A class of nonzero-sum investment and reinsurance games subject to systematic risks

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Recently, there have been numerous insightful applications of zero-sum stochastic differential games in insurance, as discussed in Liu et al. [Liu, J., Yiu, C. K.-F. & Siu, T. K. (2014). Optimal investment of an insurer with regime-switching and risk constraint. Scandinavian Actuarial Journal 2014(7), 583–601]. While there could be some practical situations under which nonzero-sum game approach is more appropriate, the development of such approach within actuarial contexts remains rare in the existing literature. In this article, we study a class of nonzero-sum reinsurance-investment stochastic differential games between two competitive insurers subject to systematic risks described by a general compound Poisson risk model. Each insurer can purchase the excess-of-loss reinsurance to mitigate both systematic and idiosyncratic jump risks of the inter-arrival claims; and can invest in one risk-free asset and one risky asset whose price dynamics follows the famous Heston stochastic volatility model [Heston, S. L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. Review of Financial Studies6, 327–343]. The main objective of each insurer is to maximize the expected utility of his terminal surplus relative to that of his competitor. Dynamic programming principle for this class of nonzero-sum game problems leads to a non-canonical fixed-point problem of coupled non-linear integral-typed equations. Despite the complex structure, we establish the unique existence of the Nash equilibrium reinsurance-investment strategies and the corresponding value functions of the insurers in a representative example of the constant absolute risk aversion insurers under a mild, time-independent condition. Furthermore, Nash equilibrium strategies and value functions admit closed forms. Numerical studies are also provided to illustrate the impact of the systematic risks on the Nash equilibrium strategies. Finally, we connect our results to that under the diffusion-approximated model by proving explicitly that the Nash equilibrium under the diffusion-approximated model is an (Formula presented.) -Nash equilibrium under the general Poisson risk model, thereby establishing that the analogous Nash equilibrium in Bensoussan et al. [Bensoussan, A., Siu, C. C., Yam, S. C. P. & Yang, H. (2014). A class of nonzero-sum stochastic differential investment and reinsurance games. Automatica50(8), 2025–2037] serves as an interesting complementary case of the present framework

A novel induction machine design suitable for inverter-driven variable speed systems

Induction machines designed for inverter-driven variable speed systems are different from those fed directly from a utility power line. In this paper, a novel design approach for inverter driven induction machines is presented and implemented. This is followed by an investigation on sizing equations and rotor slot shape specifically for this purpose. The proposed approach permits the integration of the design of machines with inverters, comprehensive performance analysis, and system optimization, resulting in 20-30% higher power density for the induction machine than those designed for direct utility power supplies by conventional methods. Simulation analysis and experimental results are presented to substantiate the conclusions.published_or_final_versio

Intra-genomic internal transcribed spacer region sequence heterogeneity and molecular diagnosis in clinical microbiology

Internal transcribed spacer region (ITS) sequencing is the most extensively used technology for accurate molecular identification of fungal pathogens in clinical microbiology laboratories. Intra-genomic ITS sequence heterogeneity, which makes fungal identification based on direct sequencing of PCR products difficult, has rarely been reported in pathogenic fungi. During the process of performing ITS sequencing on 71 yeast strains isolated from various clinical specimens, direct sequencing of the PCR products showed ambiguous sequences in six of them. After cloning the PCR products into plasmids for sequencing, interpretable sequencing electropherograms could be obtained. For each of the six isolates, 10–49 clones were selected for sequencing and two to seven intra-genomic ITS copies were detected. The identities of these six isolates were confirmed to be Candida glabrata (n = 2), Pichia (Candida) norvegensis (n = 2), Candida tropicalis (n = 1) and Saccharomyces cerevisiae (n = 1). Multiple sequence alignment revealed that one to four intra-genomic ITS polymorphic sites were present in the six isolates, and all these polymorphic sites were located in the ITS1 and/or ITS2 regions. We report and describe the first evidence of intra-genomic ITS sequence heterogeneity in four different pathogenic yeasts, which occurred exclusively in the ITS1 and ITS2 spacer regions for the six isolates in this study.published_or_final_versio

Can interference patterns in the reflectance spectra of GaN epilayers give important information of carrier concentration?

