Comparing univariate and multivariate models to forecast portfolio value-at-risk

This article addresses the problem of forecasting portfolio value-at-risk (VaR) with multivariate GARCH models vis-à-vis univariate models. Existing literature has tried to answer this question by analyzing only small portfolios and using a testing framework not appropriate for ranking VaR models. In this work we provide a more comprehensive look at the problem of portfolio VaR forecasting by using more appropriate statistical tests of comparative predictive ability. Moreover, we compare univariate vs. multivariate VaR models in the context of diversified portfolios containing a large number of assets and also provide evidence based on Monte Carlo experiments. We conclude that, if the sample size is moderately large, multivariate models outperform univariate counterparts on an out-of-sample basis.Market risk, Backtesting, Conditional predictive ability, GARCH, Volatility, Capital requirements, Basel II

Combinatorial Formulae for Vassiliev Invariants from Chern-Simons Gauge Theory

We analyse the perturbative series expansion of the vacuum expectation value of a Wilson loop in Chern-Simons gauge theory in the temporal gauge. From the analysis emerges the notion of the kernel of a Vassiliev invariant. The kernel of a Vassiliev invariant of order n is not a knot invariant, since it depends on the regular knot projection chosen, but it differs from a Vassiliev invariant by terms that vanish on knots with n singular crossings. We conjecture that Vassiliev invariants can be reconstructed from their kernels. We present the general form of the kernel of a Vassiliev invariant and we describe the reconstruction of the full primitive Vassiliev invariants at orders two, three and four. At orders two and three we recover known combinatorial expressions for these invariants. At order four we present new combinatorial expressions for the two primitive Vassiliev invariants present at this order.Comment: 73 pages, latex, epsf, 18 figures, 2 table

Allosteric modulation of zinc speciation by fatty acids

Background: Serum albumin is the major protein component of blood plasma and is responsible for the circulatory transport of a range of small molecules that include fatty acids, hormones, metal ions and drugs. Studies examining the ligand-binding properties of albumin make up a large proportion of the literature. However, many of these studies do not address the fact that albumin carries multiple ligands (including metal ions) simultaneously in vivo. Thus the binding of a particular ligand may influence both the affinity and dynamics of albumin interactions with another. Scope of review: Here we review the Zn2 + and fatty acid transport properties of albumin and highlight an important interplay that exists between them. Also the impact of this dynamic interaction upon the distribution of plasma Zn2 +, its effect upon cellular Zn2 + uptake and its importance in the diagnosis of myocardial ischemia are considered. Major conclusions: We previously identified the major binding site for Zn2 + on albumin. Furthermore, we revealed that Zn2 +-binding at this site and fatty acid-binding at the FA2 site are interdependent. This suggests that the binding of fatty acids to albumin may serve as an allosteric switch to modulate Zn2 +-binding to albumin in blood plasma. General significance: Fatty acid levels in the blood are dynamic and chronic elevation of plasma fatty acid levels is associated with some metabolic disorders such as cardiovascular disease and diabetes. Since the binding of Zn2 + to albumin is important for the control of circulatory/cellular Zn2 + dynamics, this relationship is likely to have important physiological and pathological implications. This article is part of a Special Issue entitled Serum Albumin

Melt block copolymerization of ε-caprolactone and L-lactide

AB block copolymers of ε-caprolactone and (L)-lactide could be prepared by ring-opening polymerization in the melt at 110°C using stannous octoate as a catalyst and ethanol as an initiator provided ε-caprolactone was polymerized first. Ethanol initiated the polymerization of ε-caprolactone producing a polymer with ε-caprolactone derived hydroxyl end groups which after addition of L-lactide in the second step of the polymerization initiated the ring-opening copolymerization of L-lactide. The number-average molecular weights of the poly(ε-caprolactone) blocks varied from 1.5 to 5.2 × 103, while those of the poly(L-lactide) blocks ranged from 17.4 to 49.7 × 103. The polydispersities of the block copolymers varied from 1.16 to 1.27. The number-average molecular weights of the polymers were controlled by the monomer/hydroxyl group ratio, and were independent on the monomer/stannous octoate ratio within the range of experimental conditions studied. When L-lactide was polymerized first, followed by copolymerization of ε-caprolactone, random copolymers were obtained. The formation of random copolymers was attributed to the occurrence of transesterification reactions. These side reactions were caused by the ε-caprolactone derived hydroxyl end groups generated during the copolymerization of ε-caprolactone with pre-polymers of L-lactide. The polymerization proceeds through an ester alcoholysis reaction mechanism, in which the stannous octoate activated ester groups of the monomers react with hydroxyl groups

Comparing univariate and multivariate models to forecast portfolio value-at-risk

This article addresses the problem of forecasting portfolio value-at-risk (VaR) with multivariate GARCH models vis-à-vis univariate models. Existing literature has tried to answer this question by analyzing only small portfolios and using a testing framework not appropriate for ranking VaR models. In this work we provide a more comprehensive look at the problem of portfolio VaR forecasting by using more appropriate statistical tests of comparative predictive ability. Moreover, we compare univariate vs. multivariate VaR models in the context of diversified portfolios containing a large number of assets and also provide evidence based on Monte Carlo experiments. We conclude that, if the sample size is moderately large, multivariate models outperform univariate counterparts on an out-of-sample basis

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.

Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury

Genomic Data from NSCLC Tumors Reveals Correlation between SHP-2 Activity and PD-L1 Expression and Suggests Synergy in Combining SHP-2 and PD-1/PD-L1 Inhibitors

The identification of novel therapies, new strategies for combination of therapies, and repurposing of drugs approved for other indications are all important for continued progress in the fight against lung cancers. Antibodies that target immune checkpoints can unmask an immunologically hot tumor from the immune system of a patient. However, despite accounts of significant tumor regression resulting from these medications, most patients do not respond. In this study, we sought to use protein expression and RNA sequencing data from The Cancer Genome Atlas and two smaller studies deposited onto the Gene Expression Omnibus (GEO) to advance our hypothesis that inhibition of SHP-2, a tyrosine phosphatase, will improve the activity of immune checkpoint inhibitors (ICI) that target PD-1 or PD-L1 in lung cancers. We first collected protein expression data from The Cancer Proteome Atlas (TCPA) to study the association of SHP-2 and PD-L1 expression in lung adenocarcinomas. RNA sequencing data was collected from the same subjects through the NCI Genetic Data Commons and evaluated for expression of the PTPN11 (SHP-2) and CD274 (PD-L1) genes. We then analyzed RNA sequencing data from a series of melanoma patients who were either treatment naïve or resistant to ICI therapy. PTPN11 and CD274 expression was compared between groups. Finally, we analyzed gene expression and drug response data collected from 21 non-small cell lung cancer (NSCLC) patients for PTPN11 and CD274 expression. From the three studies, we hypothesize that the activity of SHP-2, rather than the expression, likely controls the expression of PD-L1 as only a weak relationship between PTPN11 and CD274 expression in either lung adenocarcinomas or melanomas was observed. Lastly, the expression of CD274, not PTPN11, correlates with response to ICI in NSCLC