• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.323-336
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• 2024

The accurate forecasting of insurance claims is a critical component for insurers' risk management decisions. Hierarchical Bayesian parametric (BP) models can be used for health insurance claims forecasting, but they are unsatisfactory to describe the claims distribution. Therefore, Bayesian nonparametric (BNP) models can be a more suitable alternative to deal with the complex characteristics of the health insurance claims distribution, including heavy tails, skewness, and multimodality. In this study, we apply both a BP model and a BNP model to predict group health claims using simulated and real-world data for a private life insurer in Indonesia. The findings show that the BNP model outperforms the BP model in terms of claims prediction accuracy. Furthermore, our analysis highlights the flexibility and robustness of BNP models in handling diverse data structures in health insurance claims.

• Research in Community and Public Health Nursing
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• v.29 no.3
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• pp.279-289
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• 2018

Purpose: This study has been conducted to identify factors that influence the initiation of treatment after the diagnosis of Korean patients with HIV. Methods: A cross-sectional study design was used, and 290 patients with HIV from outpatient departments of 7 hospitals participated. Self-report questionnaires included items on the days from the primary diagnosis to the initiation of treatment, and the patients' demographic and disease related characteristics. Negative binomial regression model (NBR) was utilized to determine risk factors influencing the initiation of treatment after the diagnosis of the patients with HIV. Results: The skewness of days was 6.62, and the degree of asymmetry of distribution was severe. In NBR, patients who were in their 40s and 50s, female, unmarried and living with their family, jobless, in a middle or high level of economic status, and diagnosed before 2014 showed a higher risk of delayed treatment than patients who were younger, male, married and living with family, in a low level of economic status, and diagnosed in 2014 or afterwards. Conclusion: The findings suggest the necessity of intervention to promote HIV patients' early entry into treatment based on the participants' characteristics.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.947-978
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• 2007

In this paper, we investigated a Value-at-Risk approach to the volatility of two crude oil markets (Brent and Dubai). We also assessed the performance of various VaR models (RiskMetrics, GARCH, IGARCH and FIGARCH models) with the normal and skewed Student-t distribution innovations. The FIGARCH model outperforms the GARCH and IGARCH models in capturing the long memory property in the volatility of crude oil markets returns. This implies that the long memory property is prevalent in the volatility of crude oil returns. In addition, from the results of VaR analysis, the FIGARCH model with the skewed Student-t distribution innovation predicts critical loss more accurately than other models with the normal distribution innovation for both long and short positions. This finding indicates that the skewed Student-t distribution innovation is better for modeling the skewness and excess kurtosis in the distribution of crude oil returns. Overall, these findings might improve the measurement of the dynamics of crude oil prices and provide an accurate estimation of VaR for buyers and sellers in crude oil markets.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.297-309
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• 2021

In this study, to capture the skewness and kurtosis detected in both conditional and unconditional return distributions of the stock markets of Kazakhstan and Russia, two versions of normal mixture GARCH models are employed. The data set consists of daily observations of the Kazakhstan and Russia stock prices, and world crude oil price, covering the period from 1 June 2006 through 1 March 2021. From the empirical results, incorporating the long memory effect on the returns not only provides better descriptions of dynamic behaviors of the stock market prices but also plays a significant role in improving a better understanding of the return dynamics. In addition, normal mixture models for time-varying volatility provide a better fit to the conditional densities than the usual GARCH specifications and has an important advantage that the conditional higher moments are time-varying. This implies that the volatility skews implied by normal mixture models are more likely to exhibit the features of risk and the direction of the information flow is regime-dependent. The findings of this study contain useful information for diverse purposes of cross-border stock market players such as asset allocation, portfolio management, risk management, and market regulations.

• The Journal of Economics, Marketing and Management
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• v.12 no.4
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• pp.27-36
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• 2024

Purpose: The paper analyzes for detecting unexpected shocks such as global financial crisis and COVID-19 pandemic, and contagion between countries by capturing in the mean-shift, variance-covariance-shift, and skewness-coskewness-shift parameters of interest rates. Research design, data and methodology: A flexible multivariate model of interest rates is provided by allowing for regime switching and a joint skewed normal distribution. The model is applying to the structural breaks of crisis and contagion between the US and the selected global bond markets during the global financial crisis and COVID-19 pandemic, respectively. Inspection of the moment statistics weakly suggests a flight to safety to the US during the global financial crisis and to Canada during the COVID-19 pandemic. Results: The results indicate that risk averse investors had a higher risk appetite for the US and Canada assets during the crisis regimes, compared to their counterparts. Conclusions: The results show that coskewness contagion dominates correlation contagion, and coskewness contagion is significant for the Korea and Japan-US pairs for the global financial crisis and the Euro-US pair for the COVID-19 pandemic. All channels of structural breaks of crisis and contagion are significant when considered jointly, reinforcing the need to consider contagion and structural breaks during crises in a multivariate setting.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1263-1274
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• 2013

