Interpreting the ERM Crisis: Country-Specific and Systemic Issues

Most interpretations of the Exchange Rate Mechanism crisis in 1992-93 ignore the key role played by structural policy spillovers among European countries, and overlook the effects of coordination (or lack thereof) of monetary and exchange rate policies among the countries making up the periphery of the system. This paper provides a simple analytical framework, able to encompass the recent literature on currency crises, while developing it by bringing out the decisive role of the strategic interactions among national policy makers in a multi-country monetary and exchange rate game. In contrast to an approach that focuses exclusively on country-specific issues, a systemic view is ultimately able to unravel more coherently, and more convincingly, the "puzzles" of the ERM crisis.

Reverse sensitivity testing: What does it take to break the model?

Sensitivity analysis is an important component of model building, interpretation and validation. A model comprises a vector of random input factors, an aggregation function mapping input factors to a random output, and a (baseline) probability measure. A risk measure, such as Value-at-Risk and Expected Shortfall, maps the distribution of the output to the real line. As is common in risk management, the value of the risk measure applied to the output is a decision variable. Therefore, it is of interest to associate a critical increase in the risk measure to specific input factors. We propose a global and model-independent framework, termed ‘reverse sensitivity testing’, comprising three steps: (a) an output stress is specified, corresponding to an increase in the risk measure(s); (b) a (stressed) probability measure is derived, minimising the Kullback-Leibler divergence with respect to the baseline probability, under constraints generated by the output stress; (c) changes in the distributions of input factors are evaluated. We argue that a substantial change in the distribution of an input factor corresponds to high sensitivity to that input and introduce a novel sensitivity measure to formalise this insight. Implementation of reverse sensitivity testing in a Monte-Carlo setting can be performed on a single set of input/output scenarios, simulated under the baseline model. Thus the approach circumvents the need for additional computationally expensive evaluations of the aggregation function. We illustrate the proposed approach through a numerical example of a simple insurance portfolio and a model of a London Insurance Market portfolio used in industry

Euler allocations in the presence of non-linear reinsurance: comment on Major (2018)

Major (2018) discusses Euler/Aumann-Shapley allocations for non-linear portfolios. He argues convincingly that many (re)insurance portfolios, while non-linear, are nevertheless positively homogeneous, owing to the way that deductibles and limits are typically set. For such non-linear but homogeneous portfolio structures, he proceeds with defining and studying a particular type of capital allocation. In this comment, we build on Major's (2018) insights but take a slightly different direction, to consider Euler capital allocations for distortion risk measures applied to homogeneous portfolios. Thus, the important problem of capital allocation in portfolios with non-linear reinsurance is solved

Hypercoagulability of COVID-19 patients in Intensive Care Unit: A Report of Thromboelastography Findings and other Parameters of Hemostasis

BACKGROUND: The severe inflammatory state secondary to Covid-19 leads to a severe derangement of hemostasis that has been recently described as a state of disseminated intravascular coagulation (DIC) and consumption coagulopathy, defined as decreased platelet count, increased fibrin(ogen) degradation products such as D-dimer as well as low fibrinogen. AIMS: Whole blood from 24 patients admitted at the intensive care unit because of Covid-19 was collected and evaluated with thromboelastography by the TEG point-of-care device on a single occasion and six underwent repeated measurements on two consecutive days for a total of 30 observations. Plasma was evaluated for the other parameters of hemostasis. RESULTS: TEG parameters are consistent with a state of hypercoagulability as shown by decreased R and K values, and increased values of K angle and MA. Platelet count was normal or increased, prothrombin time and activated partial thromboplastin time were near(normal). Fibrinogen was increased and D-dimer was dramatically increased. C-reactive protein was increased. Factor VIII and von Willebrand factor (n=11) were increased. Antithrombin (n=11) was marginally decreased and protein C (n=11) was increased. CONCLUSION: The results of this cohort of patients with Covid-19 are not consistent with acute DIC, rather they support hypercoagulability together with a severe inflammatory state. These findings may explain the events of venous thromboembolism observed in some of these patients and support antithrombotic prophylaxis/treatment. Clinical trials are urgently needed to establish the type of drug, dosage and optimal duration of prophylaxis

Differential Sensitivity in Discontinuous Models

Differential sensitivity measures provide valuable tools for interpreting complex computational models used in applications ranging from simulation to algorithmic prediction. Taking the derivative of the model output in direction of a model parameter can reveal input-output relations and the relative importance of model parameters and input variables. Nonetheless, it is unclear how such derivatives should be taken when the model function has discontinuities and/or input variables are discrete. We present a general framework for addressing such problems, considering derivatives of quantile-based output risk measures, with respect to distortions to random input variables (risk factors), which impact the model output through step-functions. We prove that, subject to weak technical conditions, the derivatives are well-defined and derive the corresponding formulas. We apply our results to the sensitivity analysis of compound risk models and to a numerical study of reinsurance credit risk in a multi-line insurance portfolio

Scenario Weights for Importance Measurement (SWIM) – an R package for sensitivity analysis

The SWIM package implements a flexible sensitivity analysis framework, based primarily on results and tools developed by Pesenti et al. (2019). SWIM provides a stressed version of a stochastic model, subject to model components (random variables) fulfilling given probabilistic constraints (stresses). Possible stresses can be applied on moments, probabilities of given events, and risk measures such as Value-at-Risk and Expected Shortfall. SWIM operates upon a single set of simulated scenarios from a stochastic model, returning scenario weights, which encode the required stress and allow monitoring the impact of the stress on all model components. The scenario weights are calculated to minimise the relative entropy with respect to the baseline model, subject to the stress applied. As well as calculating scenario weights, the package provides tools for the analysis of stressed models, including plotting facilities and evaluation of sensitivity measures. SWIM does not require additional evaluations of the simulation model or explicit knowledge of its underlying statistical and functional relations; hence it is suitable for the analysis of black box models. The capabilities of SWIM are demonstrated through a case study of a credit portfolio model

In vitro demonstration of intestinal absorption mechanisms of different sugars using 3d organotypic tissues in a fluidic device

Intestinal permeability is crucial in regulating the bioavailability and, consequently, the biological effects of drugs and compounds. However, systematic and quantitative studies of the absorption of molecules are quite limited due to a lack of reliable experimental models able to mimic human in vivo responses. In this work, we present an in vitro perfused model of the small intestinal barrier using a 3D reconstructed intestinal epithelium integrated into a fluid-dynamic bioreactor (MIVO\uae) resembling the physiological stimuli of the intestinal environment. This platform was investigated in both healthy and induced pathological conditions by monitoring the absorption of two non-metabolized sugars, lactulose and mannitol, frequently used as indicators of intestinal barrier dysfunctions. In healthy conditions, an in vivo-like plateau of the percentage of absorbed sugars was reached, where mannitol absorption was much greater than lactulose absorption. Moreover, a model of pathologically altered intestinal permeability was generated by depleting extracellular Ca2+ using a calcium-specific chelator. After calcium depletion, the pattern of sugar passage observed under pathological conditions was reversed only in dynamic conditions in the MIVO\uae chamber, due to the dynamic fluid flow beneath the membrane, but not in static conditions. Therefore, the combination of the MIVO\uae with the EpiIntestinal\u2122 platform can represent a reliable in vitro model to study the passage of molecules across the healthy or pathological small intestinal barrier by discriminating the two main mechanisms of intestinal absorption