Tail Point Density Estimation Using Probabilistic Fuzzy Systems
نویسندگان
چکیده
Value at Risk (VaR) is a popular measure for quantifying the market risk that a financial institution faces into a single number. Due to the complexity of financial markets, the risks associated with a portfolio may vary over time. For accurate VaR estimation, it is necessary to have flexible methods that adapt to the underlying data distribution. In this paper, we consider VaR estimation by using probabilistic fuzzy systems (PFS). Contrary to previous publications, our focus is on the modeling of the tail points of the distribution of returns. We study two approaches to designing probabilistic fuzzy VaR models that take into account the extreme values of the data and compare their performance with the performance of a GARCH model. It is found that the VaR estimation process is simplified and improved by our proposed method. Keywords— Density estimation, extreme values, fuzzy histograms, probabilistic fuzzy systems, value-at-risk.
منابع مشابه
Coupled common fixed point theorems for $varphi$-contractions in probabilistic metric spaces and applications
In this paper, we give some new coupled common fixed point theorems for probabilistic $varphi$-contractions in Menger probabilistic metric spaces. As applications of the main results, we obtain some coupled common fixed point theorems in usual metric spaces and fuzzy metric spaces. The main results of this paper improvethe corresponding results given by some authors. Finally, we give one exa...
متن کاملFunction Approximation Using Probabilistic Fuzzy Systems
We consider function approximation by fuzzy systems. Fuzzy systems are typically used for approximating deterministic functions, in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems in which the probabilistic nature of uncertainty is taken into account. Furthermore, these systems take also fuzzy uncertainty into account by their fuzzy partitioning of input and ...
متن کاملA Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin
Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover ...
متن کاملProbabilistic Optimal Operation of a Smart Grid Including Wind Power Generator Units
This paper presents a probabilistic optimal power flow (POPF) algorithm considering different uncertainties in a smart grid. Different uncertainties such as variation of nodal load, change in system configuration, measuring errors, forecasting errors, and etc. can be considered in the proposed algorithm. By increasing the penetration of the renewable energies in power systems, it is more essent...
متن کاملProbabilistic Fuzzy Systems as Additive Fuzzy Systems
Probabilistic fuzzy systems combine a linguistic description of the system behaviour with statistical properties of data. It was originally derived based on Zadeh’s concept of probability of a fuzzy event. Two possible and equivalent additive reasoning schemes were proposed, that lead to the estimation of the output’s conditional probability density. In this work we take a complementary approac...
متن کامل