• Atmosphere
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• v.26 no.4
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• pp.697-709
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• 2016

This study estimates and evaluates the extreme value of 30 m-resolution daily maximum and minimum temperatures over South Korea, using inverse distance weighting (IDW), parameter-elevation regression on independent slopes model (PRISM) and generalized extreme value (GEV) method. The three experiments are designed and performed to find the optimal estimation strategy to obtain extreme value. First experiment (EXP1) applies GEV firstly to automated surface observing system (ASOS) to estimate extreme value and then applies IDW to produce high-resolution extreme values. Second experiment (EXP2) is same as EXP1, but using PRISM to make the high-resolution extreme value instead of IDW. Third experiment (EXP3) firstly applies PRISM to ASOS to produce the high-resolution temperature field, and then applies GEV method to make high resolution extreme value data. By comparing these 3 experiments with extreme values obtained from observation data, we find that EXP3 shows the best performance to estimate extreme values of maximum and minimum temperatures, followed by EXP1 and EXP2. It is revealed that EXP1 and EXP2 have a limitation to estimate the extreme value at each grid point correctly because the extreme values of these experiments with 30 m-resolution are calculated from only 60 extreme values obtained from ASOS. On the other hand, the extreme value of EXP3 is similar to observation compared to others, since EXP3 produces 30m-resolution daily temperature through PRISM, and then applies GEV to that result at each grid point. This result indicates that the quality of statistically produced high-resolution extreme values which are estimated from observation data is different depending on the combination and procedure order of statistical methods.

• The Korean Journal of Financial Management
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• v.22 no.1
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• pp.119-146
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• 2005

This study applies extreme value theory to get extreme value-VAR for Korean Stock market and showed the usefulness of the approach. Block maxima model and POT model were used as extreme value models and tested which model was more appropriate through back testing. It was shown that the block maxima model was unstable as the variation of the estimate was very large depending on the confidence level and the magnitude of the estimates depended largely on the block size. This shows that block maxima model was not appropriate for Korean Stock market. On the other hand POT model was relatively stable even though extreme value VAR depended on the selection of the critical value. Back test also showed VAR showed a better result than delta VAR above 97.5% confidence level. POT model performs better the higher the confidence level, which suggests that POT model is useful as a risk management tool especially for VAR estimates with a confidence level higher than 99%. This study picks up the right tail and left tail of the return distribution and estimates the EVT-VAR for each, which reflects the asymmetry of the return distribution of the Korean Stock market.

• Wind and Structures
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• v.1 no.1
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• pp.25-42
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• 1998

Improvements to the Gumbel method of extreme value analysis of wind data made over the last two decades are reviewed and illustrated using sample data for Jersey. A new procedure for extending the Gumbel method to include M-th highest annual extremes is shown to be less effective than the standard method, but leads to a method for calibrating peak-over-threshold methods against the standard Gumbel approach. Peak-over-threshold methods that include at least the 3rd highest annual extremes, specifically the modified Jensen and Franck method and the "Method of independent storms" are shown to give the best estimates of extremes from observations.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.4 no.4
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• pp.250-260
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• 1992

Two methods for computing extreme sea levels, which are the extreme probability method and the joint probability method, are examined at five different ports (Incheon, Cheju, Yeosu, Pusan, Mukho). The extreme probability mothod estimates the extreme sea levels from three different probability papers of Gumbel, Weibull and generalized extreme value(GEV) using the least square method, conventional moment method and probability weighted moment method. respectively. The results showed that the extreme sea levels estimated by the Gumbel paper or the least square method appeared higher than those calculated by other papers or methods. The extreme values estimated by the extreme probability method are approximately 5-10 cm lower than the values by the joint probability method.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.655-665
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• 2014

An adequate understanding and response to natural hazards such as heat wave, heavy rainfall and severe drought is required. We apply extreme value theory to analyze these abnormal weather phenomena. It is common for extremes in climatic data to be nonstationary in space and time. In this paper, we analyze summer rainfall data in South Korea using exceedance values over thresholds estimated by quantile regression with location information and time as covariates. We group weather stations in South Korea into 5 clusters and t extreme value models to threshold exceedances for each cluster under the assumption of independence in space and time as well as estimates of uncertainty for spatial dependence as proposed in Northrop and Jonathan (2011).

