The past decade has witnessed the rapid growing of the world palladium market. Thus, it is even more important to develop effective quantitative tools for risk management of palladium assets at this moment. In this paper, we investigate five different types of widely-used statistical distributions and employ the industry standard risk measurement, Value at Risk, for risk management of daily palladium spot returns. We first apply four different criteria to compare the goodness of fit of the five distributions, and then calculate the VaRs based on the parameters estimated from the first step. Our results indicate the Skewed t distribution has the best in-sample fitting and generate VaR values closest to the nonparametric historical VaR values.
{"title":"Fat Tails, Value at Risk, and the Daily Palladium Returns","authors":"Jianhua Ding, T. Guo, Bin Guo","doi":"10.2139/ssrn.3019733","DOIUrl":"https://doi.org/10.2139/ssrn.3019733","url":null,"abstract":"The past decade has witnessed the rapid growing of the world palladium market. Thus, it is even more important to develop effective quantitative tools for risk management of palladium assets at this moment. In this paper, we investigate five different types of widely-used statistical distributions and employ the industry standard risk measurement, Value at Risk, for risk management of daily palladium spot returns. We first apply four different criteria to compare the goodness of fit of the five distributions, and then calculate the VaRs based on the parameters estimated from the first step. Our results indicate the Skewed t distribution has the best in-sample fitting and generate VaR values closest to the nonparametric historical VaR values.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128383216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modified value-at-risk (mVaR) and modified expected shortfall (mES) are risk estimators that can be calculated without modeling the distribution of asset returns. These modified estimators use skewness and kurtosis corrections to normal distribution parametric VaR and ES formulas to obtain more accurate risk measurement for non-normal return distributions. Use of skewness and kurtosis corrections can result in reduced bias, but these also lead to inflated mVaR and mES estimator standard errors. We compare modified estimators with their respective parametric counterparts in three ways. First, we assess the magnitude of standard error inflation by deriving formulas for the large-sample standard errors of mVaR and mES using the multivariate delta method. Monte Carlo simulation is then used to determine sample sizes and tail probabilities for which our asymptotic variance formula can be reliably used to compute finite-sample standard errors. Second, to evaluate the large-sample bias, we derive formulas for the asymptotic bias of modified estimators for t-distributions. Third, we analyze the finite-sample performance of the modified estimators for normal and t -distributions using their root-mean-squared-error efficiency relative to the parametric VaR and ES maximum likelihood estimators using Monte Carlo simulation. Our results show that the modified estimators are inefficient for both normal and t-distributions: the more so for t-distributions.
{"title":"Inefficiency and Bias of Modified Value-at-Risk and Expected Shortfall","authors":"Doug Martin, Rohit Arora","doi":"10.21314/JOR.2017.365","DOIUrl":"https://doi.org/10.21314/JOR.2017.365","url":null,"abstract":"Modified value-at-risk (mVaR) and modified expected shortfall (mES) are risk estimators that can be calculated without modeling the distribution of asset returns. These modified estimators use skewness and kurtosis corrections to normal distribution parametric VaR and ES formulas to obtain more accurate risk measurement for non-normal return distributions. Use of skewness and kurtosis corrections can result in reduced bias, but these also lead to inflated mVaR and mES estimator standard errors. We compare modified estimators with their respective parametric counterparts in three ways. First, we assess the magnitude of standard error inflation by deriving formulas for the large-sample standard errors of mVaR and mES using the multivariate delta method. Monte Carlo simulation is then used to determine sample sizes and tail probabilities for which our asymptotic variance formula can be reliably used to compute finite-sample standard errors. Second, to evaluate the large-sample bias, we derive formulas for the asymptotic bias of modified estimators for t-distributions. Third, we analyze the finite-sample performance of the modified estimators for normal and t -distributions using their root-mean-squared-error efficiency relative to the parametric VaR and ES maximum likelihood estimators using Monte Carlo simulation. Our results show that the modified estimators are inefficient for both normal and t-distributions: the more so for t-distributions.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124868420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores historical return and risk properties of equity-hedged options across the S&P 500 option surface. We evaluate returns by estimating alpha to the S&P 500 index, and we quantify risk using three metrics: return volatility, losses under stress tests, and conditional value at risk. We show that analyzing option risk-adjusted alphas using different risk metrics leads to significantly different conclusions. We find that the most compensated options to sell on the S&P 500 surface per unit of stress-test loss are front-month options with strikes near-the-money and moderately below the index level. We apply these results to evaluate return expectations for short volatility strategies, potential added return from option selection, and implications for variance swaps.
