投资谈判中的法律风险

达成基本的投资意向后,投资双方需要就投资金额和投资整合过程等进行具体的谈判和协商。达到双赢固然是大家所乐见的局面,但隐藏在白纸黑字下的风险却也需谨慎对待。     

Stochastic modelling of reservoir operations

The nature of hydrologic parameters in reservoir management models is uncertain. In mathematical programming models the uncertainties are dealt with either indirectly (sensitivity analysis of a deterministic model) or directly by applying a chance-constrained type of formulation or some of the stochastic programming techniques (LP and DP based models). Various approaches are reviewed in the paper. Moran's theory of storage is an alternative stochastic modelling approach to mathematical programming techniques. The basis of the approach and its application is presented. Reliability programming is a stochastic technique based on the chance-constrained approach, where the reliabilities of the chance constraints are considered as extra decision variables in the model. The problem of random event treatment in the reservoir management model formulation using reliability programming is addressed in this paper.     

Stochastic modelling of combat with reinforcement

The homogeneous combat when reinforcements are made at prespecified force levels has been modelled as a continuous-time discrete-state space Markov process. The effect of reinforcement on various combat characteristics, viz., the mean combat duration, the winning probabilities, the expected survivors, etc., is studied using the uniformization method. Two new concepts, i.e., Reinforcement Effectiveness Index and Reinforcement Parity Curve, have also been defined and are illustrated numerically.     

Stochastic modelling of tropical cyclone tracks

A stochastic model for the tracks of tropical cyclones that allows for the computerised generation of a large number of synthetic cyclone tracks is introduced. This will provide a larger dataset than previously available for the assessment of risks in areas affected by tropical cyclones. To improve homogeneity, the historical tracks are first split into six classes. The points of cyclone genesis are modelled as a spatial Poisson point process, the intensity of which is estimated using a generalised version of a kernel estimator. For these points, initial values of direction, translation speed, and wind speed are drawn from histograms of the historical values of these variables observed in the neighbourhood of the respective points, thereby generating a first 6-h segment of a track. The subsequent segments are then generated by drawing changes in theses variables from histograms of the historical data available near the cyclone’s current location. A termination probability for the track is determined after each segment as a function of wind speed and location. In the present paper, the model is applied to historical cyclone data from the western North Pacific, but it is general enough to be transferred to other ocean basins with only minor adjustments. A version for the North Atlantic is currently under preparation.     

Stochastic network optimization models for investment planning

We describe and compare stochastic network optimization models for investment planning under uncertainty. Emphasis is placed on multiperiod a sset allocation and active portfolio management problems. Myopic as well as multiple period models are considered. In the case of multiperiod models, the uncertainty in asset returns filters into the constraint coefficient matrix, yielding a multi-scenario program formulation. Different scenario generation procedures are examined. The use of utility functions to reflect risk bearing attitudes results in nonlinear stochastic network models. We adopt a newly proposed decomposition procedure for solving these multiperiod stochastic programs. The performance of the models in simulations based on historical data is discussed.Research partially supported by National Science Foundation Grant No. DCR-861-4057 and IBM Grant No. 5785. Also, support from Pacific Financial Companies is gratefully acknowledged.     

Enriched workflow modelling and Stochastic Branch-and-Bound

Workflow systems provide means and techniques for modelling, designing, performing and controlling repetitive (business) processes. The quality of commercial workflow systems is usually determined to a large extent by their versatility and multi-purpose application. One of the current trends in improving workflow systems lies in enriching modelling methods and techniques in order to enlarge design alternatives.The need for such advanced methods is particularly apparent in those fields in which the process duration can be determined only vaguely, but whose completion schedules are at the same time strictly enforced by a highly competitive market by means of fines and penalties. The risk of an overrun has to be weighed against the expected costs and benefits of certain measures reducing turn-around time and their combinations. Because they can help to avoid such penalties—or, at least, keep any potential losses low by identifying critical subprocesses and evaluate appropriate measures—modelling and evaluation techniques are becoming essential features of workflow systems.Methodologically, we use Stochastic Branch-and-Bound as a technique for finding “optimal” bundles of measures. A numerical study shows the benefits of this meta-approach by means of five stepwise-developed decision scenarios requiring rich modelling. Petri nets as a modelling tool and Stochastic Branch-and-Bound as an optimization technique determine for multi-mode resource constrained workflows of varying complexity an optimal workforce strategy with respect to the number of workers and their qualification.     

