**9.** "Portfolio Analysis Using Excel" by J. Laws analyses the familiar Markovitz model
using Excel. This topic is taught on Finance degrees and Master's programmes all
over the world, increasingly through the use of Excel. The author takes time out to
explain how the spreadsheet is set up and how simple short cuts can make analysis of
this type of problem quick and straightforward. In the first section of the chapter the
author uses a two variable example to show how portfolio risk and return vary with
the input weights, then he goes on to show how to determine the optimal weights,
in a risk minimisation sense, using both linear expressions and matrix algebra. In
the second part of the chapter the author extends the number of assets to seven
and illustrates that using matrix algebra within Excel, the Markovitz analysis of an
n-asset portfolio is as straightforward as the analysis of a two asset portfolio. The
author takes special care in showing how the correlation matrix can be generated
most efficiently and how within the same framework the optimisation objective can
be modified without fuss.

**10.** "Applied Volatility and Correlation Modelling Using Excel" by F. Bourgoin. The
originality of this chapter lies in the fact that the author manages to implement
a range of univariate and multivariate models within the software package, Excel.
This is extremely useful as a large proportion of finance practitioners, students and
researchers are familiar with this package. Using S&P500 return data the author
generates one step ahead forecasts of volatility using the J.P. Morgan RiskMetrics
model, the J.P. Morgan RiskMetrics model with optimal decay, a GARCH(1,1) model
with and without a variance reduction technique and finally using the GJR model to
account for asymmetric reaction to news. A comparison of forecasts is made and
some useful insights into the efficacy of the models highlighted. In the second part
of the chapter the author uses return data on the DAX30 and CAC40 to model the
correlation structure using a number of models. As with the univariate approach
this includes the J.P. Morgan RiskMetrics model with and without optimal decay, a
GARCH model with and without variance reduction and finally the so called "Fast
GARCH" model of which the author has previously made significant contributions
to the literature.

**11.** "Optimal Allocation of Trend Following Rules: An Application Case of Theoretical
Results" by P. Lequeux uses sophisticated Excel modelling tools to determine what
should be the optimal weighting of trading rules to maximise the information ratio.
The trading rules utilised in the chapter are moving average trading rules ranging in
order from 2 to 117 days and they are applied to a sample of five currency pairs
(USD-JPY, EUR-USD, GBP-USD, USD-CAD and AUD-USD) over the period
15/02/1996 to 12/03/2002. The analysis could however be applied to any financial
asset and any linear trading rule. In the applied example the author attempts to
determine ex-ante what would be the optimal weighting between moving averages
of order 2, 3, 5, 9, 32, 61 and 117 to maximise the delivered information ratio.
To assist in understanding, the model has been programmed into a spreadsheet to
give the reader the possibility to experiment. The results show that in four currency
pairs out of five the optimal weighting procedure is superior, when measured by the
information ratio, to an equally weighted basket of trading rules.

**12.** "Portfolio Management and Information from Over the Counter Currency Options"
by J. B. Luis: this chapter looks at the informational content of risk reversals and
strangles derived from OTC at the money forward volatilities. Three empirical applications
of the literature are presented: one on the EUR/USD, followed by the analysis
of implied correlations and the credibility of the Portuguese exchange rate policy during
the transition to the EMU, and of the Danish exchange rate policy around the
euro referendum in September 2000. This chapter is supported by the necessary Excel
files to allow the reader to validate the author's results and/or apply the analysis to a
different dataset.

**13.** "Filling Analysis for Missing Data: An Application to Weather Risk Management" by
C. L. Dunis and V. Karalis: this chapter analyses the use of alternative methods when
confronted with missing data, a common problem when not enough historical data or
clean historical data exist, which will typically be the case when trying to develop a
decision tool either for a new asset in a given asset class (say a recently issued stock
in a given company sector) or for a new asset class as such (for instance weather
derivatives). The application to weather data derives from the fact that most weather
derivatives pricing methodologies rely heavily on clean data. The statistical imputation
accuracy of different filling methods for missing historical records of temperature data
is compared: the Expectation Maximisation (EM) algorithm, the Data Augmentation
(DA) algorithm, the Kalman Filter (KF), Neural Networks Regression (NNR) models
and, finally, Principal Component Analysis (PCA). Overall, it is found that, for the
periods and the data series concerned, the results of PCA outperformed the other
methodologies in all cases of missing observations analysed.

Overall, the objective of Applied Quantitative Methods for Trading and Investment is not
to make new contributions to finance theory and/or financial econometrics: more simply,
but also more practically, it is to enable its readers to make competent use of advanced
methods for modelling financial markets.

We hope that, with the numerous files and software programs made available on the
accompanying CD-Rom, it will constitute a valuable reference textbook for quantitative
market professionals, academics and finance graduate students.
Many of the authors of chapters contained in this book have an affiliation to the Forecasting
Financial Markets (FFM) conference which has been held each May since 1993.
The editors of the text and several of the authors are members or associates of the Centre
for International Banking, Economics and Finance (CIBEF) at Liverpool John Moores
University. Details of both the conference and CIBEF may be found at www·cibef·com.

**February 2003**