Jean‐François Bégin, Diego Amaya, Geneviève Gauthier, Marie-Ève Malette
We adopt a flexible filtering procedure to extract information from high-frequency data. Specifically, we provide a parsimonious framework to integrate realized measures from high frequency index and derivative prices. In a simulation study, we document the incremental information offered by realized measures and show that even though high-frequency index prices help identify spot variance and jump price dynamics, it is the addition of high-frequency option prices that enables variance jumps to be identified. A series of empirical studies based on the S&P 500 index and options show that estimation precision improves with the addition of information from intraday option prices.
{"title":"Supplementary Material of On the Estimation of Jump-Diffusion Models Using Intraday Data: A Filtering-Based Approach","authors":"Jean‐François Bégin, Diego Amaya, Geneviève Gauthier, Marie-Ève Malette","doi":"10.2139/ssrn.3737708","DOIUrl":"https://doi.org/10.2139/ssrn.3737708","url":null,"abstract":"We adopt a flexible filtering procedure to extract information from high-frequency data. Specifically, we provide a parsimonious framework to integrate realized measures from high frequency index and derivative prices. In a simulation study, we document the incremental information offered by realized measures and show that even though high-frequency index prices help identify spot variance and jump price dynamics, it is the addition of high-frequency option prices that enables variance jumps to be identified. A series of empirical studies based on the S&P 500 index and options show that estimation precision improves with the addition of information from intraday option prices.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129389227","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}
Uncertainty in banking regulation may impose widespread economic costs by increasing financial constraints on credit availability. Four years of Dodd Frank uncertainty over undecided risk weightings increased regulatory uncertainty for smaller banks, restricting "vanilla" interest rate hedging activities. This paper uses newly reported mortgage banking data as an identification strategy and finds that when costs of uncertainty are removed, small banks hedge 97-120% more interest rate risk while mortgage securitization income increases by 65.2% compared to large banks. These findings support the need for tailored regulations that considers the higher costs of regulatory uncertainty for smaller banks.
{"title":"Disproportionate Costs of Uncertainty: Small Bank Hedging and Dodd-Frank","authors":"R. Kim","doi":"10.2139/ssrn.3320224","DOIUrl":"https://doi.org/10.2139/ssrn.3320224","url":null,"abstract":"Uncertainty in banking regulation may impose widespread economic costs by increasing financial constraints on credit availability. Four years of Dodd Frank uncertainty over undecided risk weightings increased regulatory uncertainty for smaller banks, restricting \"vanilla\" interest rate hedging activities. This paper uses newly reported mortgage banking data as an identification strategy and finds that when costs of uncertainty are removed, small banks hedge 97-120% more interest rate risk while mortgage securitization income increases by 65.2% compared to large banks. These findings support the need for tailored regulations that considers the higher costs of regulatory uncertainty for smaller banks.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131442895","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 presents how to use Chebyshev Tensors to compute dynamic sensitivities of financial instruments within a Monte Carlo simulation. Dynamic sensitivities are then used to compute Dynamic Initial Margin as defined by ISDA (SIMM). The technique is benchmarked against the computation of dynamic sensitivities obtained by using pricing functions like the ones found in risk engines. We obtain high accuracy and computational gains for FX swaps and Spread Options.
{"title":"Dynamic Sensitivities and Initial Margin via Chebyshev Tensors","authors":"Mariano Zeron Medina Laris, I. Ruiz","doi":"10.2139/ssrn.3727479","DOIUrl":"https://doi.org/10.2139/ssrn.3727479","url":null,"abstract":"This paper presents how to use Chebyshev Tensors to compute dynamic sensitivities of financial instruments within a Monte Carlo simulation. Dynamic sensitivities are then used to compute Dynamic Initial Margin as defined by ISDA (SIMM). The technique is benchmarked against the computation of dynamic sensitivities obtained by using pricing functions like the ones found in risk engines. We obtain high accuracy and computational gains for FX swaps and Spread Options.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115389262","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 present the hybrid-exponential scheme for simulating stochastic Volterra equations. The scheme is based on an exact approximation of the kernel function near the origin and an approximation by a sum of exponentials across the rest of the domain. The first part is similar to the hybrid scheme introduced in and is needed to capture any singular behavior of the kernel. The second part follows the ideas of where rough volatility models are under consideration and results in a number of stochastic factors to be simulated, one for each exponential term, and all with linear complexity in time. Since the efficiency of our scheme relies heavily on ensuring a low number of factors, we include also a review of various methods for finding the exponential terms. We here discover the method of and show that many fewer terms are needed for the rough fractional kernel than previously established in. Lastly, we provide a proof of convergence and also numerically demonstrate the efficiency of the scheme by example on the rough Bergomi model from.