{"title":"Which Index Options Should You Sell?","authors":"R. Israelov, Harsha Tummala","doi":"10.2139/SSRN.2990542","DOIUrl":"https://doi.org/10.2139/SSRN.2990542","url":null,"abstract":"This paper explores historical return and risk properties of equity-hedged options across the S&P 500 option surface. We evaluate returns by estimating alpha to the S&P 500 index, and we quantify risk using three metrics: return volatility, losses under stress tests, and conditional value at risk. We show that analyzing option risk-adjusted alphas using different risk metrics leads to significantly different conclusions. We find that the most compensated options to sell on the S&P 500 surface per unit of stress-test loss are front-month options with strikes near-the-money and moderately below the index level. We apply these results to evaluate return expectations for short volatility strategies, potential added return from option selection, and implications for variance swaps.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129133705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we propose a Value at Risk (VaR) and Expected Shortfall (ES) with normal and t-student distribution to estimate the daily market risk in the Colombian futures market Exchange rate with US dollar, the current regulation by the (CRCC) Colombian Central Counterparty Clearing House establish a constant 7% of guarantee to mitigate the counterparty risk, we found in the backtestings that the current method does not capture the current volatility and the leptokurtosis of the gain/loss distribution which conducts in a overestimation of risk, maybe that moves away the market from efficiency according to classical economy statements, we suggest that this is one reason for the lack of development of the Colombian Exchange future market.
{"title":"Value at Risk and Expected Shortfall Performance Versus Currents Regulation Applied to Colombian Exchange Rate Futures Market","authors":"Daniel Velásquez, Andrés Mora-Valencia","doi":"10.2139/ssrn.3258609","DOIUrl":"https://doi.org/10.2139/ssrn.3258609","url":null,"abstract":"In this paper we propose a Value at Risk (VaR) and Expected Shortfall (ES) with normal and t-student distribution to estimate the daily market risk in the Colombian futures market Exchange rate with US dollar, the current regulation by the (CRCC) Colombian Central Counterparty Clearing House establish a constant 7% of guarantee to mitigate the counterparty risk, we found in the backtestings that the current method does not capture the current volatility and the leptokurtosis of the gain/loss distribution which conducts in a overestimation of risk, maybe that moves away the market from efficiency according to classical economy statements, we suggest that this is one reason for the lack of development of the Colombian Exchange future market.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129659838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the intra-horizon value at risk (iVaR) in a general jump diffusion setup and propose a new model of asset returns called displaced mixed-exponential model, which can arbitrarily closely approximate finite-activity jump-diffusions and completely monotone Levy processes. We derive analytical results for the iVaR and disentangle the risk contribution of jumps from diffusion. Estimating the iVaR for several popular jump models using on S&P 100 option data, we find that option-implied estimates are much more responsive to market changes relative to their historical counterparts. Moreover, disentangling jumps from diffusion, jump account for about 90 percent of iVaR on average.
{"title":"Option-Implied Intra-Horizon Value-at-Risk","authors":"Markus Leippold, N. Vasiljević","doi":"10.2139/ssrn.2804702","DOIUrl":"https://doi.org/10.2139/ssrn.2804702","url":null,"abstract":"We study the intra-horizon value at risk (iVaR) in a general jump diffusion setup and propose a new model of asset returns called displaced mixed-exponential model, which can arbitrarily closely approximate finite-activity jump-diffusions and completely monotone Levy processes. We derive analytical results for the iVaR and disentangle the risk contribution of jumps from diffusion. Estimating the iVaR for several popular jump models using on S&P 100 option data, we find that option-implied estimates are much more responsive to market changes relative to their historical counterparts. Moreover, disentangling jumps from diffusion, jump account for about 90 percent of iVaR on average.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117004132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main objective of this paper is to model the losses caused by frost events and use it to price frost insurances. Since the data on frost events are either unavailable or rarely available, we have chosen to obtain a model for frost losses based on temperature by using some fundamental agricultural engineering findings on frost damages. The main challenges in modeling frost loss variables are first, the non-linearity of the frost losses with respect to the temperature and second, the fruit resistance to the first few hours of low temperature. We address both issues when introducing our frost loss variable. Then after finding the loss model, we use it to price frost insurances for a general family of insurance contracts that do not generate any risk of moral hazard. In particular, we will find the premiums of stop-loss policies for losses to citrus fruits using Value at Risk, Conditional Value at Risk and Wang's premium based on temperature data from San Joaquin Drainage County in California.
