Quantitative Finance and Algorithmic Trading II-Time Series – Holczer Balazs

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Salepage link: At HERE. Archive: http://archive.is/0RWSSnn

Quantitative Finance & Algorithmic Trading II – Time Series

Random walk, autoregressive model, moving average model, arima model, arch and garch model

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!

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

INSTITUTIONAL RISK DISCLOSURE: Trading foreign exchange, cryptocurrencies, and algorithmic assets on margin carries a high level of risk and may not be suitable for all investors. Past performance of any trading system or quantitative blueprint does not guarantee future results.