{"title":"Hybrid multifactor scheme for stochastic Volterra equations","authors":"Sigurd Emil Rømer","doi":"10.2139/ssrn.3706253","DOIUrl":"https://doi.org/10.2139/ssrn.3706253","url":null,"abstract":"We present the hybrid-exponential scheme for simulating stochastic Volterra equations. The scheme is based on an exact approximation of the kernel function near the origin and an approximation by a sum of exponentials across the rest of the domain. The first part is similar to the hybrid scheme introduced in and is needed to capture any singular behavior of the kernel. The second part follows the ideas of where rough volatility models are under consideration and results in a number of stochastic factors to be simulated, one for each exponential term, and all with linear complexity in time. Since the efficiency of our scheme relies heavily on ensuring a low number of factors, we include also a review of various methods for finding the exponential terms. We here discover the method of and show that many fewer terms are needed for the rough fractional kernel than previously established in. Lastly, we provide a proof of convergence and also numerically demonstrate the efficiency of the scheme by example on the rough Bergomi model from.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128544664","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 contains details for implementing credit spread variance pricing methodologies based on credit default swap (CDS) options. A model independent formula for expected volatility is available, based on the prices of vanilla CDS options (VCOs). However, VCOs are currently not traded, and their prices must be inferred from those of actively traded CDS options with exotic payoffs (ECOs). Plugging ECO prices directly into the index formula is not theoretically justified, and the economic significance in the context of variance pricing of the difference in options contract specifications must be examined empirically. The paper develops methodology for converting observed ECO prices into hypothetical VCO prices for the purpose of index calculation, and assesses the economic impact of using ECOs and VCOs on index values under realistic market conditions.
{"title":"Credit Volatility Indexes","authors":"A. Melé, Yoshiki Obayashi","doi":"10.2139/ssrn.3714671","DOIUrl":"https://doi.org/10.2139/ssrn.3714671","url":null,"abstract":"This paper contains details for implementing credit spread variance pricing methodologies based on credit default swap (CDS) options. A model independent formula for expected volatility is available, based on the prices of vanilla CDS options (VCOs). However, VCOs are currently not traded, and their prices must be inferred from those of actively traded CDS options with exotic payoffs (ECOs). Plugging ECO prices directly into the index formula is not theoretically justified, and the economic significance in the context of variance pricing of the difference in options contract specifications must be examined empirically. The paper develops methodology for converting observed ECO prices into hypothetical VCO prices for the purpose of index calculation, and assesses the economic impact of using ECOs and VCOs on index values under realistic market conditions.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129510635","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}
Prior literature recognizes that liquidity is essential in understanding the information content of option trades. In this paper, we model the duration and volume jointly, for the first time, as a natural measure of options’ trading intensity and we associate it with differential degrees of information present in option trades. We report a highly significant association between option trading intensity with contemporaneous and future underlying volatility and returns, which is distinct from the effects of option duration and option trading volume and the O/S ratio. Finally, we show that our trading intensity measure and the O/S ratio are complementary in capturing informed trading in the option market.
{"title":"Information and the Arrival Rate of Option Trading Volume","authors":"Mengyu Zhang, Thanos Verousis, I. Kalaitzoglou","doi":"10.2139/ssrn.3707991","DOIUrl":"https://doi.org/10.2139/ssrn.3707991","url":null,"abstract":"Prior literature recognizes that liquidity is essential in understanding the information content of option trades. In this paper, we model the duration and volume jointly, for the first time, as a natural measure of options’ trading intensity and we associate it with differential degrees of information present in option trades. We report a highly significant association between option trading intensity with contemporaneous and future underlying volatility and returns, which is distinct from the effects of option duration and option trading volume and the O/S ratio. Finally, we show that our trading intensity measure and the O/S ratio are complementary in capturing informed trading in the option market.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126883298","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}
Financial hedging of raw material prices and exchange rates has become an integral part of many manufacturers’ operating practices. Previous empirical research suggests that a desire to avoid financial distress and the affiliated curtailment in operations is one of the strongest hedge motivations. Taking the hedging motivation as given, we test two data-driven hedging policies to see how effective they are at mitigating financial distress in the car manufacturing industry. The first policy is the cost hedging policy, under which the carmaker hedges raw material and production input purchases. This policy appears to be widely in use. The car manufacturer needs to trade in aluminum, steel, zinc, and plastic to achieve the cost hedge. The second policy is a cash hedging policy under which the firm hedges its net cash flow. The firm solves a stochastic program with a min hedge cost objective and cash flow constraints to construct the cash hedge. Its solution suggests that the firm needs to trade S&P500, aluminum, and zinc to implement the hedge. Our results further reveal the relative importance of different market factors on the automaker’s hedging policy. The most critical drivers of hedging decisions appear to be demand shifts, especially demand elasticity shifts. The least important drivers are car design updates, which change the car’s raw material requirements. This finding sheds light on why cost hedging, which focuses on hedging raw materials, is less effective than the cash hedging technique, which hedges both costs and demand.
