A fuzzy credibility model to estimate the Operational Value at Risk using internal and external data of risk events

Peña, Alejandro; Bonet, Isis; Lochmuller, Christian; Patiño, Héctor Alejandro; Chiclana, Francisco; Góngora, Mario

doi:10.1016/j.knosys.2018.06.007

Cited by 10 publications

(13 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Where: For Cumulative Distribution Function, the SI can be zero (CDF's -Centered), Negative (CDF's -Heavy Tails) and Positive (CDF's -Long tails) (Pena et al, 2018a(Pena et al, , 2018b.…”

Section: Skewness Indexmentioning

confidence: 99%

Evolution of the Perceptions about Tourist Destinations Affected by Risk Events Using a PANAS-tDL Deep Learning Model

Peña

Mesias

Patino

et al. 2021

J INFORM SYSTEMS ENG

Self Cite

View full text Add to dashboard Cite

The tourism industry has dynamized the economy of the countries by offering places, as well as related tourism experiences, products, and services. In the context of the COVID-19 pandemic, some of these tourist destinations were affected by subjective perceptions of users on social networks, within stands out Twitter. To achieve an objective perception from user comments posted on Twitter in front of a tourist destination, we propose a PANAS-tDL (Positive and Negative Affect Schedule -Deep Learning) model which integrates into a single structure a neural model inspired by a Stacked neural deep learning model (SDL), as well as the PANAS-t methodology. For this process, a database of comments was available for four destinations (Colombia, Italy, Spain, USA), and its tourist's products and services, before and in the context of COVID-19 pandemic throughout the year 2020. The proposed model made it possible to generate objective perceptions of the tourist destinations and their products and services using an automatic classification of comments in each category defined by the PANAS-t methodology (11-sentiments). The results show how users' perceptions were towards the negative sentiment zone defined by this methodology, according to the evolution of the COVID-19 pandemic worldwide throughout the year 2020. The proposed model also integrated an automatic process of normalisation, lemmatisation and tokenisation (Natural language process -NLP) for the objective characterization of perceptions, and due to its capacity for adaption and learning, it can be extended for the evaluation of new tourist destinations, products or services using comments from different social networks.

show abstract

“…Where: For Cumulative Distribution Function, the SI can be zero (CDF's -Centered), Negative (CDF's -Heavy Tails) and Positive (CDF's -Long tails) (Pena et al, 2018a(Pena et al, , 2018b.…”

Section: Skewness Indexmentioning

confidence: 99%

Evolution of the Perceptions about Tourist Destinations Affected by Risk Events Using a PANAS-tDL Deep Learning Model

Peña

Mesias

Patino

et al. 2021

J INFORM SYSTEMS ENG

Self Cite

View full text Add to dashboard Cite

show abstract

“…This characteristic makes them ideal for the representation of complex systems at a strategic level [43,41]. In this context, where the structure of the fuzzy sets defines the linguistic rates or nodes, the relations of causality between nodes can be expressed in terms of a fuzzy credibility model as proposed by [52]:…”

Section: Stochastic Fuzzy Logistic Map (S Flm)mentioning

confidence: 99%

“…For the development of dynamic financial multi-variable scenarios that allow to describe the EIRR behaviour over time according to the FCM structure (22), a novel stochastic fuzzy logistic map (S FLM) is proposed. This proposal allows to integrate in a single structure each of the aspects that define a financial scenario and the variables expressed as linguistic random rates, based on Definitions 3 and 5, which can be expressed as follows [37,53,52]:…”

Section: Stochastic Fuzzy Logistic Map (S Flm)mentioning

confidence: 99%

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

Self Cite

View full text Add to dashboard Cite

In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios.

show abstract

“…In [11] the authors suggest an evolving possibilistic fuzzy modeling (ePFM) approach to estimate VaR; data from the main global equity market indexes are used to estimate VaR using ePFM and the performance of ePFM is compared with traditional VaR benchmarks producing encouraging results. A growing interest for researches and practitioners is directed to VaR estimation in the case of operational risk, in [12] the intrinsic properties of the data as fuzzy sets are related to the linguistic variables of the observed data (external), allowing an organization to supervise operational VaR over time.…”

Section: Introductionmentioning

confidence: 99%

Value at Risk Based on Fuzzy Numbers

Guerra

Sorini

2020

Axioms

View full text Add to dashboard Cite

Value at Risk (VaR) has become a crucial measure for decision making in risk management over the last thirty years and many estimation methodologies address the finding of the best performing measure at taking into account unremovable uncertainty of real financial markets. One possible and promising way to include uncertainty is to refer to the mathematics of fuzzy numbers and to its rigorous methodologies which offer flexible ways to read and to interpret properties of real data which may arise in many areas. The paper aims to show the effectiveness of two distinguished models to account for uncertainty in VaR computation; initially, following a non parametric approach, we apply the Fuzzy-transform approximation function to smooth data by capturing fundamental patterns before computing VaR. As a second model, we apply the Average Cumulative Function (ACF) to deduce the quantile function at point p as the potential loss VaRp for a fixed time horizon for the 100p% of the values. In both cases a comparison is conducted with respect to the identification of VaR through historical simulation: twelve years of daily S&P500 index returns are considered and a back testing procedure is applied to verify the number of bad VaR forecasting in each methodology. Despite the preliminary nature of the research, we point out that VaR estimation, when modelling uncertainty through fuzzy numbers, outperforms the traditional VaR in the sense that it is the closest to the right amount of capital to allocate in order to cover future losses in normal market conditions.

show abstract

A fuzzy credibility model to estimate the Operational Value at Risk using internal and external data of risk events

Cited by 10 publications

References 40 publications

Evolution of the Perceptions about Tourist Destinations Affected by Risk Events Using a PANAS-tDL Deep Learning Model

Evolution of the Perceptions about Tourist Destinations Affected by Risk Events Using a PANAS-tDL Deep Learning Model

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

Value at Risk Based on Fuzzy Numbers

Contact Info

Product

Resources

About