Can a Loan Valuation Adjustment (LVA) Approach Immunize Collateralized Debt from Defaults?

This study focuses on structuring tangible asset backed loans to inhibit their endemic option to default. We adapt the pragmatic approach of a margin loan in the configuring of collateralized debt to yield a quasi‐default‐free facility. We link our practical method to the current Basel III (2017) regulatory framework. Our new concept of the Loan Valuation Adjustment (LVA) and novel method to minimize the LVA converts the risky loan into a quasi risk‐free loan and achieves value maximization for the lending financial institution. As a result, entrepreneurial activities are promoted and economic growth invigorated. Information asymmetry, costly bailouts and resulting financial fragility are reduced while depositors are endowed with a safety net equivalent to deposit insurance but without the associated moral hazard between risk‐averse lenders and borrowers

Analysis of Field-Effect Biosensors using Self-Consistent 3D Drift-Diffusion and Monte-Carlo Simulations

AbstractField-effect biosensors based on nanowires enjoy considerable popularity due to their high sensitivity and direct electrical readout [1]. However, crucial issues such as the influence of the biomolecules on the charge-carrier transport or the binding of molecules to the surface have not been described satisfactorily yet in a quantitative manner. In order to analyze these effects, we present simulation results based on a 3D macroscopic transport model coupled with Monte-Carlo simulations for the bio-functionalized surface layer. Excellent agreement with measurement data has been found, while detailed study of the influence of the most prominent biomolecules, namely double-stranded DNA and single-stranded DNA, on the current through the semiconductor transducer has been carried out

The Bivariate Normal Copula

We collect well known and less known facts about the bivariate normal distribution and translate them into copula language. In addition, we prove a very general formula for the bivariate normal copula, we compute Gini's gamma, and we provide improved bounds and approximations on the diagonal.Comment: 24 page

Design, Verification, Test and In-Field Implications of Approximate Computing Systems

Today, the concept of approximation in computing is becoming more and more a “hot topic” to investigate how computing systems can be more energy efficient, faster, and less complex. Intuitively, instead of performing exact computations and, consequently, requiring a high amount of resources, Approximate Computing aims at selectively relaxing the specifications, trading accuracy off for efficiency. While Approximate Computing gives several promises when looking at systems’ performance, energy efficiency and complexity, it poses significant challenges regarding the design, the verification, the test and the in-field reliability of Approximate Computing systems. This tutorial paper covers these aspects leveraging the experience of the authors in the field to present state-of-the-art solutions to apply during the different development phases of an Approximate Computing system

Moody's Correlated Binomial Default Distributions for Inhomogeneous Portfolios

This paper generalizes Moody's correlated binomial default distribution for homogeneous (exchangeable) credit portfolio, which is introduced by Witt, to the case of inhomogeneous portfolios. As inhomogeneous portfolios, we consider two cases. In the first case, we treat a portfolio whose assets have uniform default correlation and non-uniform default probabilities. We obtain the default probability distribution and study the effect of the inhomogeneity on it. The second case corresponds to a portfolio with inhomogeneous default correlation. Assets are categorized in several different sectors and the inter-sector and intra-sector correlations are not the same. We construct the joint default probabilities and obtain the default probability distribution. We show that as the number of assets in each sector decreases, inter-sector correlation becomes more important than intra-sector correlation. We study the maximum values of the inter-sector default correlation. Our generalization method can be applied to any correlated binomial default distribution model which has explicit relations to the conditional default probabilities or conditional default correlations, e.g. Credit Risk${}^{+}$, implied default distributions. We also compare some popular CDO pricing models from the viewpoint of the range of the implied tranche correlation.Comment: 29 pages, 17 figures and 1 tabl

