This paper explores the theory behind the rich and robust family of »-stable distributions to estimate parameters from financial asset log-returns data. We discuss four-parameter estimation methods including the quantiles, logarithmic moments method, maximum likelihood (ML), and the empirical...

This paper provides a complete program for the valuation of aggregate non-life insurance liability cash flows based on claims triangle data. The valuation is fully consistent with the principle of valuation by considering the costs associated with a transfer of the liability to a so-called...

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where...

In this article, our goal is to improve the estimation of the parameters of solar photovoltaic models, we propose a method based on Simulated Annealing (SA) Optimization, the proposed algorithm takes into account the uncertainties of measurements. This algorithm consists of three steps such as...

Recently, the application of the proton exchange membrane fuel cells (PEMFCs) is extensively increasing as a popular renewable energy source. PEMFCs need low temperature for the operation along with high power density and easy implementation ability. These characteristics turned them into the...

This paper presents a new optimal method for model estimation of the unknown parameters of circuit-based proton exchange membrane fuel cells (PEMFCs). The main idea is to minimize the sum of squared error (SSE) value between the actual data and the estimated results. The optimization process...

This paper presents an optimal technique for parameter estimation of a Solid Oxide Fuel Cell (SOFC) model. The idea is to minimize the Sum of Squared Error (SSE) between the output voltage and the experimental data. To achieve this purpose, a new metaheuristic, called the Converged Grass Fibrous...

The purpose of this study is to assess model risk with respect to parameter estimation for a simple binary logistic regression model applied as a predictive model. The assessment is done by comparing the effectiveness of eleven different parameter estimation methods. The results from the...

Insurers issuing segregated fund policies apply dynamic hedging to mitigate risks related to guarantees embedded in such policies. A typical industry practice consists of using fund mapping regressions to represent basis risk stemming from the imperfect correlation between the underlying fund...