{"title":"Modeling Frost Losses: Application to Pricing Frost Insurances","authors":"H. Assa, Meng Wang, A. Pantelous","doi":"10.2139/ssrn.2965094","DOIUrl":"https://doi.org/10.2139/ssrn.2965094","url":null,"abstract":"The main objective of this paper is to model the losses caused by frost events and use it to price frost insurances. Since the data on frost events are either unavailable or rarely available, we have chosen to obtain a model for frost losses based on temperature by using some fundamental agricultural engineering findings on frost damages. The main challenges in modeling frost loss variables are first, the non-linearity of the frost losses with respect to the temperature and second, the fruit resistance to the first few hours of low temperature. We address both issues when introducing our frost loss variable. Then after finding the loss model, we use it to price frost insurances for a general family of insurance contracts that do not generate any risk of moral hazard. In particular, we will find the premiums of stop-loss policies for losses to citrus fruits using Value at Risk, Conditional Value at Risk and Wang's premium based on temperature data from San Joaquin Drainage County in California.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"529 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123451182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-31DOI: 10.5709/CE.1897-9254.230
M. Gorji, R. Sajjad
Returns in financial assets display consistent excess kurtosis and skewness, implying the presence of large fluctuations not forecasted by Gaussian models. This paper applies a resampling method based on the bootstrap and a bias-correction step to improve Value-at-Risk (VaR) forecasting ability of the n-EGARCH (normal EGARCH) model and correct the VaR for both long and short positions. Our aim is to utilize the advantages of this model, but still use the bootstrap resampling method to accurate for the tendency of the model tomiscalculate the VaR. Empirical results indicate that the bias-correction method can improve the n-GARCH and n-EGARCH VaR forecasts so much that the acquired VaR predictions are different from the proposed probability. Additionally, allowing asymmetry in the conditional variance using the EGARCH model with normal distribution instead of GARCH improves the performance of the bias-correction method in forecasting the VaR for almost all considered indices. Moreover, the bias-corrected n-EGARCH model performs better than the simple t-EGARCH model. Thus, it seems that this model can take account of both the asymmetry in the conditional variance and leptokurtosis in returns distribution. However, we find that the superiority of the bias-corrected n-EGARCH model over the t-EGARCH model is not completely confirmed for short positions based on the censored likelihood scoring rule.
{"title":"Improving Value-at-Risk Estimation from the Normal Egarch Model","authors":"M. Gorji, R. Sajjad","doi":"10.5709/CE.1897-9254.230","DOIUrl":"https://doi.org/10.5709/CE.1897-9254.230","url":null,"abstract":"Returns in financial assets display consistent excess kurtosis and skewness, implying the presence of large fluctuations not forecasted by Gaussian models. This paper applies a resampling method based on the bootstrap and a bias-correction step to improve Value-at-Risk (VaR) forecasting ability of the n-EGARCH (normal EGARCH) model and correct the VaR for both long and short positions. Our aim is to utilize the advantages of this model, but still use the bootstrap resampling method to accurate for the tendency of the model tomiscalculate the VaR. Empirical results indicate that the bias-correction method can improve the n-GARCH and n-EGARCH VaR forecasts so much that the acquired VaR predictions are different from the proposed probability. Additionally, allowing asymmetry in the conditional variance using the EGARCH model with normal distribution instead of GARCH improves the performance of the bias-correction method in forecasting the VaR for almost all considered indices. Moreover, the bias-corrected n-EGARCH model performs better than the simple t-EGARCH model. Thus, it seems that this model can take account of both the asymmetry in the conditional variance and leptokurtosis in returns distribution. However, we find that the superiority of the bias-corrected n-EGARCH model over the t-EGARCH model is not completely confirmed for short positions based on the censored likelihood scoring rule.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124073926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study the worst-case scenarios of a general class of risk measures, the Range Value-at-Risk (RVaR), in single and aggregate risk models with given mean and variance, as well as symmetry and/or unimodality of each risk. For different types of partial information settings, sharp bounds for RVaR are obtained for single and aggregate risk models, together with the corresponding worst-case scenarios of marginal risks and the corresponding copula functions (dependence structure) among them. Different from the existing literature, the sharp bounds under different partial information settings in this paper are obtained via a unified method combining convex order and the recently developed notion of joint mixability. As particular cases, bounds for Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) are derived directly. Numerical examples are also provided to illustrate our results.
