Coherent risk measures for derivatives under Black-Scholes Economy

This paper proposes a risk measure for a portfolio of European-style derivative securities over a fixed time horizon under the Black–Scholes economy. The proposed risk measure is scenario-based along the same line as [3]. The risk measure is constructed by using the risk-neutral probability (-measure), the physical probability (-measure) and a family of subjective probability measures. The subjective probabilities are introduced by using Girsanov's theorem. In this way, we provide risk managers or regulators with the flexibility of adjusting the risk measure according to their risk preferences and subjective beliefs. The advantages of the proposed measure are that it is easy to implement and that it satisfies the four desirable properties introduced in [3], which make it a coherent risk measure. Finally, we incorporate the presence of transaction costs into our framework.postprin

Optimal dividends with debts and nonlinear insurance risk processes

postprin

Epitaxial YBa/sub 2/Cu/sub 3/O/sub y/ thin films grown on silicon with a double buffer of Eu/sub 2/CuO/sub 4//YSZ

We report a double buffer of Eu/sub 2/CuO/sub 4/ (ECO)/YSZ to improve the growth of YBa/sub 2/Cu/sub 3/O/sub y/(YBCO) on Si wafer. The ECO buffer material possesses a very stable 214-T' structure. It has excellent structural and chemical compatibilities with YBCO and YSZ. Our study showed that the epitaxy and crystallinity of YBCO deposited on Si could be considerably enhanced by using such a double buffer of ECO/YSZ. The grown films were characterized by grazing incidence X-ray reflection, rocking curve, SEM, TEM, and surface profiler. It was also found that such a double buffer could lead to a very smooth surface in the YBCO layer.published_or_final_versio

Proteomics Analysis of Ovarian Cancer Cell Lines and Tissues Reveals Drug Resistance-associated Proteins

Background: Carboplatin and paclitaxel form the cornerstone of chemotherapy for epithelial ovarian cancer, however, drug resistance to these agents continues to present challenges. Despite extensive research, the mechanisms underlying this resistance remain unclear. Materials and Methods: A 2D-gel proteomics method was used to analyze protein expression levels of three human ovarian cancer cell lines and five biopsy samples. Representative proteins identified were validated via western immunoblotting. Ingenuity pathway analysis revealed metabolomic pathway changes. Results: A total of 189 proteins were identified with restricted criteria. Combined treatment targeting the proteasome-ubiquitin pathway resulted in re-sensitisation of drug-resistant cells. In addition, examination of five surgical biopsies of ovarian tissues revealed α-enolase (ENOA), elongation factor Tu, mitochondrial (EFTU), glyceraldehyde-3-phosphate dehydrogenase (G3P), stress-70 protein, mitochondrial (GRP75), apolipoprotein A-1 (APOA1), peroxiredoxin (PRDX2) and annexin A (ANXA) as candidate biomarkers of drug-resistant disease. Conclusion: Proteomics combined with pathway analysis provided information for an effective combined treatment approach overcoming drug resistance. Analysis of cell lines and tissues revealed potential prognostic biomarkers for ovarian cancer

Selective Constraints on Amino Acids Estimated by a Mechanistic Codon Substitution Model with Multiple Nucleotide Changes

Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices. Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins. The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences.Comment: Table 9 in this article includes corrections for errata in the Table 9 published in 10.1371/journal.pone.0017244. Supporting information is attached at the end of the article, and a computer-readable dataset of the ML estimates of selective constraints is available from 10.1371/journal.pone.001724

R2L: Routing With Reinforcement Learning

In a packet network, the routes taken by traffic can be determined according to predefined objectives. Assuming that the network conditions remain static and the defined objectives do not change, mathematical tools such as linear programming could be used to solve this routing problem. However, networks can be dynamic or the routing requirements may change. In that context, Reinforcement Learning (RL), which can learn to adapt in dynamic conditions and offers flexibility of behavior through the reward function, presents as a suitable tool to find good routing strategies. In this work, we train an RL agent, which we call R2L, to address the routing problem. The policy function used in R2L is a neural network and we use an evolution strategy algorithm to determine its weights and biases. We tested R2L in two different scenarios: static and dynamic networks conditions. In the first, we used a 16-node network and experimented with different reward functions, observing that R2L was able to adapt its routing behavior accordingly. Finally, in the second experiment, we used a 5-node network topology where a given link's transmission rate changed during the simulation. In this scenario, we observed that R2L was able to deliver a competitive performance, compared to heuristic benchmarks, with changing network conditions

Optimal insurance risk control with multiple reinsurers

An optimal insurance risk control problem is discussed in a general situation where several reinsurance companies enter into a reinsurance treaty with an insurance company. These reinsurance companies adopt variance premium principles with different parameters. Dividends with fixed costs and taxes are paid to shareholders of the insurance company. Under certain conditions, a combined proportional reinsurance treaty is shown to be optimal in a class of plausible reinsurance treaties. Within the class of combined proportional reinsurance strategies, analytical expressions for the value function and the optimal strategies are obtained.postprin

Situation and development of railway transportation system simulators

2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Effects of triptolide, an active ingredient of trypterygium Wilfordii Hook F (Thunder God Vine, a traditional Chinese herb), on rheumatoid synovial fibroblast function

published_or_final_versio