Quantitative Finance & Algorithmic Trading II – Time Series – Holczer Balazs

Quantitative Finance & Algorithmic Trading II – Time Series – Holczer Balazs

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

This course is about time series analyses. You will use R as the programming language and RStudio as the integrated developement environment.
IMPORTANT: only take this course, if you are interested in statistics and mathematics !!!
The aim of the course is to construct a model capable of forecasting future stock prices. You will learn about the most important time series related concepts:

white noise
moving average model
autoregressive model
conditional heteroskedastic models

In the last chapter you will implement a model (combining ARIMA and GARCH models) from scratch that is able to outperform the buy&hold (so long term investing) strategy!