Dynamic investment strategies with demand-side and cost-side risks

Investments in cost reductions are critical for the long run success of companies that operate in dynamic and stochastic market environments. This paper studies optimal investment in cost reductions as a real option under the assumption that a single firm faces two different sources of risk, stochastic demand and input prices. We derive optimal investment strategies for a monopoly as well as a firm in a perfectly competitive market and show that in case of high marginal costs, cost reductions take place earlier in competitive than in monopoly markets. While the existence of an option to invest in cost reductions increases firm value it also increases a firm’s systematic risk. Risk can be smaller in a monopolistic than in a competitive industry.     

Stochastic modelling and optimization for environmental management

In modelling and managing complex environmental systems, inherent uncertainties of all relevant natural processes are to be taken into consideration. In the present paper diverse stochastic modelling and optimization approaches for handling such problems (primarily in the field of water quality analysis and control) are highlighted, drawing on the findings of case studies and real-world applications.     

Stochastic ordering of Gini indexes for multivariate elliptical risks

In this paper, we show that the conjecture, made by Samanthi et al. (2016), on the ordering of Gini indexes of multivariate normal risks with respect to the strength of dependence, is not true. By using the positive semi-definite ordering of covariance matrices, we can obtain the usual stochastic order of the Gini indexes for multivariate normal risks. This can be generalized to multivariate elliptical risks. We also investigate the monotonicity of the Gini indexes in the usual stochastic order when the covariance (dispersion, resp.) matrices of multivariate normal (elliptical, resp) risks increase componentwise. In addition, we derive a large deviation result for the Gini indexes of multivariate normal risks.     

Stochastic modelling and optimization of water resources systems

This work involves the simultaneous optimization of the initial design and operating policy over the life of multipurpose multireservoir water resources systems receiving stochastic inflows. The approach is based on the division of the reservoir into two imaginary water storage pools, namely, the conservation and flood pools. Based on this treatment, the optimization problem is stated using the concepts of Lagrange multipliers and parameter optimization. Two nonlinear programming techniques, namely, the generalized reduced gradient technique and the gradient projection technique, combined independently with Markovian decision are proposed to solve such a problem. To illustrate the use of the proposed techniques, the Walnut River Basin in southeastern Kansas, is employed in this work.     

Stochastic modelling of dynamic properties of nonlinear water waves

This paper describes the stochastic modelling of water waves and presents predictions of their structure. The work is based on the analysis of more than 10⁵ data points, obtained in the form of wave records during experiments in a channel¹. Both turbulent wind-driven waves and artificially generated waves superimposed on the former, were studied. The wave records were considered as random time series of events; and amplitudes, surface elevations and other characteristics were considered as stochastic processes, characterisable by their moments which were calculated from the wave records by means of a computer program. Despite the two-dimensional character of the flow, the suggested distributions are not inconsistent with oceanic data, provided that the latter are treated as two-dimensional.     

Stochastic load modelling for electric energy distribution applications

Summary The problem of electric load modelling for low aggregation levels is addressed in this paper. The objective is to obtain good “demand” and “response” behaviour models of any group of loads in an electric energy distribution system for any of the functional applications that are beeing considered in the framework of the Distribution Management Systems, aimed to improve the energy efficiency, reliability and quality of the system. A brief critical revision of the methodologies used for that purpose is in the paper, and the advantages of using approaches where physical knowledge about the load characteristics is used, are stated and demonstrated.     

Stochastic functional differential equations modelling materials with selective recall

We study a problem in stochastic functional differential equations which, in addition to a standard one-one-parameter noise term involves a random perturbation of the memory. This problem can also be regarded as a first order hyperbolic system of stochastic partial differential equations with given initial data and nonlocal boundary data. Existence and uniqueness of a solution is established and the generator of the associated Markov process is analyzed. Thereafter, for two model problems arising from first- and second-order integro-differential equations suggested by physical applications we establish asymptotic stability in probability of the associated stochastic processes.     

Stochastic modelling and simulation of a kidney transplant waiting list

We created a dynamic stochastic model to evaluate the performance of a kidney transplantation system. Our model is applicable in the context of a small country where the legislation requires that a kidney from a deceased donor should be used whenever available. Using a systematic design of simulation experiments, we performed a complex simulation study based on real medical data to explore the impact of factors representing different rates of deceased kidneys harvesting, the proportion of patients with a willing living donor and different allocation policies. On the basis of careful statistical analysis carried out by two different statistical methodologies, ANOVA and bootstrap, we draw some important conclusions about the effects of these factors and recommendations for the medical community. The results of the study clearly demonstrate that in addition to increasing the numbers of kidney donors, deceased as well as living, the introduction of a kidney exchange program leads to further expansion of the numbers of donations and to shortening of waiting time for transplantation. Moreover, we observed that the largest and most counter-intuitive effect on waiting time and transplantation probability was obtained by replacing the currently implemented first-come-first-transplanted allocation policy to a policy that prioritizes the most vulnerable group of patients. This change has led to shortening the waiting time of these patients by enormous 28 months on average while leaving the waiting time of other patients practically the same.

