Drought Frequency Analysis Using Hidden Markov Chain Model and Bivariate Copula Function

Chun, Si-Young; Kim, Yong-Tak; Kwon, Hyun-Han

doi:10.3741/jkwra.2015.48.12.969

Cited by 6 publications

(3 citation statements)

References 16 publications

(9 reference statements)

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“…is the highest (97.55%), which indicates that these four industries are in a highly dependent state. As shown in Table 4, the mixed Copula model based on HMM proposed in this study has the largest logarithmic likelihood function and the lowest AIC and BIC values, which indicates that the model effect of nesting mixed Copula functions into HMM is optimal, which is consistent with the research results of Chun et al (2015) [29].…”

Section: Empirical Study Of Portfolio Risk Measurement Based On Hmm-m...supporting

confidence: 83%

Cognitive Computational Model Using Machine Learning Algorithm in Artificial Intelligence Environment

Liu

Khadidos

Abdulrazzqa

2021

Applied Mathematics and Nonlinear Sciences

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In order to accurately describe the risk dependence structure and correlation between financial variables, carry out scientific financial risk assessment, and provide the basis for accurate financial decision-making, first the basic theory of Copula function is established and the mixed Copula model is constructed. Then the hybrid Copula model is nested in a hidden Markov model (HMM), the risk dependences among banking, insurance, securities and trust industries are analysed, and the Copula–Garch model is constructed for empirical analysis of investment portfolio. Finally, the deep learning Markov model is adopted to predict the financial index. The results show that the mixed Copula model based on HMM is more effective than the single Copula and the mixed Copula models. The empirical structure shows that among the four major financial industries in China, the banking and insurance industries have strong interdependence and high probability of risk contagion. The investment failure rate under 95%, 97.5% and 99% confidence intervals calculated by Copula–Garch model are 4.53%, 2.17% and 1.08%, respectively. Moreover, the errors of deep learning Markov model in stock price prediction of Shanghai Pudong Development Bank (sh600000), Guizhou Moutai (sh600519) and China Ping An Insurance (sh601318) are 2.56%, 2.98% and 3.56% respectively, which indicates that the four major financial industries in China have strong interdependence and risk contagion, so that the macro or systemic risks may arise, and the deep-learning Markov model can be adopted to predict the stock prices.

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Section: Empirical Study Of Portfolio Risk Measurement Based On Hmm-m...supporting

confidence: 83%

Cognitive Computational Model Using Machine Learning Algorithm in Artificial Intelligence Environment

Liu

Khadidos

Abdulrazzqa

2021

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

“…After analysing the probability of the copula model based on the HMM in these two states, the probability of state 2 is the highest (97.55%), which indicates that these four industries are in a highly dependent state. As shown in Table 4, the mixed copula model based on the HMM proposed in this study has the largest logarithmic likelihood function and the lowest AIC and BIC values, which indicates that the model effect of nesting mixed copula functions into the HMM is optimal, which is consistent with the research results of Chun et al (2015) [29]. Subsequently, the dynamic transition diagrams of state 1 and state 2 of the sample from 2015 to 2019 are obtained, as shown in Figure 9A.…”

Section: Empirical Study Of Portfolio Risk Measurement Based On Hmm-m...supporting

confidence: 81%

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

Liang-xiong

Muwafak

2021

Applied Mathematics and Nonlinear Sciences

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In order to accurately describe the risk dependence structure and correlation between financial variables, a scientific financial risk assessment was carried out, and the basis for accurate financial decision-making was provided; the basic theory of copula function is established first, and the mixed copula model is constructed; then, the hybrid copula model is nested in a hidden Markov model (HMM); the risk dependences among banking, insurance, securities and trust industries are analysed, and the Copula-Garch model is constructed for empirical analysis of investment portfolio; finally, the deep learning Markov model is adopted to predict the financial index. The results show that the mixed copula model based on the HMM is more effective than the single copula model and the mixed copula model. The empirical structure shows that among the four major financial industries in China, banking and insurance industries have strong interdependence and a high probability of risk contagion. The investment failure rate under 95%, 97.5% and 99% confidence intervals calculated by the Copula-Garch model is 4.53%, 2.17% and 1.08%, respectively. Moreover, the errors of the deep learning Markov model in stock price prediction of Shanghai Pudong Development Bank (sh600000), Guizhou Moutai (sh600519) and China Ping An Insurance (sh601318) are 2.56%, 2.98% and 3.56%, respectively, which indicates that the four major financial industries in China have strong interdependence and risk contagion so that the macro or systemic risks may arise, and the deep learning Markov model can be adopted to predict the stock prices.

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“…Chen et al [25] performed multivariate frequency analysis by applying the minimum value of the drought index and the time of drought to the copula function as well as the drought severity and duration, and Halwatura et al [26] used SDF curves to investigate the applicability of rapid drought situation determination and probability drought mapping. Chun et al [27] analyzed the frequency of drought using the hidden Markov chain model and the bivariate (drought severity and duration) copula function, and Kim et al [28] performed drought frequency analysis using a trivariate copula function including cumulative drought severity in addition to the average drought severity and duration. In addition, since the three-dimensional shape of the bivariate joint distribution using the copula function decreases practical intuition, Yu et al [29] calculated the conditional probability distribution function for each duration from the copula function and proposed the concept of threshold drought severity for non-excess probability.…”

Section: Introductionmentioning

confidence: 99%

Two Ways to Quantify Korean Drought Frequency: Partial Duration Series and Bivariate Exponential Distribution, and Application to Climate Change

et al. 2020

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Studies using drought index to examine return levels of drought can be classified into two approaches: univariate frequency analysis using annual series extracted from drought index time series and multivariate frequency analysis that simultaneously reflects various characteristics of drought. In the case of drought analysis, it is important to properly consider the duration, so, in this study, univariate frequency analysis is performed using the partial duration series. In addition, a bivariate frequency analysis is performed using a relatively simple bivariate exponential distribution to give a more realistic return level to major drought events in the past while reflecting the correlation between drought severities and durations. The drought severity–duration–frequency curves using each of the two frequency analyses are derived, and these curves are used to examine how the drought phenomenon currently in progress is evolving. From this, the advantages and disadvantages of the two approaches, as well as the points to be aware of in application, are discussed. Finally, using the two approaches to the proposed drought frequency analysis, the behavior of Korea’s future extreme droughts is investigated under the conditions of various future climate change scenarios.

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Drought Frequency Analysis Using Hidden Markov Chain Model and Bivariate Copula Function

Cited by 6 publications

References 16 publications

Cognitive Computational Model Using Machine Learning Algorithm in Artificial Intelligence Environment

Cognitive Computational Model Using Machine Learning Algorithm in Artificial Intelligence Environment

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

Two Ways to Quantify Korean Drought Frequency: Partial Duration Series and Bivariate Exponential Distribution, and Application to Climate Change

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