Time Series Analysis in Finance

Table of Contents

1. Introduction
2. Understanding Time Series
3. Autoregressive (AR) Models
4. Moving Average (MA) Models
5. ARIMA Models
6. GARCH Models
7. Forecasting and Model Selection
8. Summary

Introduction

Welcome to the sixth course in our series on quantitative finance and investment. In this course, we’ll introduce the concept of time series analysis in finance, including how to model and forecast financial data over time.

Understanding Time Series

A time series is a sequence of numerical data points taken at successive equally spaced points in time. In finance, it is common to deal with time series data, such as stock prices or economic indicators, which are tracked over time.

Autoregressive (AR) Models

Autoregressive models (AR) are used to represent time series data in which future values are a linear function of past values plus a random error. The AR model is commonly used in finance to model asset returns.

Moving Average (MA) Models

Moving average models (MA) are another type of model for time series data in which a value is a linear combination of past errors. These models are often used in conjunction with AR models to account for autocorrelation in the data.

ARIMA Models

Autoregressive integrated moving average (ARIMA) models combine the AR and MA models and include a differencing step (integration) to handle non-stationary time series data. ARIMA models are commonly used for forecasting in finance.

GARCH Models

Generalized autoregressive conditional heteroskedasticity (GARCH) models are used to model volatility clustering in financial time series data. They’re often used in risk management and derivative pricing.

Forecasting and Model Selection

Time series analysis involves not just modeling the data but also forecasting future values. We’ll cover key techniques for this, such as out-of-sample validation and model comparison criteria like the Akaike information criterion (AIC).

Summary

This course introduced the concept of time series analysis in finance. We covered various types of models used to represent and forecast time series data, including AR, MA, ARIMA, and GARCH models. Understanding these models is crucial for analyzing and predicting financial data over time.

Stay tuned for our next course: Machine Learning in Finance!