MSCI Europe Research Articles

Index Tracking by Means of Optimized Sampling

We read often that investors are frequently satisfied with an average return, such as the return on an index. To develop a universally applicable model for constructing a portfolio that tracks an index as accurately as possible, one should focus primarily on its practical use in the period after optimization. The objective is to define a limited number of stocks for the portfolio and to maintain it with minimal transaction costs. The approach proposed here requires fewer undesirable assumptions than we see in other approaches to indexing. One-year backtests of the model with the MSCI Europe Index indicate that the optimized index portfolio performs much like the index.

Leptokurtic portfolio theory

The question of optimal portfolio is addressed. The conventional Markowitz portfolio optimisation is discussed and the shortcomings due to non-Gaussian security returns are outlined. A method is proposed to minimise the likelihood of extreme non-Gaussian drawdowns of the portfolio value. The theory is called Leptokurtic, because it minimises the effects from fat tails of returns. The leptokurtic portfolio theory provides an optimal portfolio for investors, who define their risk-aversion as unwillingness to experience sharp drawdowns in asset prices. Two types of risks in asset returns are defined: a fluctuation risk, that has Gaussian distribution, and a drawdown risk, that deals with distribution tails. These risks are quantitatively measured by defining the -- an ellipsoidal cloud of points in the space of asset returns. The size of the ellipse is controlled with the threshold parameter: the larger the threshold parameter, the larger return are accepted for investors as normal fluctuations. The return vectors falling into the kernel are used for calculation of fluctuation risk. Analogously, the data points falling outside the kernel are used for the calculation of drawdown risks. As a result the portfolio optimisation problem becomes three-dimensional: in addition to the return, there are two types of risks involved. Optimal portfolio for drawdown-averse investors is the portfolio minimising variance outside the noise kernel. The theory has been tested with MSCI North America, Europe and Pacific total return stock indices.

Global European portfolio construction: Does a changing volatility structure matter?

AbstractWe propose a multivariate time series model to forecast the returns and volatilities of 15 European financial markets. Using the approach of mean‐variance portfolios we develop several strategies which are based on the predictions of high‐dimensional VAR‐GARCH models for future volatilities. We explore the value of volatility timing strategies by simplifying the forecasting model. One approach for information blocking is based on factor analysis for the returns. Finally we discuss if multivariate volatility timing strategies are successful for beating the benchmark index (the MSCI Europe index). Copyright © 2004 John Wiley & Sons, Ltd.

European Private Equity Funds - A Cash Flow Based Performance Analysis

This paper presents a cash flow based analysis of the return and risk characteristics of European Private Equity Funds. For that purpose a comprehensive data set has been provided by Thomson Venture Economics. We document the typical time pattern of cash flows for European private equity funds. Specifically, it is recorded that the average European private equity fund draws down 23% of total committed capital on the vintage date; within the first three years 60% of the total commitment is draw down. It turned out that limited partners on average get back the money invested slightly after 7 years. Over the time period from 1980 to June 2003, we calculate various performance measures. For that purpose we use only liquidated funds or funds with a small residual net asset value. Under this restriction one specific data set consists of 200 funds. We document a cash flow based IRR of 12.7% and an average excess-IRR of 4.5% relative to the MSCI Europe equity index. In order to circumvent the problems associated with the IRR-approach we focus on the alternative public market equivalent approach. There it is assumed that cash flows generated by a private equity fund are reinvested in a public market benchmark index. We record an average PME of 0.96 and a value-weighted average PME of 1.04. Based on the PME-approach we develop a viable methodology to estimate the return and risk characteristics of European private equity funds and the correlation structure to public markets. As a benchmark index we used the MSCI Europe Equity Index as well as the J.P. Morgan Government Bond Index. Over the period 1980-2003 private equity funds generated an overperformance with respect to the bond index and two of our three samples an underperformance with respect to the equity index. Over the period 1989-2003 private equity funds generated an overperformance with respect to both indexes. Finally, we analyze to what extent performance measures are associated with specific funds characteristics, like size, payback period and vintage year, respectively. While the payback period and the vintage year seem to have a statistically significant influence on a fund's performance, the results with respect to size are inconclusive.

Predicting extreme performers in European equities

Based on SSgA's previous research on predicting extreme stock performers in the US equity market, this paper extends the study to European equity markets. It investigates important characteristics of stocks in the MSCI Europe index predicted to experience extreme returns over the next three months. With a two-stage multivariate logistic model, these extreme performers are separated into winners and losers. For the entire test period, over 17 per cent of 60 predicted extreme performers experience extreme price movements in the subsequent three-month period. An average total three-month return of 4.5 per cent is obtained by going long (short) predicted extreme winners (losers) over the period September 1994–June 2001.

The European Equity Investment Process

A survey of 32 asset managers looks at the asset management industry9s approach to the European equity investment process. Most managers invest money following a large-cap blend style, and do not deviate substantially from popular benchmarks such as the MSCI Europe. The majority of the European equity managers have a bottom-up investment process, in which input for stock selection comes both from qualitative and quantitative analysis. Qualitative research mainly looks to the quality of the management of a company and its strategy; quantitative analysis usually is based on the key measures of profitability and valuation. There is no consensus as to a distinction between the function of an analyst and the function of a fund manager. Most European equity analysts operate on a sector basis. The interest rate cycle, inflation, economic growth, and corporate earnings growth are considered the driving factors in asset allocation. While there are differences among managers, investment approaches have a number of common features.

Value At Risk Estimation For Stock Indices Using The Basle Committee Proposal From 1995

This paper compares different models for volatility forecasts with respect to the value at risk performance (VaR) for daily stock index returns. The VaR measures the potential loss of a portfolio for the next period at a given significance level. We will focus on the question if the choice of the appropriate volatility forecasting model is important for the VaR estimation. We compare the forecasting performance of several volatility models for the returns of the MSCI North America, the MSCI Europe and the MSCI Pacific indices. The resulting VaR estimators are evaluated with different criteria and with respect to the proposal of the Basle Committee from 1995. We propose an alternative calculation of the market risk charge (MRC).

Applying multivariate time series forecasts for active portfolio management

We illustrate in this paper the use of multivariate time series forecasts for portfolio construction and address the following research questions: First, how can forecasts of time series models be used for portfolio weight selection? Second, what kind of time series information improves portfolio performance? Third, what can be gained from developing a market timing strategy? Using a classical mean-variance approach we estimate the optimal weights for the MSCI North America, MSCI Europe and MSCI Pacific indices with four different strategies.