (3:51) - Autoregressive Model (AR)
Autoregressive model introduction (7:02)
How to select the best model? (2:25)
Autoregressive model example (3:59)
Modeling assets with autoregressive model (6:27) - Moving Average Model (MA)
Moving average model introduction (4:18)
Moving average model example (6:40)
Modeling assets with moving average model (4:12) - Autoregressive Moving Average Model (ARMA)
Autoregressive moving average model introduction (2:21)
Ljung-Box test (2:50)
Autoregressive moving average model example (3:42)
Autoregressive moving average model example II (5:41)
Modeling assets with ARMA model (6:34) - Autoregressive Integrated Moving Average Model (ARIMA)
ARIMA model introduction (3:33)
ARIMA model example (2:59)
Modeling assets with ARIMA model (5:09) - Autoregressive Conditional Heteroskedatic Model (ARCH)
Heteroskedasticity in finance (3:44)
ARCH model introduction (7:26) - Generalised Autoregressive Heteroskedastic Model
GARCH model introduction (2:12)
GARCH model example (5:14)
Modeling assets with GARCH model (5:28) - FOREX Trading Strategy
FOREX trading strategy implementation I (2:29)
FOREX trading strategy implementation II (4:48)
FOREX trading strategy implementation III (5:46)
FOREX trading strategy implementation IV (6:23)
FOREX trading strategy implementation V (3:54)
FOREX trading strategy implementation VI (2:53) - Stock Market Trading Strategy
Stock market trading strategy implementation I (1:28)
Stock market trading strategy implementation II (3:09) - Course Material
Source code & slides