Least Dependent Component Analysis Based on Mutual Information

We propose to use precise estimators of mutual information (MI) to find least dependent components in a linearly mixed signal. On the one hand this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand it has the advantage, compared to other implementations of `independent' component analysis (ICA) some of which are based on crude approximations for MI, that the numerical values of the MI can be used for: (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output, by comparing the pairwise MIs with those of re-mixed components; (iii) clustering the output according to the residual interdependencies. For the MI estimator we use a recently proposed k-nearest neighbor based algorithm. For time sequences we combine this with delay embedding, in order to take into account non-trivial time correlations. After several tests with artificial data, we apply the resulting MILCA (Mutual Information based Least dependent Component Analysis) algorithm to a real-world dataset, the ECG of a pregnant woman. The software implementation of the MILCA algorithm is freely available at http://www.fz-juelich.de/nic/cs/softwareComment: 18 pages, 20 figures, Phys. Rev. E (in press

Estimating Mutual Information

We present two classes of improved estimators for mutual information $M(X,Y)$, from samples of random points distributed according to some joint probability density $\mu(x,y)$. In contrast to conventional estimators based on binnings, they are based on entropy estimates from $k$-nearest neighbour distances. This means that they are data efficient (with $k=1$ we resolve structures down to the smallest possible scales), adaptive (the resolution is higher where data are more numerous), and have minimal bias. Indeed, the bias of the underlying entropy estimates is mainly due to non-uniformity of the density at the smallest resolved scale, giving typically systematic errors which scale as functions of $k/N$ for $N$ points. Numerically, we find that both families become {\it exact} for independent distributions, i.e. the estimator $\hat M(X,Y)$ vanishes (up to statistical fluctuations) if $\mu(x,y) = \mu(x) \mu(y)$. This holds for all tested marginal distributions and for all dimensions of $x$ and $y$. In addition, we give estimators for redundancies between more than 2 random variables. We compare our algorithms in detail with existing algorithms. Finally, we demonstrate the usefulness of our estimators for assessing the actual independence of components obtained from independent component analysis (ICA), for improving ICA, and for estimating the reliability of blind source separation.Comment: 16 pages, including 18 figure

The Role of Industry, Geography and Firm Heterogeneity in Credit Risk Diversification

In theory the potential for credit risk diversification for banks could be substantial. Portfolio diversification is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. We propose a model for exploring these dimensions of credit risk diversification: across industry sectors and across different countries or regions. We find that full firm-level parameter heterogeneity matters a great deal for capturing differences in simulated credit loss distributions. Imposing homogeneity results in overly skewed and fat-tailed loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity greatly reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogeneous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity

Decrease in treatment intensity predicts worse outcome in patients with locally advanced head and neck squamous cell carcinoma undergoing radiochemotherapy

PURPOSE: Radiochemotherapy (RCT) is an effective standard therapy for locally advanced head and neck squamous cell carcinoma (LA-HNSCC). Nonetheless, toxicity is common, with patients often requiring dose modifications. METHODS: To investigate associations of RCT toxicities according to CTCAE version 5.0 and subsequent therapy modifications with short- and long-term treatment outcomes, we studied all 193 patients with HNSCC who received RCT (70 Gy + platinum agent) at an academic center between 03/2010 and 04/2018. RESULTS: During RCT, 77 (41%, 95% CI 34-49) patients developed at least one ≥ grade 3 toxicity, including seven grade 4 and 3 fatal grade 5 toxicities. The most frequent any-grade toxicities were xerostomia (n = 187), stomatitis (n = 181), dermatitis (n = 174), and leucopenia (n = 98). Eleven patients (6%) had their radiotherapy schedule modified (mean radiotherapy dose reduction = 12 Gy), and 120 patients (64%) had chemotherapy modifications (permanent discontinuation: n = 67, pause: n = 34, dose reduction: n = 7, change to other chemotherapy: n = 10). Objective response rates to RCT were 55% and 88% in patients with and without radiotherapy modifications (p = 0.003), and 84% and 88% in patients with and without chemotherapy modifications (p = 0.468), respectively. Five-year progression-free survival estimates were 20% and 50% in patients with and without radiotherapy modifications (p = < 0.001), and 53% and 40% in patients with and without chemotherapy modifications (p = 0.88), respectively. CONCLUSIONS: Reductions of radiotherapy dose were associated with impaired long-term outcomes, whereas reductions in chemotherapy intensity were not. This suggests that toxicities during RCT should be primarily managed by modifying chemotherapy rather than radiotherapy