{"title":"Supporting Operations with Financial Hedging: Cash Hedging Versus Cost Hedging in an Automotive Industry","authors":"Panos Kouvelis, Danko Turcic","doi":"10.2139/ssrn.3705691","DOIUrl":"https://doi.org/10.2139/ssrn.3705691","url":null,"abstract":"Financial hedging of raw material prices and exchange rates has become an integral part of many manufacturers’ operating practices. Previous empirical research suggests that a desire to avoid financial distress and the affiliated curtailment in operations is one of the strongest hedge motivations. Taking the hedging motivation as given, we test two data-driven hedging policies to see how effective they are at mitigating financial distress in the car manufacturing industry. The first policy is the cost hedging policy, under which the carmaker hedges raw material and production input purchases. This policy appears to be widely in use. The car manufacturer needs to trade in aluminum, steel, zinc, and plastic to achieve the cost hedge. The second policy is a cash hedging policy under which the firm hedges its net cash flow. The firm solves a stochastic program with a min hedge cost objective and cash flow constraints to construct the cash hedge. Its solution suggests that the firm needs to trade S&P500, aluminum, and zinc to implement the hedge. Our results further reveal the relative importance of different market factors on the automaker’s hedging policy. The most critical drivers of hedging decisions appear to be demand shifts, especially demand elasticity shifts. The least important drivers are car design updates, which change the car’s raw material requirements. This finding sheds light on why cost hedging, which focuses on hedging raw materials, is less effective than the cash hedging technique, which hedges both costs and demand.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116858650","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 studies the factor structure of the cross-section of delta-hedged equity option returns. We find that a four-factor model explains the cross-section and time-series of equity option returns. Out of the four factors, three are characteristic based factors from the long-short option portfolios based on firm size, idiosyncratic volatility, and the difference between implied and historical volatilities. The fourth factor is the market volatility risk factor proxied by the delta-hedged option return of the the S&P 500 index.
{"title":"Common Factors in Equity Option Returns","authors":"A. Horenstein, Aurelio Vasquez, Xiao Xiao","doi":"10.2139/ssrn.3290363","DOIUrl":"https://doi.org/10.2139/ssrn.3290363","url":null,"abstract":"This paper studies the factor structure of the cross-section of delta-hedged equity option returns. We find that a four-factor model explains the cross-section and time-series of equity option returns. Out of the four factors, three are characteristic based factors from the long-short option portfolios based on firm size, idiosyncratic volatility, and the difference between implied and historical volatilities. The fourth factor is the market volatility risk factor proxied by the delta-hedged option return of the the S&P 500 index.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124673487","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 looks into recent issues involving close-out netting and collateralization for over-the-counter (OTC) derivatives transactions from the point of Japanese and international financial law.
In Japan, collateralization of OTC derivatives transactions is generally structured using the loan and set-off method, rather than as a security interest (pledge). The problem with this loan and set-off scheme is that the collateral provider is not entitled to restore the portion of the collateral that exceeds the amount of secured obligation when the collateral receiver becomes insolvent (Tokyo High Court judgement dated 27 October 2010). Japan might be able to address this problem by creating a new legislation on financial collateral in line with the EU Directive on Financial Collateral Arrangements. Another option could be the use of a trust scheme, enabling the asset receiver to use or dispose of the deposited asset while managing it separately from its own assets, including the ‘trusts for customer's deposits for securities transactions’ under the Financial Instruments and Exchange Act of Japan (FIEA) primarily designed for retail customers. However, due to various practical hurdles, parties to OTC derivatives transactions are often unwilling to use the trust scheme for collateral management (eg, because the amount of required collateral changes from time to time depending on the market value of the underlying assets). At present, the most effective way to minimize the counterparty risk of derivative transactions is centralized clearing at the clearing house. Most categories of OTC derivative transactions are eligible for such centralized clearing. For non-cleared OTC derivatives, a solution would be to accumulate collateral (margin) in just proportion at the start of the transaction and to frequently evaluate and adjust the amount of collateral (margin) required as the market value of the derivative transaction fluctuates. This method complicates the procedure but enables a very stable operation. Indeed, international regulations have moved in this direction. Japan also implemented the margin requirements for non-cleared OTC derivatives that required posting or collection of the amount equivalent to market value fluctuations as variable margin, and initial margins to be made by way of either loans, or deposits, for consumption.
