Disturbing Extremal Behavior of Spot Rate Dynamics

This paper presents a study of extreme interest rate movements in the U.S. Federal Funds market over almost a half century of daily observations from the mid 1950s through the end of 2000. We analyze the fluctuations of the maximal and minimal changes in short term interest rates and test the significance of time-varying paths followed by the mean and volatility of extremes. We formally determine the relevance of introducing trend and serial correlation in the mean, and of incorporating the level and GARCH effects in the volatility of extreme changes in the federal funds rate. The empirical findings indicate the existence of volatility clustering in the standard deviation of extremes, and a significantly positive relationship between the level and the volatility of extremes. The results point to the presence of an autoregressive process in the means of both local maxima and local minima values. The paper proposes a conditional extreme value approach to calculating value at risk by specifying the location and scale parameters of the generalized Pareto distribution as a function of past information. Based on the estimated VaR thresholds, the statistical theory of extremes is found to provide more accurate estimates of the rate of occurrence and the size of extreme observations.extreme value theory, volatility, interest rates, value at risk

WHY FINANCIAL MARKETS DO NOT USE ECONOMETRIC FORECASTING: FOREIGN EXCHANGE EXOTICS, CENTRAL BANKS AND POSITION TAKING

Clients and market practitioners act based on very different sets of information. It is natural for a client to use the Wiener-Kolmogorov prediction theory. Market makers are different. They also use econometric forecasting. But these are not standard econometric forecasting techniques. The practitioner has to take into account various levels or barriers known to exist in the short run. In this paper, we argue that the presence of such levels changes the classical forecasting problem into a Bayesian one. Copyright � 2007 The Author; Journal compilation � 2007 Blackwell Publishing Ltd and The University of Manchester.

Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis."

This article attempts a formal study of technical analysis, which is a class of informal prediction rules, often preferred to Wiener-Kolmogorov prediction theory by participants of financial markets. Yet Wiener-Kolmogorov prediction theory provides optimal linear forecasts. This article investigates two issues that may explain this contradiction. First, the article attempts to devise formal algorithms to represent various forms of technical analysis in order to see if these rules are well defined. Second, the article discusses under which conditions (if any) technical analysis might capture those properties of stock prices left unexploited by linear models of Wiener-Kolmogorov theory. Copyright 1991 by University of Chicago Press.