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.757-773
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• 2012

Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.

• Wind and Structures
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• v.16 no.4
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• pp.355-372
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• 2013

The Gringorten estimator has been extensively used in extreme value analysis of wind speed records to obtain unbiased estimates of design wind speeds. This paper reviews the derivation of the Gringorten estimator for the mean plotting position of extremes drawn from parents of the exponential type and demonstrates how it eliminates most of the bias caused by the classical Weibull estimator. It is shown that the coefficients in the Gringorten estimator are the asymptotic values for infinite sample sizes, whereas the estimator is most often used for small sample sizes. The principles used by Gringorten are used to derive a new Consistent Linear Unbiased Estimator (CLUE) for the mean plotting positions for the Fisher Tippett Type 1, Exponential and Weibull distributions and for the associated standard deviations. Analytical and Bootstrap methods are used to calibrate the bias error in each of the estimators and to show that the CLUE are accurate to better than 1%.

• Wind and Structures
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• v.3 no.3
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• pp.143-158
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• 2000

This report presents the findings of a one-year monitoring effort to empirically characterize and evaluate the nature of near-ground winds for structural engineering purposes. The current wind engineering practice in the United States does not explicitly consider certain important near-ground wind characteristics in typical rough terrain conditions and the possible effect on efficient design of low-rise structures, such as homes and other light-frame buildings that comprise most of the building population. Therefore, near ground wind data was collected for the purpose of comparing actual near-ground wind characteristics to the current U.S. wind engineering practice. The study provides data depicting variability of wind speeds, wind velocity profiles for a major thunderstorm event and a northeaster, and the influence of thunderstorms on annual extreme wind speeds at various heights above ground in a typical rough environment. Data showing the decrease in the power law exponent with increasing wind speed is also presented. It is demonstrated that near-ground wind speeds (i.e., less than 10 m above ground) are likely to be over-estimated in the current design practice by as much as 20 percent which may result in wind load over-estimate of about 50% for low-rise buildings in typical rough terrain. The importance of thunderstorm wind profiles on determination of design wind speeds and building loads (particularly for buildings substantially taller than 10 m) is also discussed. Recommendations are given for possible improvements to the current design practice in the United States with respect to low-rise buildings in rough terrain and for the need to study the impact of thunderstorm gust profile shapes on extreme value wind speed estimates and building loads.

• Earthquakes and Structures
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• v.14 no.5
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• pp.477-492
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• 2018

Recently, the adaptive nonlinear static analysis method has been widely used in the field of performance based earthquake engineering. However, the proposed methods are almost deterministic and cannot directly consider the seismic record uncertainties. In the current study an innovative Stochastic Adaptive Pushover Analysis, called "SAPA", based on equivalent hysteresis system responses is developed to consider the earthquake record to record uncertainties. The methodology offers a direct stochastic analysis which estimates the seismic demands of the structure in a probabilistic manner. In this procedure by using a stochastic linearization technique in each step, the equivalent hysteresis system is analyzed and the probabilistic characteristics of the result are obtained by which the lateral force pattern is extracted and the actual structure is pushed. To compare the results, three different types of analysis have been considered; conventional pushover methods, incremental dynamic analysis, IDA, and the SAPA method. The result shows an admirable accuracy in predicting the structure responses.

• Ocean Systems Engineering
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• v.7 no.4
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• pp.345-369
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• 2017

The ocean environment offshore West Africa is considered to be mild. However, the generated swell from distant North and South Atlantic during austral winter and summer can reach high wave amplitudes with relatively low wave periods or low wave amplitudes with long wave periods, the later can be a crucial scenario to consider when the assessment of vessel resonance is of importance. Most offshore operations, which include offshore drilling, and installation in West Africa, are carried out from floating systems. The response of these systems and performance are governed by characteristics, such as amplitude and frequency of the wave and swell seas. It is therefore important to fully understand the sea conditions offshore Nigeria. This study covers the description of the swell sea offshore Nigeria using Bonga offshore wave measurements collected from the directional wave-rider (DWR), positioned at the Bonga site off the coast of Nigeria.