Rachel Campbell

Increased correlation in bear markets

A number of studies have provided evidence of increased correlations in global financial market returns during bear markets. Other studies, however, have shown that some of this evidence may be biased. We derive an alternative to previous estimators for implied correlation that is based on measures of portfolio downside risk and that does not suffer from bias. The unbiased quantile correlation estimates are directly applicable to portfolio optimization and to risk management techniques in general. This simple and practical method captures the increasing correlation in extreme market conditions while providing a pragmatic approach to understanding correlation structure in multivariate return distributions. Based on data for international equity markets, we found evidence of significant increased correlation in international equity returns in bear markets. This finding proves the importance of providing a tail-adjusted mean–variance covariance matrix. A generally accepted concept today is that, over time, returns when the markets are experiencing large negative movements are more highly correlated than returns during more normal times. If true, this phenomenon has serious implications for portfolio and risk management because it means that the benefits of diversification are curtailed precisely when investors most need them. The correlation, however, depends on how the returns are conditioned on the size of the returns. Previous studies have provided alternative correlation structures with which to compare conditional empirical correlations, but these estimates have upward or downward biases that need to be corrected. In this article, we provide a quantile correlation approach that is not biased by the size of the return distribution. The result is a simple and pragmatic approach to estimating correlations conditional on the size of the returns. Based on empirical data, we show how the correlation estimates can be used directly in portfolio and risk management. We derive a conditional correlation structure based on the quantile of the joint return distribution; that is, correlation is conditioned on the size of the return distribution. In a bivariate framework, the correlation is estimated by using those observations that fall below the portfolio return of the two assets. The approach is thus in line with current correlation measures used in Markowitz-style portfolio analysis and in current risk management techniques. The quantile correlation structure is determined by the weights of the assets in the portfolio and the quantile estimates of the distribution of returns on the two assets and of the portfolio return. When the distribution is normal, the conditional correlation structure is constant; hence, the conditional quantile correlation will equal the unconditional correlation. Therefore, because the correlation structure is constant over the distribution for normality, one can easily compare empirical estimates of conditional correlation with their theoretical values under conditions of normal distribution. We examine a variety of daily returns from international stock market indexes for the period May 1990 through December 1999 to establish, first, their unconditional correlations. For example, this procedure produced a correlation between the U.S. market (S&P 500 Index) and the U.K. market (the FTSE 100 Index) of 0.349. Assuming bivariate normality for the whole distribution, we would expect the quantile conditional correlation also to be 0.349. For quantiles up to the 95 percent level, we found that the assumption of normality cannot be rejected at the 95 percent confidence level for all the series. For higher quantiles, however—that is, large negative returns in the bivariate return distribution—the conditional correlation structure increased the correlations; in the case of the U.S. and U.K. markets, the correlation increased to 0.457. The effect on mean–variance portfolio optimization is a reduction in the recommended weight of the risky assets held in the portfolio. These results imply that the gains from diversification are not reaped in periods when diversification benefits are most crucial from a mean–variance perspective—in bear markets. Practitioners, therefore, need to know what sort of model is generating the correlations they are relying on. If the underlying model assumed normality, then the correlation estimates used in the model need to be adjusted to incorporate the bear markets higher-than-normal correlation structure.

A Survey of the Venture Capital Industry in Central and Eastern Europe

Publisher Summary This chapter focuses on the venture capital (VC) industry of the eight former communist countries in the Central- and East-European (CEE) region, which became EU member states in May 2004. It discusses the current state of the VC industry in the CEE region and compares it with the emerging VC industry in Western Europe and the well-developed VC environment in the United States. The analysis indicates the importance of an integrated European equity market and the importance of a mature VC industry. Further financial integration may improve exit channels for VC and reallocate talent and human capital. For providing an outlook on the VC industry of these particular countries, a qualitative scenario analysis is conducted. It shows that the VC industry is developing quickly but some political-cultural aspects like the heritage of communism can make this process even less rapid than in Western Europe. Instead of the supply of sufficient VC, the demand for VC is the main back holding factor as there is hardly any entrepreneurial spirit in these former communist countries. The chapter concludes by offering some advice on how to alter this situation. The CEE countries need a regulatory environment that encourages entrepreneurial activities by providing consistent corporate and tax laws, efficient procedures for the set-up of new companies, and a public administration that sees itself as a service to entrepreneurs rather than a burden. They need to create an attractive environment for researchers, enabling interesting research results and a high number of patents. To achieve these goals, an intensive cooperation between universities and the economy has to be established.

Time-Varying Downside Risk: An Application to the Art Market

The economic downturn during 2000 left many investors with burnt fingers and weary of investing in equities. Since then, there has been a search for alternative asset classes to fulfill the need to preserve returns, while not involving too high a risk. Arising from the media’s continued concern about a potential bubble in the housing market, many investors are showing an increasing interest in alternative asset classes that are not so highly correlated with equities, and provide hedging potential as part of a diversified portfolio of investments. One such innovative alternative asset class to stocks, bonds and real estate is art, which is seen increasingly as not merely items with aesthetic value, but also as attractive investments with a potential capital gain. The planned launch of a Fund of Art Funds by ABN Amro in 2005, aiming to channel money into some existing (and some yet to be launched) independent art funds, serves to highlight this point.