Financial Time Series – Analysis, Modelling and Applications

Day One

09.00 - 09.15 Welcome and Introduction

09.15 - 12.00 General Introduction to Time Series Analysis

• Financial Time Series and their Characteristics
  • Asset returns
  • Dynamics of time series
  • Trends, cyclical and seasonal variations and irregular variations
• Overview of Applications in Finance

Linear Time Series Analysis

• Stationarity
• Random Walk Processes
• Correlation and Autocorrelation Functions
• White Noise and Linear Time Series
• Linear and Log-linear Trend Models
  • Structure
  • In-sample and out-of-sample forecasts
  • Calculating predicted trend values given the estimated coefficients
• Small Exercises

12.00 - 13.00 Lunch

13.00 - 16.30 Linear Time Series Analysis (Continued)

• Mean Reversion
• Autoregressive Models
• Moving Average Models
• ARMA Models
• Unit-Root Non-Stationarity
• Seasonal Models
  • Testing and correcting for seasonality in a time-series model
  • Examples: Seasonal adjustment of economic time series
• Regression Models with Time Series Errors
• Long-Memory Models
• Small Exercises

Day Two

09.00 - 09.15 Recap

09.15 - 12.00 Conditional Heteroscedastic Models

• Volatility and Its Characteristics
• Analyzing Time Series for Nonstationarity
• Testing for Cointegration
• The ARCH Model
• The GARCH Model
• Random Coefficient Autoregressive Models
• Long-Memory Stochastic Model
• Examples of Applications
  • Predicting variance with ARCH and GARCH models

Non-Linear Models and their Applications

• Non-Linear Models
• Non-Linear Forecasting
• Example of Applications

12.00 - 13.00 Lunch

13.00 - 16.30 High-Frequency Data Analysis

• Non-Synchronous Trading
• Bid-Ask Spread
• Duration Models
• Non-Linear Duration Models
• Bivariate Models for Price Change and Duration

Case Study: Using Time Series Analysis to Improve Portfolio Decisions

• Forecasting Stock and Commodity Prices
• Quantitative Trading Strategies

Evaluation and Termination of the Seminar