Realizing stock market crashes: stochastic cusp catastrophe model of returns under the time-varying volatility

This paper develops a two-step estimation methodology, which allows us to apply catastrophe theory to stock market returns with time-varying volatility and model stock market crashes. Utilizing high frequency data, we estimate the daily realized volatility from the returns in the first step and use stochastic cusp catastrophe on data normalized by the estimated volatility in the second step to study possible discontinuities in markets. We support our methodology by simulations where we also discuss the importance of stochastic noise and volatility in deterministic cusp catastrophe model. The methodology is empirically tested on almost 27 years of U.S. stock market evolution covering several important recessions and crisis periods. Due to the very long sample period we also develop a rolling estimation approach and we find that while in the first half of the period stock markets showed marks of bifurcations, in the second half catastrophe theory was not able to confirm this behavior. Results suggest that the proposed methodology provides an important shift in application of catastrophe theory to stock markets

The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables\u2019 relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles

Identifying and Modelling Complex Workflow Requirements in Web Applications

Workflow plays a major role in nowadays business and therefore its requirement elicitation must be accurate and clear for achieving the solution closest to business’s needs. Due to Web applications popularity, the Web is becoming the standard platform for implementing business workflows. In this context, Web applications and their workflows must be adapted to market demands in such a way that time and effort are minimize. As they get more popular, they must give support to different functional requirements but also they contain tangled and scattered behaviour. In this work we present a model-driven approach for modelling workflows using a Domain Specific Language for Web application requirement called WebSpec. We present an extension to WebSpec based on Pattern Specifications for modelling crosscutting workflow requirements identifying tangled and scattered behaviour and reducing inconsistencies early in the cycle

Biogeography of Endemic Dragonflies of the Ozark-Ouachita Interior Highlands

A common pattern across many taxonomic groups is that relatively few species are widespread while the majority are restricted in their geographic ranges. Such species distributions are used to inform conservation status, which poses unique challenges for rare or cryptic species. Further, priority status is often designated within geopolitical boundaries, which may include only a portion of a species range. This, coupled with lack of distributional data, has resulted in species being designated as apparently rare throughout some portions of their range, which may not accurately reflect their overall conservation need. The Interior Highlands region of the central United States harbors a rich diversity of flora and fauna, many of which are regional endemics. Among these are four dragonfly species considered Species of Greatest Conservation Need: Ouachita spiketail (Cordulegaster talaria), Ozark Emerald (Somatochlora ozarkensis), Westfall’s snaketail (Ophiogomphus westfalli), and Ozark clubtail (Gomphurus ozarkensis). I combined species distribution modeling with field surveys to better understand the current biogeography for the two species with ample presence data (S. ozarkensis and G. ozarkensis). Additionally, models were used to project species’ distributions under two climate change scenarios of differing severity. To assess reliability of model predictions, I used two machine learning algorithms commonly used with limited, presence-only data. Current areas of suitability predicted by both algorithms largely overlapped for each species. An analysis of variable contribution showed congruence in important environmental predictors between models. Field validation of these models resulted in new detections for both species showing their utility in guiding future surveys. Future projections across two climate change scenarios showed the importance of maintaining current suitable areas as these will continue to be strongholds for these species under climate change

Some Remarks about the Complexity of Epidemics Management

Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Assessing Central Bank credibility during the ERM crises: comparing option and spot market-based forecasts

Financial markets embed expectations of central bank policy into asset prices. This paper compares two approaches that extract a probability density of market beliefs. The first is a simulatedmoments estimator for option volatilities described in Mizrach (2002); the second is a new approach developed by Haas, Mittnik and Paolella (2004a) for fat-tailed conditionally heteroskedastic time series. In an application to the 1992-93 European Exchange Rate Mechanism crises, that both the options and the underlying exchange rates provide useful information for policy makers. JEL Klassifikation: G12, G14, F31