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Both in practice and in the academic literature, models for setting margin requirements in futures markets classically use daily closing price changes. However, as well documented by research on high-frequency data, financial markets have recently shown high intraday volatility, which could bring more risk than expected. This paper tries to answer two questions relevant for margin committees in practice: is it right to compute margin levels based on closing prices and ignoring intraday dynamics? Is it justified to implement intraday margin calls? The paper focuses on the impact of intraday dynamics of market prices on daily margin levels. Daily margin levels are obtained in two ways: first, by using daily price changes defined with different time-intervals (say from 3 pm to 3 pm on the following trading day instead of traditional closing times); second, by using 5-minute and 1-hour price changes and scaling the results to one day. Our empirical analysis uses the FTSE 100 futures contract traded on LIFFE. University College Dublin. Smurfit School of Business ESSEC Business School Available on the Munich Personal RePEc Archive, Paper No. 3528

Value at risk (VaR) is a risk measure that has been widely implemented by financial institutions. This paper measures the correlation among asset price changes implied from VaR calculation. Empirical results using US and UK equity indexes show that implied correlation is not constant but tends to be higher for events in the left tails (crashes) than in the right tails (booms). University College Dublin. Michael Smurfit Graduate School of Business

Both in practice and in the academic literature, models for setting margin requirements in futures markets use daily closing price changes. However, financial markets have recently shown high intraday volatility, which could bring more risk than expected. Such a phenomenon is well documented in the literature on high-frequency data and has prompted some exchanges to set intraday margin requirements and ask intraday margin calls. This article proposes to set margin requirements by taking into account the intraday dynamics of market prices. Daily margin levels are obtained in two ways: first, by using daily price changes defined with different time-intervals (say from 3 pm to 3 pm on the following trading day instead of traditional closing times); second, by using 5-minute and 1-hour price changes and scaling the results to one day. An application to the FTSE 100 futures contract traded on LIFFE demonstrates the usefulness of this new approach. University College Dublin. Michael Smurfit Graduate School of Business

Cahier de Recherche du Groupe HEC Paris, n° 646; Recent studies in international finance have shown that correlation of international equity returns increases during volatile periods. However, correlation should be used with great care. For example, assuming a multivariate normal distribution with constant correlation, conditional correlation during volatile periods (large absolute returns) is higher than conditional correlation during tranquil periods (small absolute returns) even though the correlation of all returns remains constant over time. In order to test whether correlation increases during volatile periods, the distribution of the conditional correlation under the null hypothesis must then be clearly specified. In this paper we focus on the correlation conditional on large returns and study the dependence structure of international equity markets during extremely volatile bear and bull periods. We use "extreme value theory" to model the multivariate distribution of large returns. This theory allows one to specify the distribution of correlation conditional on large negative or positive returns under the null hypothesis of multivariate normality with constant correlation. Empirically, using monthly data from January 1959 to December 1996 for the five largest stock markets, we find that the correlation of large positive returns is not inconsistent with multivariate normality, while the correlation of large negative returns is much greater than expected.

Working Paper Series, Social Science Research Network

The paper compares a number of available measures of financial risk and presents arguments in favor of a dynamic measure of risk. We argue that traditional measures are static, while the dynamic measure of risk lacks statistical scrutiny. The main obstacle to building a body of empirical evidence in support of the dynamic risk measure is computational difficulty of identifying local extrema as price charts appear objects of fractal geometry. \ud We overview approaches to financial risk measurement and formulate a number of open questions. The arguments are illustrated on examples of real data.