VaR (value at risk), which represents the expectation of the worst loss that may occur over a period of time within a given level of confidence, is currently used by various financial institutions for the purpose of risk management. In the majority of previous studies, the probability of return has been modeled with normal distribution. Recently Chen et al. (2010) measured VaR with asymmetric Laplacian distribution. However, it is difficult to estimate the mode, the skewness, and the degree of variance that determine the shape of an asymmetric Laplacian distribution with limited data in the real-world market. In this paper, we show that the VaR estimated with (symmetric) Laplacian distribution model provides more accuracy than those with normal distribution model or asymmetric Laplacian distribution model with real world stock market data and with various statistical measures.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.55-64
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• 2024

In this study, we consider the sample size determination problem for clustered count data with many zeros. In general, zero-inflated Poisson and binomial models are commonly used for zero-inflated data; however, in real data the assumptions that should be satisfied when using each model might be violated. We calculate the required sample size based on a discrete Weibull regression model that can handle both underdispersed and overdispersed data types. We use the Monte Carlo simulation to compute the required sample size. With our proposed method, a unified model with a low failure risk can be used to cope with the dispersed data type and handle data with many zeros, which appear in groups or clusters sharing a common variation source. A simulation study shows that our proposed method provides accurate results, revealing that the sample size is affected by the distribution skewness, covariance structure of covariates, and amount of zeros. We apply our method to the pancreas disorder length of the stay data collected from Western Australia.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.446-452
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• 2018

A traffic route is an area associated with high risk for accidents due to the flow of heavy traffic. Despite this concern, most studies related to traffic focus solely on traffic distribution. Therefore, there is a need for studies investigating the characteristics of ships' routes and traffic patterns. In this study, an investigation was carried out to analyze the traffic distribution and pattern in 3 major traffic routes for 3 days. For the purpose of the study, based on the prevailing traffic conditions, the route was divided into 10 gate lines. The ships passing through the lines were also classified into either small, medium and large. ND-K-S (normal distribution, kurtosis, and skewness) test was carried out for the traffic distribution at each gate line based on the information analyzed on each traffic route. The analysis of the results obtained from the ND test showed that large vessels have normal distribution, medium sized vessels have satisfied normal distribution in one-way route only while small sized vessels do not have normal distribution. According to the result obtained from the K-S test, normal traffic pattern shows a significant difference between two-way route and one-way route. Results obtained from the K test result shows that in the case of one-way route, vessels have a traffic pattern using a wide range on traffic route. Further analysis shows that vessels concentrate on one side of route in case of two-way route. Results obtained from the S test show that, in case of one-way route, vessels have a normal traffic pattern according to center line. However, analysis pf the results shows that vessels are shifted to the right side of route in case of two-way route. Despite these findings, it should be noted that this study was carried out in only 3 ports, therefore there is need for investigation to be carried out in various routes and conditions in future studies.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.320-332
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• 2021

This paper investigates the influence of stock-level left-tail risk, which is defined using Value-at-Risk(VaR) estimates of past one-year daily stock returns, in the expected stock returns in the Korean stock market. Our results are summarized as follows: First, monthly-constructed zero-cost portfolios that buy (shortsell) the highest (lowest) left-tail risk decile in the previous month exhibit an average monthly return (called left-tail risk premium) of -2.29%. Second, Fama-MacBeth cross-sectional regressions suggest that left-tail risk in the previous month shows significant and negative explanatory power over return in this month, after controlling for various firm characteristics such as firm size, B/M, market beta, liquidity, maximum daily return, idiosyncratic volatility, and skewness. Third, the stocks with larger recent month loss have lower returns in the next month. Fourth, the magnitude of left-tail risk premium is negatively related with lagged market-level volatility. These results support the hypothesis from a perspective of behavioral finance that the overpricing of stocks with left-tail risk is attributed to the investors' underreaction to it.

• International Area Studies Review
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• v.12 no.2
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• pp.3-26
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• 2008

This article investigates the usefulness of the skewed Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Australian financial series; the ASX200 stock index and AUD/USD exchange rate. For this purpose we assess the performance of FIGARCH and FIAPARCH Value-at-Risk (VaR) models based on the normal, Student-t, and skewed Student-t distribution innovations. Our results support the argument that the skewed Student-t distribution models produce more accurate VaR estimates of Australian financial markets than the normal and Student-t distribution models. Thus, consideration of skewness and excess kurtosis in asset return distributions provides appropriate criteria for model selection in the context of long memory volatility models in Australian stock and foreign exchange markets.