FTSE 100 INDEX

FTSE 100 index

Abstract

This paper examines the lead-lag relationship between the FTSE 100 index and index futures price employing a number of time series models. Using 10-min observations from January 2004 to December 2009, it is found that lagged changes in the futures price can help to predict changes in the spot price. The best forecasting model is of the error correction type, allowing for the theoretical difference between spot and futures prices according to the cost of carry relationship. This predictive ability is in turn utilised to derive a trading strategy which is tested under real-world conditions to search for systematic profitable trading opportunities. It is revealed that although the model forecasts produce significantly higher returns than a passive benchmark, the model was unable to outperform the benchmark after allowing for transaction costs.

Table of Contents

Chapter 15

Introduction5

Background5

Significance of Study6

Rationale6

Structural Models or Multivariate Approach8

Univariate Time Series Analysis (Linear and Non-Linear Models)10

Capital Asset Pricing Model11

APM and the Modern Portfolio Theory12

Risk And Return Analysis Of The Market13

Chapter 2 · Analyse daily changes in the FTSE 100 index18

The theoretical relationship between spot and futures markets22

The data27

Econometric analysis, methodology and results29

Cointegration and error correction29

ECM-COC — the cost of carry theory model33

An ARMA model34

VAR model35

Out of sample forecasting accuracy35

Forming a trading strategy based on statistical forecasts37

Chapter 3 · Discuss Returns and Risks37

Strategy description37

Liquid trading strategy38

Buy and hold strategy38

Filter strategy — better predicted return than average38

Filter strategy — better predicted return than first decile39

Filter strategy — high arbitrary cut-off39

Risk adjustment39

Transaction costs40

Chapter 4 Discuss (CAPM)42

Capital Assets Pricing Model (CAPM)42

Estimation technique46

Chapter 5 · Discuss the benefits of diversification52

Discussion52

Chapter 6 ARCH and GARCH models57

The Black-Scholes and Merton standard results58

Discrete time models and stochastic volatility61

Systematic consumption risk64

Simulations65

Steady state volatility66

Pricing biases67

Modelling the variance68

The preference parameter69

Monte Carlo simulations70

Unit-root tests73

ARCH effects74

Univariate GARCH modelling74

Conclusions80

References81

Chapter 1

Introduction

The subject of forecasting the exchange rate has attracted scrutiny and an overwhelming body of work has been done in the past. A large body of work has sought to accurately forecast the exchange rates. A study of the work done shows that there is a debate regarding whether the exchange rates follow a random walk or can be modelled. A debate also persists whether the structural models, linear, non-linear time series models best forecast the exchange rate.

Background

EMH and the Random Walk Theory The concept of efficient market was introduced first by Fama et al.,(1969) who defines an efficient market as a market which rapidly adjusts to any new information. Though the rapid adjustment to new information is an important element of an efficient market; it is not the only one. A new definition was put forward by Fama (1991) that states that the asset prices fully reflect all available information. This is a stronger definition of the EMH. This means that it is impossible to outperform the market consistently because currency prices already incorporate and reflect all relevant information. Grossman & Stiglitz (1980) concludes that if the information was fully reflected in the asset prices, there will be no financial incentive to obtain that ...