In March 2016, the Financial Services Agency of Japan revised the Cabinet Office Ordinance and introduced the new margin requirements to prohibit securities firms and registered financial institutions from conducting certain non-cleared OTC derivative transactions without taking prescribed measures such as depositing margins. With regard to margin for non-centrally cleared derivative transactions, these requirements were agreed upon twice, in September 2013 and March 2015, by the Basel Committee on Banking Supervision (BCBS) and the International Organization of Securities Commissions (IOSCO). The final
{"title":"Issues Involving Close-out Netting and Collateral for OTC Derivatives Transactions – A Japanese and International Finance Law Perspective","authors":"Hiroyuki Watanabe","doi":"10.2139/ssrn.3687596","DOIUrl":"https://doi.org/10.2139/ssrn.3687596","url":null,"abstract":"This paper looks into recent issues involving close-out netting and collateralization for over-the-counter (OTC) derivatives transactions from the point of Japanese and international financial law. <br><br>In Japan, collateralization of OTC derivatives transactions is generally structured using the loan and set-off method, rather than as a security interest (pledge). The problem with this loan and set-off scheme is that the collateral provider is not entitled to restore the portion of the collateral that exceeds the amount of secured obligation when the collateral receiver becomes insolvent (Tokyo High Court judgement dated 27 October 2010). Japan might be able to address this problem by creating a new legislation on financial collateral in line with the EU Directive on Financial Collateral Arrangements. Another option could be the use of a trust scheme, enabling the asset receiver to use or dispose of the deposited asset while managing it separately from its own assets, including the ‘trusts for customer's deposits for securities transactions’ under the Financial Instruments and Exchange Act of Japan (FIEA) primarily designed for retail customers. However, due to various practical hurdles, parties to OTC derivatives transactions are often unwilling to use the trust scheme for collateral management (eg, because the amount of required collateral changes from time to time depending on the market value of the underlying assets). At present, the most effective way to minimize the counterparty risk of derivative transactions is centralized clearing at the clearing house. Most categories of OTC derivative transactions are eligible for such centralized clearing. <br>For non-cleared OTC derivatives, a solution would be to accumulate collateral (margin) in just proportion at the start of the transaction and to frequently evaluate and adjust the amount of collateral (margin) required as the market value of the derivative transaction fluctuates. This method complicates the procedure but enables a very stable operation. Indeed, international regulations have moved in this direction. Japan also implemented the margin requirements for non-cleared OTC derivatives that required posting or collection of the amount equivalent to market value fluctuations as variable margin, and initial margins to be made by way of either loans, or deposits, for consumption.<br><br>In March 2016, the Financial Services Agency of Japan revised the Cabinet Office Ordinance and introduced the new margin requirements to prohibit securities firms and registered financial institutions from conducting certain non-cleared OTC derivative transactions without taking prescribed measures such as depositing margins. With regard to margin for non-centrally cleared derivative transactions, these requirements were agreed upon twice, in September 2013 and March 2015, by the Basel Committee on Banking Supervision (BCBS) and the International Organization of Securities Commissions (IOSCO). The final ","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121393269","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}
Many studies report that American option investors often exercise their positions suboptimally late. Yet, when that can happen in case of puts, there is an arbitrage opportunity in perfect markets, exploitable by longing the asset-and-riskfree-asset portfolio replicating the put and shorting the put. Using early exercise data, we show that the arbitrage strategy also earns a highly significant mean return with low risk in real single-stock put markets, in which exactly replicating options is impossible. In line with theory, the strategy performs particularly well on high strike-price puts in high interest-rate regimes. It further performs well on short time-to-maturity puts on low volatility stocks, consistent with evidence that investors do not correctly incorporate those characteristics into their exercise decisions. The strategy survives accounting for trading and short-selling costs, at least when executed on liquid assets.
{"title":"Taking Money Off the Table: Suboptimal Early Exercises, Risky Arbitrage, and American Put Returns","authors":"K. Aretz, I. Garrett, A. Gazi","doi":"10.2139/ssrn.3677041","DOIUrl":"https://doi.org/10.2139/ssrn.3677041","url":null,"abstract":"Many studies report that American option investors often exercise their positions suboptimally late. Yet, when that can happen in case of puts, there is an arbitrage opportunity in perfect markets, exploitable by longing the asset-and-riskfree-asset portfolio replicating the put and shorting the put. Using early exercise data, we show that the arbitrage strategy also earns a highly significant mean return with low risk in real single-stock put markets, in which exactly replicating options is impossible. In line with theory, the strategy performs particularly well on high strike-price puts in high interest-rate regimes. It further performs well on short time-to-maturity puts on low volatility stocks, consistent with evidence that investors do not correctly incorporate those characteristics into their exercise decisions. The strategy survives accounting for trading and short-selling costs, at least when executed on liquid assets.","PeriodicalId":293888,"journal":{"name":"Econometric Modeling: Derivatives eJournal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129559634","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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