The Influence of Dispersion Stochastic Variation on Bright Soliton Transmision System

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基于Allan方差的MEMS陀螺随机误差建模

针对由于MEMS陀螺随机误差较大而影响MEMS惯性测量系统测量精度的问题,提出一种利用Allan方差分析随机误差并建模的方法。在分析Allan方差原理的基础上,通过Allan方差分析法分离和辨识了MEMS陀螺仪的各项随机误差以及误差系数,并利用随机误差系数进行了数学建模。通过与ARMA模型比较,表明利用Allan方差建立的模型更加精确。该方法为MEMS惯性导航系统中姿态测量的误差补偿和滤波提供了新的思路,对提高MEMS惯性测量系统的测量精度具有一定的实际应用价值。     

基于蒙特卡罗方法的几何因子计算程序

应用蒙特卡罗方法求解几何因子,基于蒙特卡罗方法的几何因子计算程序使用C++语言编写,可用于任意位姿的各种尺寸的圆面探测器对圆面源几何因子的计算。该程序使用了方差减小技巧。通过与国际通用蒙特卡罗计算程序(MCNP5)的计算结果对比,该方法具有结果准确(误差较小)、计算速度快、使用方便等优点。最终使用该程序计算几何因子,与实验数据进行对比,成功验证了中子深度分布分析(NDP)能谱测量系统探测器位姿的准确性(误差5%以内),并对其移动位置进行修正,发现电机移动20mm大约会产生1mm的误差。     

气相色谱-质谱法测定猪可食性组织中3种β-兴奋剂残留的基质效应

建立了气相色谱-质谱法测定猪可食性组织中克伦特罗、沙丁胺醇及莱克多巴胺3种β-兴奋剂残留的分析方法,评价了基质的状态和重量等因素对基质效应的影响。用N,O-双三甲基硅基三氟乙酰胺衍生,气相色谱-质谱选择离子监测模式测定猪肝和肌肉组织及其相应冻干粉中3种药物残留及其基质效应强度,均显示显著的基质增强效应,特别是莱克多巴胺的基质效应超过1000%。方差分析表明,不同基质重量的基质效应差异显著(P0.05),样品重量在1~5 g范围内,3种药物的基质效应随样品重量增加而增大;猪肝和猪肉鲜样与其相应冻干粉的基质效应差异均不显著(P0.05)。选择猪组织的冻干粉配制基质匹配标准溶液,能够有效、方便地校正气相色谱-质谱测定β-兴奋剂残留的基质效应。     

AOT微乳液中通过水化电子产额调控合成BaSO_4纳米纤维

通过γ-辐照含有K2S2O8和BaCl2的二(2-乙基己基)琥珀酸酯磺酸钠(AOT)反相微乳液,将S2O82-通过辐射还原实现了SO42-的原位缓释,从而成功制备出BaSO4纳米纤维单晶,并进一步制得多层次的纳米纤维束结构.在此基础上,通过改变水与表面活性剂物质的量之比(ω值)、改变钡盐阴离子和在微乳液连续相添加芳香化合物等手段来调节水化电子(e-aq)产额,控制微乳液水池中S2O82-的还原和SO42-的缓释速率,成功实现了对BaSO4纳米粒子形貌的调控:随着ω值的增加或剂量率的增加,e-aq产额增加,从而加快了SO42-的释放,不利于BaSO4纳米纤维的生成;采用Ba(NO3)2为钡源时,NO3-能有效地降低e-aq产额和S2O82-的还原速率,因而在较高的剂量率和较高ω值下能得到BaSO4纳米纤维;在微乳液油相中加入甲苯来捕获油相中过量电子(e-oil),降低e-aq产额,从而在较高的剂量率下得到BaSO4纳米纤维.研究结果表明:通过e-aq产额调控纳米粒子形貌的机理在BaSO4纳米粒子的制备中得到很好体现.     

Interpolation theorem for Marcinkiewicz spaces with applications to Lorentz gamma spaces

This paper is devoted to the interpolation principle between spaces of weak type. We characterise interpolation spaces between two Marcinkiewicz spaces in terms of Hardy type operators involving suprema. We study general properties of such operators and their behavior on Lorentz gamma spaces. A particular emphasis is placed on elementary and comprehensive proofs.     

