Duration: 2 days
- Trends, Cyclical and Seasonal Variations
- Linear Time Series Analysis
- Forecasting Economic and Financial Variables
- Using Autoregressive Models for Financial Forecasting
- Testing and Correcting for Seasonality in Financial Time Series
- Using ARCH Models to Predict Variance
- Analyzing High-Frequency Data
- Using Time Series Analysis in Financial Management
The objective of this course is to give you a good understanding of financial time series, of the
statistical tools used for analyzing these series, and of the practical applications of various
econometric methods.
We start with a general introduction to time series analysis, and we explain how dynamic behavior of
economic or financial variables (such as trends, cyclical variations, and seasonal variations) can be
modelled and forecasted and how relationships between the time series of different financial variables
and economic indicators can be detected.
We introduce and explain the concept of linear time series analysis. We describe linear models for
handling serial dependencies and we discuss regression models with time series errors, seasonality,
unit-root non-stationarity, and long-memory processes. We discuss the structure of an autoregressive
model of order p, and we calculate one- and multiple-period ahead forecasts given the estimated
coefficients, and we explain how autocorrelations of the residuals can be used to test whether the auto
regressive model fits the time series. We describe the characteristics of random walk processes, and
contrast them to covariance stationary processes. We also discuss how to test and correct for
seasonality in a time-series model.
We then turn to the modelling of conditional heteroscedasticity. We introduce various econometric
models (such as ARCH and GARCH), that describe the evolution of asset returns over time, and we
demonstrate the use of these models to forecast volatility/variance over short and long horizons.
Further, we address the non-linearity in financial time series, introduce test statistics that can
discriminate linear from non-linear series, and we present and discuss several non-linear models.
We explain how “high-frequency” financial data can be analysed and we show how serial correlations in
e.g. stock returns can result from non-synchronous trading and “bid-ask” bounce. We also look at the
dynamics of time duration between trades and at econometric models for analyzing transaction data.
We conclude with a complete practical case study of the use of time series analysis to improve
portfolio management decision-making in a real-life investment setting.
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