scholarly journals Using EVT for Geological Anomaly Design and Its Application in Identifying Anomalies in Mining Areas

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Feilong Qin ◽  
Bingli Liu ◽  
Ke Guo
Keyword(s):  
Generalized Pareto Distribution ◽  
Mining Area ◽  
Value Theory ◽  
Study Data ◽  
Mineral Deposit ◽  
Mining Areas ◽  
Generalized Pareto ◽  
Ore Bodies ◽  
Geological Anomaly ◽  
Anomaly Region

A geological anomaly is the basis of mineral deposit prediction. Through the study of the knowledge and characteristics of geological anomalies, the category of extreme value theory (EVT) to which a geological anomaly belongs can be determined. Associating the principle of the EVT and ensuring the methods of the shape parameter and scale parameter for the generalized Pareto distribution (GPD), the methods to select the threshold of the GPD can be studied. This paper designs a new algorithm called the EVT model of geological anomaly. These study data on Cu and Au originate from 26 exploration lines of the Jiguanzui Cu-Au mining area in Hubei, China. The proposed EVT model of the geological anomaly is applied to identify anomalies in the Jiguanzui Cu-Au mining area. The results show that the model can effectively identify the geological anomaly region of Cu and Au. The anomaly region of Cu and Au is consistent with the range of ore bodies of actual engineering exploration. Therefore, the EVT model of the geological anomaly can effectively identify anomalies, and it has a high indicating function with respect to ore prospecting.

scholarly journals Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory

1997 ◽  
Vol 27 (1) ◽  
pp. 117-137 ◽  
Author(s):  
Alexander J. McNeil
Keyword(s):  
Extreme Value Theory ◽  
Generalized Pareto Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Parametric Curve ◽  
Excess Loss ◽  
Generalized Pareto ◽  
Fire Insurance ◽  
Loss Severity ◽  
Parametric Curve Fitting

AbstractGood estimates for the tails of loss severity distributions are essential for pricing or positioning high-excess loss layers in reinsurance. We describe parametric curve-fitting methods for modelling extreme historical losses. These methods revolve around the generalized Pareto distribution and are supported by extreme value theory. We summarize relevant theoretical results and provide an extensive example of their application to Danish data on large fire insurance losses.

scholarly journals Risiko Operasional Stasiun Pengisian dan Pengangkutan Bulk Elpiji pada PT Surya Artha Chanya

2014 ◽  
Vol 5 (2) ◽  
pp. 447
Author(s):  
Teguh Sriwidadi ◽  
Meivi Kristiani
Keyword(s):  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Study Data ◽  
Distribution Method ◽  
Important Indicator ◽  
Generalized Pareto ◽  
Expert Choice ◽  
Data Collection Technique ◽  
Hierarchy Process ◽  
Process Method

PT Surya Artha Chanya is a 3kg LPG refilling service company. In charging process there are some errors and the error certainly raises the risk of loss for the company. To minimize the risk in operations, risk management is attempted. This research is a descriptive study; data collection technique used was interview with internal parties company that could be trusted. Research used Analytical Hierarchy Process method to process the data questionnaire with Expert Choice 11 software which serves to compare and find the most important indicator of the operations. To determine the amount of loss that exceeds the threshold calculation, the Generalized Pareto Distribution method was used. These methods were expected to issue the company's risk of loss can be resolved.

scholarly journals ON THE TWO STEP THRESHOLD SELECTION FOR OVER-THRESHOLD MODELLING

2012 ◽  
Vol 1 (33) ◽  
pp. 42
Author(s):  
Pietro Bernardara ◽  
Franck Mazas ◽  
Jérôme Weiss ◽  
Marc Andreewsky ◽  
Xavier Kergadallan ◽  
...  
Keyword(s):  
Pareto Distribution ◽  
Extreme Values ◽  
Generalized Pareto Distribution ◽  
Value Theory ◽  
Water Levels ◽  
Distributed Data ◽  
Threshold Selection ◽  
The Past ◽  
Generalized Pareto ◽  
Selection For

In the general framework of over-threshold modelling (OTM) for estimating extreme values of met-ocean variables, such as waves, surges or water levels, the threshold selection logically requires two steps: the physical declustering of time series of the variable in order to obtain samples of independent and identically distributed data then the application of the extreme value theory, which predicts the convergence of the upper part of the sample toward the Generalized Pareto Distribution. These two steps were often merged and confused in the past. A clear framework for distinguishing them is presented here. A review of the methods available in literature to carry out these two steps is given here together with the illustration of two simple and practical examples.