Tail risk measures and risk allocation for the class of multivariate normal mean–variance mixture distributions

The Conditional Tail Expectation (CTE), also known as the Expected Shortfall and Tail-VaR, has received much attention as a preferred risk measure in finance and insurance applications. A related risk management exercise is to allocate the amount of the CTE computed for the aggregate or portfolio risk into individual risk units, a procedure known as the CTE allocation. In this paper we derive analytic formulas of the CTE and its allocation for the class of multivariate normal mean–variance mixture (NMVM) distributions, which is known to be extremely flexible and contains many well-known special cases as its members. We also develop the closed-form expression of the conditional tail variance (CTV) for the NMVM class, an alternative risk measure proposed in the literature to supplement the CTE by capturing the tail variability of the underlying distribution. To illustrate our findings, we focus on the multivariate Generalized Hyperbolic Distribution (GHD) family which is a popular subclass of the NMVM in connection with Lévy processes and contains some common distributions for financial modelling. In addition, we also consider the multivariate slash distribution which is not a member of GHD family but still belongs to the NMVM class. Our result is an extension of the recent contribution of Ignatieva and Landsman (2015).     

Stochastic Stackelberg differential reinsurance games under time-inconsistent mean–variance framework

We study optimal reinsurance in the framework of stochastic Stackelberg differential game, in which an insurer and a reinsurer are the two players, and more specifically are considered as the follower and the leader of the Stackelberg game, respectively. An optimal reinsurance policy is determined by the Stackelberg equilibrium of the game, consisting of an optimal reinsurance strategy chosen by the insurer and an optimal reinsurance premium strategy by the reinsurer. Both the insurer and the reinsurer aim to maximize their respective mean–variance cost functionals. To overcome the time-inconsistency issue in the game, we formulate the optimization problem of each player as an embedded game and solve it via a corresponding extended Hamilton–Jacobi–Bellman equation. It is found that the Stackelberg equilibrium can be achieved by the pair of a variance reinsurance premium principle and a proportional reinsurance treaty, or that of an expected value reinsurance premium principle and an excess-of-loss reinsurance treaty. Moreover, the former optimal reinsurance policy is determined by a unique, model-free Stackelberg equilibrium; the latter one, though exists, may be non-unique and model-dependent, and depend on the tail behavior of the claim-size distribution to be more specific. Our numerical analysis provides further support for necessity of integrating the insurer and the reinsurer into a unified framework. In this regard, the stochastic Stackelberg differential reinsurance game proposed in this paper is a good candidate to achieve this goal.     

On Carr and Lee’s Correlation Immunization Strategy

In their seminal work Robust Replication of Volatility Derivatives, Carr and Lee show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset’s volatility process is independent of the Brownian motion that drives the asset’s price. Additionally, they propose a correlation immunization strategy that minimizes the pricing and hedging error that results when the correlation between the risky asset’s price and volatility is non-zero. In this paper, we show that the correlation immunization strategy is the only strategy among the class of strategies discussed in Carr and Lee's paper that results in real-valued hedging portfolios when the correlation between the asset’s price and volatility is non-zero. Additionally, we perform a number of Monte Carlo experiments to test the effectiveness of Carr and Lee’s immunization strategy. Our results indicate that the correlation immunization method is an effective means of reducing pricing and hedging errors that result from a non-zero correlation.     

Mean‐variance hedging with basis risk

Basis risk arises in a number of financial and insurance risk management problems when the hedging assets do not perfectly match the underlying asset in a hedging program. Notable examples in insurance include the hedging for longevity risks, weather index–based insurance products, variable annuities, etc. In the presence of basis risk, a perfect hedging is impossible, and in this paper, we adopt a mean‐variance criterion to strike a balance between the expected hedging error and its variability. Under a time‐dependent diffusion model setup, explicit optimal solutions are derived for the hedging target being either a European option or a forward contract. The solutions are obtained by a delicate application of the linear quadratic control theory, the method of backward stochastic differential equation, and Malliavin calculus. A numerical example is presented to illustrate our theoretical results and their interesting implications.