Modeling the risk process in the XploRe computing environment

A user friendly approach to modeling the risk process is presented. It utilizes the insurance library of the XploRe computing environment which is accompanied by on-line, hyperlinked and freely downloadable from the web manuals and e-books. The empirical analysis for Danish fire losses for the years 1980-90 is conducted and the best fitting of the risk process to the data is illustrated.Risk process, Monte Carlo simulation, XploRe computing environment

Modeling the risk process in the XploRe computing environment

A user friendly approach to modeling the risk process is presented. It utilizes the insurance library of the XploRe computing environment which is accompanied by on-line, hyperlinked and freely downloadable from the web manuals and e-books. The empirical analysis for Danish fire losses for the years 1980-90 is conducted and the best fitting of the risk process to the data is illustrated. --

Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S,s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants' birthweights in the range between 250 and 1500 grams.Comment: 41 pages, 9 figure

Inference for extremal conditional quantile models, with an application to market and birthweight risks

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S,s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants' birthweights in the range between 250 and 1500 grams.

Double Whammy - How ICT Projects are Fooled by Randomness and Screwed by Political Intent

The cost-benefit analysis formulates the holy trinity of objectives of project management - cost, schedule, and benefits. As our previous research has shown, ICT projects deviate from their initial cost estimate by more than 10% in 8 out of 10 cases. Academic research has argued that Optimism Bias and Black Swan Blindness cause forecasts to fall short of actual costs. Firstly, optimism bias has been linked to effects of deception and delusion, which is caused by taking the inside-view and ignoring distributional information when making decisions. Secondly, we argued before that Black Swan Blindness makes decision-makers ignore outlying events even if decisions and judgements are based on the outside view. Using a sample of 1,471 ICT projects with a total value of USD 241 billion - we answer the question: Can we show the different effects of Normal Performance, Delusion, and Deception? We calculated the cumulative distribution function (CDF) of (actual-forecast)/forecast. Our results show that the CDF changes at two tipping points - the first one transforms an exponential function into a Gaussian bell curve. The second tipping point transforms the bell curve into a power law distribution with the power of 2. We argue that these results show that project performance up to the first tipping point is politically motivated and project performance above the second tipping point indicates that project managers and decision-makers are fooled by random outliers, because they are blind to thick tails. We then show that Black Swan ICT projects are a significant source of uncertainty to an organisation and that management needs to be aware of

Expected utility and catastrophic risk in a stochastic economy-climate model

We analyze a stochastic dynamic finite-horizon economic model with climate change, in which the social planner faces uncertainty about future climate change and its economic damages. Our model (SDICE*) incorporates, possibly heavy-tailed, stochasticity in Nordhaus’ deterministic DICE model. We develop a regression-based numerical method for solving a general class of dynamic finite-horizon economy–climate models with potentially heavy-tailed uncertainty and general utility functions. We then apply this method to SDICE* and examine the effects of light- and heavy-tailed uncertainty. The results indicate that the effects can be substantial, depending on the nature and extent of the uncertainty and the social planner's preferences