Characterization of ERA5 daily precipitation using the extended generalized Pareto distribution

2020 ◽  
Author(s):  
Pauline Rivoire ◽  
Olivia Martius ◽  
Philippe Naveau
Keyword(s):  
Confidence Intervals ◽  
Pareto Distribution ◽  
Daily Precipitation ◽  
Generalized Pareto Distribution ◽  
Value Theory ◽  
Cumulative Distribution ◽  
Simultaneous Occurrence ◽  
Lower Tail ◽  
Generalized Pareto ◽  
Return Levels

<p>Both mean and extreme precipitation are highly relevant and a probability distribution that models the entire precipitation distribution therefore provides important information. Very low and extremely high precipitation amounts have traditionally been modeled separately. Gamma distributions are often used to model low and moderate precipitation amounts and extreme value theory allows to model the upper tail of the distribution. However, difficulties arise when making a link between upper and lower tail. One solution is to define a threshold that separates the distribution into extreme and non-extreme values, but the assignment of such a threshold for many locations is not trivial. </p><p>Here we apply the Extended Generalized Pareto Distribution (EGPD) used by Tencaliec & al. 2019. This method overcomes the problem of finding a threshold between upper and lower tails thanks to a transition function (G) that describes the transition between the empirical distribution of precipitation and a Pareto distribution. The transition cumulative distribution function G has to be constrained by the upper tail and lower tail behavior. G can be estimated using Bernstein polynomials.</p><p>EGPD is used here to characterize ERA-5 precipitation. ERA-5 is a new ECMWF climate re-analysis dataset that provides a numerical description of the recent climate by combining a numerical weather model with observations. The data set is global with a spatial resolution of 0.25° and currently covers the period from 1979 to present.</p><p>ERA-5 daily precipitation is compared to EOBS, a gridded dataset spatially interpolated from observations over Europe, and to CMORPH, a satellite-based global precipitation product. Simultaneous occurrence of extreme events is assessed with a hit rate. An intensity comparison is conducted with return levels confidence intervals and a Kullback Leibler divergence test, both derived from the EGPD.</p><p>Overall, extreme event occurrences between ERA5 and EOBS over Europe appear to agree. The presence of overlap between 95% confidence intervals on return levels highly depends on the season and the probability of occurrence.</p>

Using Extreme Value Theory to Model Electricity Price Risk with an Application to the Alberta Power Market

2005 ◽  
Vol 23 (5) ◽  
pp. 375-403 ◽  
Author(s):  
W. D. Walls ◽  
Wei. Zhang
Keyword(s):  
Extreme Value Theory ◽  
Value At Risk ◽  
Generalized Pareto Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Financial Assets ◽  
Historical Simulation ◽  
Generalized Pareto ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed

Value-at-risk (VaR) is a measure of the maximum potential change in value of a portfolio of financial assets with a given probability over a given time horizon. VaR has become a standard measure of market risk and a common practice is to compute VaR by assuming that changes in value of the portfolio are conditionally normally distributed. However, assets returns usually come from heavy-tailed distributions, so computing VaR under the assumption of conditional normality can be an important source of error. We illustrate in our application to competitive electric power prices in Alberta, Canada, that VaR estimates based on extreme value theory models, in particular the generalized Pareto distribution are, more accurate than those produced by alternative models such as normality or historical simulation.

scholarly journals EXPECTED SHORTFALL DENGAN PENDEKATAN GLOSTEN-JAGANNATHAN-RUNKLE GARCH DAN GENERALIZED PARETO DISTRIBUTION

2020 ◽  
Vol 9 (4) ◽  
pp. 505-514
Author(s):  
Lina Tanasya ◽  
Di Asih I Maruddani ◽  
Tarno Tarno
Keyword(s):  
Time Series ◽  
Stock Return ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Garch Model ◽  
Value Theory ◽  
Expected Shortfall ◽  
Series Data ◽  
Generalized Pareto ◽  
Heavy Tailed