Low-temperature reflectance spectra of a series of Si-doped GaN epilayers with different doping concentrations grown on sapphire by metal-organic chemical vapour deposition were measured. In addition to the excitonic polariton resonance structures at the band edge, interference oscillating patterns were observed in the energy region well below the band gap. The amplitudes of these oscillation patterns show a distinct dependence on the doping concentrations of the samples. From the thin-film optical interference principle, an approach connecting the amplitude of the interference oscillations and the impurity scattering was established. Good agreement between experiment and theory is achieved. © 2012 American Institute of Physics.published_or_final_versio

Mining Uncertain Sequential Patterns in Iterative MapReduce

This paper proposes a sequential pattern mining (SPM) algorithm in large scale uncertain databases. Uncertain sequence databases are widely used to model inaccurate or imprecise timestamped data in many real applications, where traditional SPM algorithms are inapplicable because of data uncertainty and scalability. In this paper, we develop an efficient approach to manage data uncertainty in SPM and design an iterative MapReduce framework to execute the uncertain SPM algorithm in parallel. We conduct extensive experiments in both synthetic and real uncertain datasets. And the experimental results prove that our algorithm is efficient and scalable

Positron-annihilation study of compensation defects in InP

Positron-annihilation lifetime and positron-annihilation Doppler-broadening (PADB) spectroscopies have been employed to investigate the formation of vacancy-type compensation defects in n-type undoped liquid encapsulated Czochrolski grown InP, which undergoes conduction-type conversions under high temperature annealing. N-type InP becomes p-type semiconducting by short time annealing at 700°C, and then turns into n-type again after further annealing but with a much higher resistivity. Long time annealing at 950°C makes the material semi-insulating. Positron lifetime measurements show that the positron average lifetime τ av increases from 245 ps to a higher value of 247 ps for the first n-type to p-type conversion and decreases to 240 ps for the ensuing p-type to n-type conversion. The value of τ av increases slightly to 242 ps upon further annealing and attains a value of 250 ps under 90 h annealing at 950°C. These results together with those of PADB measurements are explained by the model proposed in our previous study. The correlation between the characteristics of positron annihilation and the conversions of conduction type indicates that the formation of vacancy-type defects and the progressive variation of their concentrations during annealing are related to the electrical properties of the bulk InP material. © 2002 American Institute of Physics.published_or_final_versio

Towards Efficient Sequential Pattern Mining in Temporal Uncertain Databases

Uncertain sequence databases are widely used to model data with inaccurate or imprecise timestamps in many real world applications. In this paper, we use uniform distributions to model uncertain timestamps and adopt possible world semantics to interpret temporal uncertain database. We design an incremental approach to manage temporal uncertainty efficiently, which is integrated into the classic pattern-growth SPM algorithm to mine uncertain sequential patterns. Extensive experiments prove that our algorithm performs well in both efficiency and scalability

Universal scaling relation in high-temperature superconductors

Scaling laws express a systematic and universal simplicity among complex systems in nature. For example, such laws are of enormous significance in biology. Scaling relations are also important in the physical sciences. The seminal 1986 discovery of high transition-temperature (high-T_c) superconductivity in cuprate materials has sparked an intensive investigation of these and related complex oxides, yet the mechanism for superconductivity is still not agreed upon. In addition, no universal scaling law involving such fundamental properties as T_c and the superfluid density \rho_s, a quantity indicative of the number of charge carriers in the superconducting state, has been discovered. Here we demonstrate that the scaling relation \rho_s \propto \sigma_{dc} T_c, where the conductivity \sigma_{dc} characterizes the unidirectional, constant flow of electric charge carriers just above T_c, universally holds for a wide variety of materials and doping levels. This surprising unifying observation is likely to have important consequences for theories of high-T_c superconductivity.Comment: 11 pages, 2 figures, 2 table

Classification tree methods for panel data using wavelet-transformed time series

Wavelet-transformed variables can have better classification performance for panel data than using variables on their original scale. Examples are provided showing the types of data where using a wavelet-based representation is likely to improve classification accuracy. Results show that in most cases wavelet-transformed data have better or similar classification accuracy to the original data, and only select genuinely useful explanatory variables. Use of wavelet-transformed data provides localized mean and difference variables which can be more effective than the original variables, provide a means of separating “signal” from “noise”, and bring the opportunity for improved interpretation via the consideration of which resolution scales are the most informative. Panel data with multiple observations on each individual require some form of aggregation to classify at the individual level. Three different aggregation schemes are presented and compared using simulated data and real data gathered during liver transplantation. Methods based on aggregating individual level data before classification outperform methods which rely solely on the combining of time-point classifications