INSURANCE VALUATION: A TWO-STEP GENERALISED REGRESSION APPROACH

Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.

Comparison between classical ‘P&O’ algorithm and FLC of MPPT for GPV under partial shading

<p><span lang="EN-US">When the GPV is under partial shading, several peaks appear in the characteristic P-V, namely a GMP and one or more local maximums. The classical algorithm ‘P&amp;O’ MPPT cannot converge on the GMP for low irradiation values and is trapped by tracking down a LMP so making the algorithm ineffective making the algorithm ineffective, in this case under 200 W/m². An alternative objective function is developed to optimize the performance of the FLC by selecting the appropriate gains using PSO. In this simulation the GPV is composed of one hundred modules grouped parallel series (10x10) and subjected to partial shading. The proposed FLC provides better performance for GMP tracking for the chosen shade configuration selected.</span></p>

On integer linear programming formulations of a patrol boat scheduling problem with complete coverage requirements

The Patrol Boat Scheduling Problem with Complete Coverage (PBSPCC) is concerned with finding a minimum size patrol boat fleet to provide continuous coverage at a set of maritime patrol regions, ensuring that there is at least one vessel on station in each patrol region at any given time. This requirement is complicated by the necessity for patrol vessels to be replenished on a regular basis in order to carry out patrol operations indefinitely. In this paper, we establish a number of important theoretical results for the PBSPCC. In particular, we establish a set of conditions under which an alternative objective function (minimize the total time not spent on patrol) can be used to derive a minimum size fleet. Preliminary results suggest that the new theoretical insights can be used as part of an acceleration strategy to improve the column generation runtime performance.

Broadband Energy Harvesting From Vibrations Using Magnetic Transduction

In this paper, an adaptive electromagnetic energy harvester is proposed and analyzed. It is composed of an oscillating mass equipped with an electromagnetic transducer, whose pins are connected to a resonant resistive–inductive–capacitive electric circuit in order to increase its effective bandwidth. Closed-form expressions for the optimal circuit parameters are presented, which maximize the power harvested by the device under harmonic excitation. The harvesting efficiency, defined as the ratio between the harvested power and the power absorbed by the oscillating device, is also reported. It is used as an alternative objective function for the optimization of the harvester circuit parameters.

A noise‐optimized objective method for predictive deconvolution

Predictive deconvolution filters are designed to remove as much predictable energy as possible from the input data. It is generally understood that temporally correlated geology can cause problems for these filters. It is perhaps less well appreciated that uncorrelated random noise can also severely affect filter performance. The root of these problems is in the objective function being minimized; in addition to minimizing predictable multiple energy, the filter is attempting to simultaneously minimize the temporally correlated geology and the random‐noise energy. Instead of minimizing the input trace energy, an alternative objective function for minimization can be defined that is the result of a linear operator acting on the input data. Ideally this alternative objective function contains only the targeted noise (e.g., multiples). The linear operator that creates this objective function is designated as the “noise‐optimized objective” (NOO) operator. The filter that minimizes this new objective function is the NOO filter. Useful NOO operators for multiple suppression are those that maximize multiple energy and/or minimize primary or random noise energy in the data. Examples of such linear operators include stacking, bandpass filtering, dip filtering, and muting or scaling. Simply scaling down the primary‐containing portion of the objective function can address the problematic removal of correlated geology. Stacking can also be a useful NOO operator. By minimizing the predictable energy on a stacked trace, the prestack filters are less affected by random noise. The NOO stacking method differs from a standard poststack filter design because the filters are designed to be applied prestack. Further, this method differs from a standard prestack prediction filter because it minimizes the predictable energy on the stacked trace. The standard prestack filter has reduced multiple suppression because the filter must compromise between minimizing the multiple energy and minimizing the random noise energy. Minimizing the impact of random noise can be quite important in prediction filtering. At a signal‐to‐random‐noise ratio of one, for example, half the multiple remains after filtering. This random noise‐related degradation might help to explain the common observation that prediction filters tend to leave multiple energy in the data. A time‐varying gap implementation of a stacking NOO filter addresses these random noise effects while also addressing data aperiodicity issues.