What Happened to Risk Management During the 2008-09 Financial Crisis?

When dealing with market risk under the Basel II Accord, variation pays in the form of lower capital requirements and higher profits. Typically, GARCH type models are chosen to forecast Value-at-Risk (VaR) using a single risk model. In this paper we illustrate two useful variations to the standard mechanism for choosing forecasts, namely: (i) combining different forecast models for each period, such as a daily model that forecasts the supremum or infinum value for the VaR; (ii) alternatively, select a single model to forecast VaR, and then modify the daily forecast, depending on the recent history of violations under the Basel II Accord. We illustrate these points using the Standard and Poor’s 500 Composite Index. In many cases we find significant decreases in the capital requirements, while incurring a number of violations that stays within the Basel II Accord limits.risk management;violations;conservative risk strategy;aggressive risk strategy;value-at-risk forecast

GFC-Robust Risk Management Strategies under the Basel Accord

A risk management strategy is proposed as being robust to the Global Financial Crisis (GFC) by selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models. The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models. This risk management strategy is GFC-robust in the sense that maintaining the same risk management strategies before, during and after a financial crisis would lead to comparatively low daily capital charges and violation penalties. The new method is illustrated by using the S&P500 index before, during and after the 2008-09 global financial crisis. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria. The median VaR risk management strategy is GFC-robust as it provides stable results across different periods relative to other VaR forecasting models. The new strategy based on combined forecasts of single models is straightforward to incorporate into existing computer software packages that are used by banks and other financial institutions.Value-at-Risk (VaR);daily capital charges;optimizing strategy;robust forecasts;violation penalties;global financial crisis;Basel II Accord;aggressive risk management strategy;conservative risk management strategy

A decision rule to minimize daily capital charges in forecasting value-at-risk

Under the Basel II Accord, banks and other Authorized Deposit-taking Institutions (ADIs) have to communicate their daily risk estimates to the monetary authorities at the beginning of the trading day, using a variety of Value-at-Risk (VaR) models to measure risk. Sometimes the risk estimates communicated using these models are too high, thereby leading to large capital requirements and high capital costs. At other times, the risk estimates are too low, leading to excessive violations, so that realised losses are above the estimated risk. In this paper we propose a learning strategy that complements existing methods for calculating VaR and lowers daily capital requirements, while restricting the number of endogenous violations within the Basel II Accord penalty limits. We suggest a decision rule that responds to violations in a discrete and instantaneous manner, while adapting more slowly in periods of no violations. We apply the proposed strategy to Standard & Poor’s 500 Index and show there can be substantial savings in daily capital charges, while restricting the number of violations to within the Basel II penalty limits.value-at-risk;daily capital charges;optimizing strategy;risk forecasts;endogenous violations;frequency of violations

Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm

Electrochemical reduction of carbamazepine in ethanol and water solutions using a glassy carbon electrode

The electrochemical reduction of carbamazepine in ethanol and water using a glassy carbon electrode has been studied. In all experimental conditions of scan rate and concentration of carbamazepine an irreversible cathodic wave was observed by cyclic voltammetry (CV). Electrochemical parameters and a plausible EqC mechanism have been reported from the electrochemical measurements and digital simulation. The values of thermodynamic E1/2 were correlated with solvent polarity parameters that it can be interesting for biological, pharmaceutical and forensic purposes. Limits of Detection (LOD) for DPV are 1.1 and 9.0 g/mL (4.65x10-6 and 3.81x10-5 M) in ethanol and water, respectively. The precision and recoveries obtained for tablets and plasma samples showed that the method could be successfully used for analysis

Endoscopic diagnosis of gastric peptic ulcer penetrating into the liver

A 61-year-old man was admitted with upper gastrointestinal bleeding. Endoscopy showed a large gastric peptic ulcer with a pseudotumoral mass protruding from the ulcer bed. Histological examination of biopsies taken from the mass revealed distorted hepatic tissue and inflammatory changes. Hepatic penetration was diagnosed as the cause of bleeding. Surgery findings confirmed the endoscopic diagnosis

Parallelization of a Monte Carlo Ray Tracing Algorithm for Channel Modelling in Underwater Wireless Optical Communications

AbstractIn this paper, an algorithm to calculate the underwater wireless optical impulse response is presented. It is based on a modified Monte Carlo Ray Tracing algorithm and takes into account the most significant phenomena of the underwater channel. In order to reduce the simulation time, two parallelization schemes are proposed, one based on a multiprocessor architecture and other based on the use of GPU (Graphics Processing Unit). Several simulation results are presented, including scenario channel simulations and calculation of time computation complexity for each algorithm implementation