{"title":"Worst-Case Range Value-at-Risk with Partial Information","authors":"Lujun Li, Hui Shao, Ruodu Wang, Jingping Yang","doi":"10.2139/ssrn.2920334","DOIUrl":"https://doi.org/10.2139/ssrn.2920334","url":null,"abstract":"In this paper, we study the worst-case scenarios of a general class of risk measures, the Range Value-at-Risk (RVaR), in single and aggregate risk models with given mean and variance, as well as symmetry and/or unimodality of each risk. For different types of partial information settings, sharp bounds for RVaR are obtained for single and aggregate risk models, together with the corresponding worst-case scenarios of marginal risks and the corresponding copula functions (dependence structure) among them. Different from the existing literature, the sharp bounds under different partial information settings in this paper are obtained via a unified method combining convex order and the recently developed notion of joint mixability. As particular cases, bounds for Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) are derived directly. Numerical examples are also provided to illustrate our results.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133167572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study Pareto optimality of reinsurance arrangements under general model settings. We give the necessary and sufficient conditions for a reinsurance contract to be Pareto-optimal and characterize all Pareto-optimal reinsurance contracts under more general model assumptions. We also obtain the sufficient conditions that guarantee the existence of the Pareto-optimal reinsurance contracts. When the losses of an insurer and a reinsurer are both measured by the Tail-Value-at-Risk (TVaR) risk measures, we obtain the explicit forms of the Pareto-optimal reinsurance contracts under the expected value premium principle. For the purpose of practice, we use numerical examples to show how to determine the mutually acceptable Pareto-optimal reinsurance contracts among the available Pareto-optimal reinsurance contracts such that both the insurer’s aim and the reinsurer’s goal can be met under the mutually acceptable Pareto-optimal reinsurance contracts.
{"title":"Pareto-Optimal Reinsurance Arrangements Under General Model Settings","authors":"Jun Cai, Haiyan Liu, Ruodu Wang","doi":"10.2139/ssrn.2887632","DOIUrl":"https://doi.org/10.2139/ssrn.2887632","url":null,"abstract":"In this paper, we study Pareto optimality of reinsurance arrangements under general model settings. We give the necessary and sufficient conditions for a reinsurance contract to be Pareto-optimal and characterize all Pareto-optimal reinsurance contracts under more general model assumptions. We also obtain the sufficient conditions that guarantee the existence of the Pareto-optimal reinsurance contracts. When the losses of an insurer and a reinsurer are both measured by the Tail-Value-at-Risk (TVaR) risk measures, we obtain the explicit forms of the Pareto-optimal reinsurance contracts under the expected value premium principle. For the purpose of practice, we use numerical examples to show how to determine the mutually acceptable Pareto-optimal reinsurance contracts among the available Pareto-optimal reinsurance contracts such that both the insurer’s aim and the reinsurer’s goal can be met under the mutually acceptable Pareto-optimal reinsurance contracts.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114303276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study assesses the ‘safehavenness’ of a number of currencies with a view to providing a better understanding of how capital flows tend to react to sharp increases in global risk aversion during periods of financial crisis. It focuses on how currencies are perceived by dollar-based international investors or, more specifically, whether they are seen as safe-haven or risky currencies. To assess the ‘safehavenness’ of a currency, we use a measure of risk reversal, which is the price difference between a call and put option of a currency. This measures how disproportionately market participants are willing to pay to hedge against appreciation or depreciation of the currency. The relationship between the risk reversal of a currency and global risk aversion is estimated by means of both parametric and non-parametric regressions which allow us to capture the relationship in times of extreme adversity, i.e., tail risk. Our empirical results suggest that the Japanese yen and, to a lesser extent, the Hong Kong dollar are the only safe haven currencies under stressful conditions out of 34 currencies vis-a-vis the US dollar.
{"title":"Gauging the Safehavenness of Currencies","authors":"A. Wong, T. Fong","doi":"10.2139/ssrn.2330680","DOIUrl":"https://doi.org/10.2139/ssrn.2330680","url":null,"abstract":"This study assesses the ‘safehavenness’ of a number of currencies with a view to providing a better understanding of how capital flows tend to react to sharp increases in global risk aversion during periods of financial crisis. It focuses on how currencies are perceived by dollar-based international investors or, more specifically, whether they are seen as safe-haven or risky currencies. To assess the ‘safehavenness’ of a currency, we use a measure of risk reversal, which is the price difference between a call and put option of a currency. This measures how disproportionately market participants are willing to pay to hedge against appreciation or depreciation of the currency. The relationship between the risk reversal of a currency and global risk aversion is estimated by means of both parametric and non-parametric regressions which allow us to capture the relationship in times of extreme adversity, i.e., tail risk. Our empirical results suggest that the Japanese yen and, to a lesser extent, the Hong Kong dollar are the only safe haven currencies under stressful conditions out of 34 currencies vis-a-vis the US dollar.","PeriodicalId":203996,"journal":{"name":"ERN: Value-at-Risk (Topic)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126276727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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