     

Some further ideas concerning the interaction between insurance and investment risks

For many years it has been a frequently discussed question which is more important: insurance or investment risk. Based on Bühlmann’s (1995) method to separate these two risks with the help of conditional expectations, this paper presents a decomposition of the prospective portfolio loss into a sum of three addends that uniquely correspond to unsystematic insurance risk, systematic insurance risk, and investment risk. Calculating their variances for homogeneous portfolios of term life and pure endowment insurances shows that answering the initial question is more complex than frequently thought. In a second step, an extended duration concept is introduced, which allows one to analyze the non-diversifiable investment and systematic insurance in view of parameter changes at certain points in time.     

On consumption/investment problems with long-term time-average utilities

We study consumption/investment problems with long-term time-average utilities. The associated Hamilton-Jacobi-Bellman equation can be solved under some regularity conditions of utility rate function, and the optimal portfolio and consumption-rates are exhibited in explicit forms. An application to the optimization problem with finite horizon is also given     

Stochastic birth-and-growth processes modelling crystallization of polymers with spatially heterogeneous parameters

The morphology of a crystalline polymeric material is characterized by the crystallization process, which occurs during the solidification from the liquid state. This process includes a stochastic birth process (nucleation) and the growth of crystals. From a mathematical point of view, the nucleation process is a marked point process with a stochastic intensity depending on the history of the proceses of birth and growth. Here we consider an extension of the classical Avrami–Kolmogorov model to the case of space-time heterogeneous kinetic parameters. Concepts from stochastic geometry are used, which include the so called causal cone. The Markov property of the process is also analysed.     

Robust optimal reinsurance and investment strategies for an AAI with multiple risks

This paper studies the robust optimal reinsurance and investment problem for an ambiguity averse insurer (abbr. AAI). The AAI sells insurance contracts and has access to proportional reinsurance business. The AAI can invest in a financial market consisting of four assets: one risk-free asset, one bond, one inflation protected bond and one stock, and has different levels of ambiguity aversions towards the risks. The goal of the AAI is to seek the robust optimal reinsurance and investment strategies under the worst case scenario. Here, the nominal interest rate is characterized by the Vasicek model; the inflation index is introduced according to the Fisher’s equation; and the stock price is driven by the Heston’s stochastic volatility model. The explicit forms of the robust optimal strategies and value function are derived by introducing an auxiliary robust optimal control problem and stochastic dynamic programming method. In the end of this paper, a detailed sensitivity analysis is presented to show the effects of market parameters on the robust optimal reinsurance policy, the robust optimal investment strategy and the utility loss when ignoring ambiguity.     

Stochastic modelling of the effect of environmental disturbance on the population dynamics

The dynamics of a population, with its growth characterised by two stages namely an initial non-reproductive stage of length ρ, resistant to the environmental fluctuations and a second susceptible stage adding continuously to the population is modelled. The environment alternates in its character being hostile and favourable.The favourable periods are independently and identically distributed random variables and during the constantharsh periods all the adults in the population are wiped out While the existing models tacitly assume the environmental period to be much smaller than the biological period ρ, our modelling enables us to consider the two periods to be of comparable scale. In such a case, apart from the various statistical characteristics of interest derived, we show that the average extinction time increases with increasing duration of the disturbance, a result which is counter-intutive.Numerical evaluation of the time for extinction for certain values of the parameters involved are made.     

Stochastic field processes in the mathematical modelling of damped transmission line vibrations

Wind-excited vibrations in the frequency range of 10 to 50 Hz due to vortex shedding often cause fatigue failures in the cables of overhead transmission lines. Damping devices, such as the Stockbridge dampers, have been in use for a long time for supressing these vibrations. The dampers are conveniently modelled by means of their driving point impedance, measured in the lab over the frequency range under consideration. The cables can be modelled as strings with additional small bending stiffness. The main problem in modelling the vibrations does however lay in the aerodynamic forces, which usually are approximated by the forces acting on a rigid cylinder in planar flow. In the present paper, the wind forces are represented by stochastic processes with arbitrary crosscorrelation in space; the case of a Kármán vortex street on a rigid cylinder in planar flow is contained as a limit case in this approach. The authors believe that this new view of the problem may yield useful results, particularly also concerning the reliability of the lines and the probability of fatigue damages.