Stock is a type of investment in financial assets that are many interested by investors. When investing, investors must calculate the expected return on stocks and notice risks that will occur. There are several methods can be used to measure the level of risk one of which is Value at Risk (VaR), but these method often doesn’t fulfill coherence as a risk measure because it doesn’t fulfill the nature of subadditivity. Therefore, the Expected Shortfall (ES) method is used to accommodate these weakness. Stock return data is time series data which has heteroscedasticity and heavy tailed, so time series models used to overcome the problem of heteroscedasticity is GARCH, while the theory for analyzing heavy tailed is Extreme Value Theory (EVT). In this study, there is also a leverage effect so used the asymmetric GARCH model with Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model and the EVT theory with Generalized Pareto Distribution (GPD) to calculate ES of the stock return from PT. Bank Central Asia Tbk for the period May 1, 2012-January 31, 2020. The best model chosen was ARIMA(1,0,1) GJR-GARCH(1,2). At the 95% confidence level, the risk obtained by investors using a combination of GJR-GARCH and GPD calculations for the next day is 0.7147% exceeding the VaR value of 0.6925%. 

scholarly journals The Quantitative Modeling of Operational Risk: Between G-and-H and EVT

2007 ◽  
Vol 37 (02) ◽  
pp. 265-291 ◽  
Author(s):  
Matthias Degen ◽  
Paul Embrechts ◽  
Dominik D. Lambrigger
Keyword(s):  
Value At Risk ◽  
Generalized Pareto Distribution ◽  
Operational Risk ◽  
Value Theory ◽  
Data Sets ◽  
Generalized Pareto ◽  
High Quantiles ◽  
Interesting Candidate ◽  
The One ◽  
Parameter Values

Operational risk has become an important risk component in the banking and insurance world. The availability of (few) reasonable data sets has given some authors the opportunity to analyze operational risk data and to propose different models for quantification. As proposed in Dutta and Perry [12], the parametric g-and-h distribution has recently emerged as an interesting candidate. In our paper, we discuss some fundamental properties of the g-and-h distribution and their link to extreme value theory (EVT). We show that for the g-and-h distribution, convergence of the excess distribution to the generalized Pareto distribution (GPD) is extremely slow and therefore quantile estimation using EVT may lead to inaccurate results if data are well modeled by a g-and-h distribution. We further discuss the subadditivity property of Value-at-Risk (VaR) for g-and-h random variables and show that for reasonable g and h parameter values, superadditivity may appear when estimating high quantiles. Finally, we look at the g-and-h distribution in the one-claim-causes-ruin paradigm.

scholarly journals The Quantitative Modeling of Operational Risk: Between G-and-H and EVT

2007 ◽  
Vol 37 (2) ◽  
pp. 265-291 ◽  
Author(s):  
Matthias Degen ◽  
Paul Embrechts ◽  
Dominik D. Lambrigger
Keyword(s):  
Value At Risk ◽  
Generalized Pareto Distribution ◽  
Operational Risk ◽  
Value Theory ◽  
Data Sets ◽  
Generalized Pareto ◽  
High Quantiles ◽  
Interesting Candidate ◽  
The One ◽  
Parameter Values

Operational risk has become an important risk component in the banking and insurance world. The availability of (few) reasonable data sets has given some authors the opportunity to analyze operational risk data and to propose different models for quantification. As proposed in Dutta and Perry [12], the parametric g-and-h distribution has recently emerged as an interesting candidate.In our paper, we discuss some fundamental properties of the g-and-h distribution and their link to extreme value theory (EVT). We show that for the g-and-h distribution, convergence of the excess distribution to the generalized Pareto distribution (GPD) is extremely slow and therefore quantile estimation using EVT may lead to inaccurate results if data are well modeled by a g-and-h distribution. We further discuss the subadditivity property of Value-at-Risk (VaR) for g-and-h random variables and show that for reasonable g and h parameter values, superadditivity may appear when estimating high quantiles. Finally, we look at the g-and-h distribution in the one-claim-causes-ruin paradigm.

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