In this work we present a methodology to detect rare events which are defined as large price movements relative to the volume traded. We analyze the behavior of equity after the detection of these rare events. We provide methods to calibrate trading rules based on the detection of these events and illustrate for a particular trading rule. We apply the methodology to tick data for thousands of equities over a period of five days. In order to draw comprehensive conclusions, we group the equities into classes and calculate probabilities of price recovery after these rare events for each class. The methodology that we have developed is based on non-parametric statistics and makes no assumption about the distribution of the random variables in the study. Copyright © 2011 Wilmott Magazine Ltd.

The objective of this paper is to investigate a class of general tire models that provides results suitable for usage in vehicle dynamics. Tire models currently used for vehicle dynamic analyses are overly simplistic (springs, a spring and damper combination or semi‐elastic substance) or based on curve fits of experimental data. In contrast, the tire models used by major tire companies are extremely complex with solutions possible only by finite element analysis. Between these two extremes exists the potential for an elasticity based shell theory tire model. Micro‐mechanics and composite laminate theories provide an integrated approach to the macroscopic behavior of the tire carcass and the tread support plies. This methodology has the capability of including centrifugal and friction forces. Finite difference methods are applied that produce reliable and accurate solutions of the tire response.

Exchange-Traded Funds (ETFs) have been widely viewed as a more liquid alternative to mutual funds. Liquidity is an important consideration while analyzing investment opportunities of ETFs. Since liquidity is not directly observable in financial markets, there exist proxies to quantify liquidity in the market. In this study, we consider 25 liquidity measures that are feasible to serve as proxies and maybe calculated using the high frequency data set available. We use a statistical model to examine whether liquidity affects the movement of price. We study this effect to show how liquidity measures impact the components of XLU ETF. We conclude that considering only one liquidity measure in an asset pricing model is not sufficient to capture the dynamics of market liquidity.