Pub Date : 2023-01-19eCollection Date: 2023-01-01DOI: 10.3897/BDJ.11.e98828
Andriy Novikov, Oleh Prylutskyi
Background: The dataset represents a comprehensive collection of occurrence records concerning the genus Aconitum (Ranunculaceae) in the Ukrainian Carpathians and adjacent territories. It is based primarily on the results of critical revision of the main herbarium collections of the Carpathian region (i.e. LW, LWS, LWKS, KRA, KRAM, CHER, KW, UU and KWHU). Besides this, the dataset contains the data parsed (and taxonomically revised) from the published materials and other available sources (e.g. Karel Domin's Card Index).
New information: In total, 2,280 occurrence records of the genus Aconitum representatives distributed in the Ukrainian Carpathians were published.
{"title":"Genus <i>Aconitum</i> (Ranunculaceae) in the Ukrainian Carpathians and adjacent territories.","authors":"Andriy Novikov, Oleh Prylutskyi","doi":"10.3897/BDJ.11.e98828","DOIUrl":"10.3897/BDJ.11.e98828","url":null,"abstract":"<p><strong>Background: </strong>The dataset represents a comprehensive collection of occurrence records concerning the genus <i>Aconitum</i> (Ranunculaceae) in the Ukrainian Carpathians and adjacent territories. It is based primarily on the results of critical revision of the main herbarium collections of the Carpathian region (i.e. LW, LWS, LWKS, KRA, KRAM, CHER, KW, UU and KWHU). Besides this, the dataset contains the data parsed (and taxonomically revised) from the published materials and other available sources (e.g. Karel Domin's Card Index).</p><p><strong>New information: </strong>In total, 2,280 occurrence records of the genus <i>Aconitum</i> representatives distributed in the Ukrainian Carpathians were published.</p>","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"16 1","pages":"e98828"},"PeriodicalIF":1.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10848784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89821407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimating risks of European option books using neural stochastic differential equation market models","authors":"Samuel N. Cohen, C. Reisinger, Sheng Wang","doi":"10.21314/jcf.2022.028","DOIUrl":"https://doi.org/10.21314/jcf.2022.028","url":null,"abstract":"","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67703201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A general control variate method for time-changed Lévy processes: an application to options pricing","authors":"Kenichiro Shiraya, Cong Wang, A. Yamazaki","doi":"10.21314/jcf.2023.006","DOIUrl":"https://doi.org/10.21314/jcf.2023.006","url":null,"abstract":"","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67703471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric","authors":"Pere Díaz Lozano, Toni Lozano Bagén, J. Vives","doi":"10.21314/jcf.2023.005","DOIUrl":"https://doi.org/10.21314/jcf.2023.005","url":null,"abstract":"","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67702970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José Brito, Andrei Goloubentsev, Evgeny Gonacharov
In this paper we explain how to compute gradients of functions of the form G = ½∑mi=1(Eyi - Ci)2, which often appear in the calibration of stochastic models, using automatic adjoint differentiation and parallelization. We expand on the work of Goloubentsev and Lakshtanov and give approaches that are faster and easier to implement. We also provide an implementation of our methods and apply the technique to calibrate European options.
{"title":"Automatic adjoint differentiation for special functions involving expectations","authors":"José Brito, Andrei Goloubentsev, Evgeny Gonacharov","doi":"10.21314/jcf.2023.007","DOIUrl":"https://doi.org/10.21314/jcf.2023.007","url":null,"abstract":"In this paper we explain how to compute gradients of functions of the form G = ½∑mi=1(Eyi - Ci)2, which often appear in the calibration of stochastic models, using automatic adjoint differentiation and parallelization. We expand on the work of Goloubentsev and Lakshtanov and give approaches that are faster and easier to implement. We also provide an implementation of our methods and apply the technique to calibrate European options.","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135550769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The construction of replication strategies for the pricing and hedging of derivative contracts in incomplete markets is a key problem in financial engineering. We interpret this problem as a “game with the world”, where one player (the investor) bets on what will happen and the other player (the market) decides what will happen. Inspired by the success of the Monte Carlo tree search (MCTS) in a variety of games and stochastic multiperiod planning problems, we introduce this algorithm as a method for replication in the presence of risk and market friction. Unlike model-free reinforcement learning methods (such as Q-learning), MCTS makes explicit use of an environment model. The role of this model is taken by a market simulator, which is frequently adopted even in the training of model-free methods, but its use allows MCTS to plan for the consequences of decisions prior to the execution of actions. We conduct experiments with the AlphaZero variant of MCTS on toy examples of simple market models and derivatives with simple payoff structures. We show that MCTS is capable of maximizing the utility of the investor’s terminal wealth in a setting where no external pricing information is available and rewards are granted only as a result of contractual cashflows. In this setting, we observe that MCTS has superior performance compared with the deep Q-network algorithm and comparable performance to “deep-hedging” methods.
{"title":"Hedging of financial derivative contracts via Monte Carlo tree search","authors":"Oleg Szehr","doi":"10.21314/jcf.2023.009","DOIUrl":"https://doi.org/10.21314/jcf.2023.009","url":null,"abstract":"The construction of replication strategies for the pricing and hedging of derivative contracts in incomplete markets is a key problem in financial engineering. We interpret this problem as a “game with the world”, where one player (the investor) bets on what will happen and the other player (the market) decides what will happen. Inspired by the success of the Monte Carlo tree search (MCTS) in a variety of games and stochastic multiperiod planning problems, we introduce this algorithm as a method for replication in the presence of risk and market friction. Unlike model-free reinforcement learning methods (such as Q-learning), MCTS makes explicit use of an environment model. The role of this model is taken by a market simulator, which is frequently adopted even in the training of model-free methods, but its use allows MCTS to plan for the consequences of decisions prior to the execution of actions. We conduct experiments with the AlphaZero variant of MCTS on toy examples of simple market models and derivatives with simple payoff structures. We show that MCTS is capable of maximizing the utility of the investor’s terminal wealth in a setting where no external pricing information is available and rewards are granted only as a result of contractual cashflows. In this setting, we observe that MCTS has superior performance compared with the deep Q-network algorithm and comparable performance to “deep-hedging” methods.","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135181001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The no-butterfly-arbitrage domain of the Gatheral stochastic-volatility-inspired (SVI) five-parameter formula for the volatility smile has recently been described. It requires in general a numerical minimization of two functions together with a few root-finding procedures. We study here the case of the famous surface SVI (SSVI) model with three parameters, to which we apply the SVI results in order to provide the nobutterfly- arbitrage domain. As side results, we prove that, under simple requirements on parameters, SSVI slices always satisfy Fukasawa’s weak conditions of no arbitrage (ie, the corresponding Black–Scholes functions d1 and d2 are always decreasing), and we find a simple subdomain of no arbitrage for the SSVI model that we compare with the well-known subdomain of Gatheral and Jacquier. We simplify the obtained no-arbitrage domain into a parameterization that requires only one immediate numerical procedure, leading to an easy-to-implement calibration algorithm. Finally, we show that the long-term Heston SVI model is in fact an SSVI model, and we characterize the horizon beyond which it is arbitrage free.
{"title":"Refined analysis of the no-butterfly-arbitrage domain for SSVI slices","authors":"Claude Martini, Arianna Mingone","doi":"10.21314/jcf.2023.008","DOIUrl":"https://doi.org/10.21314/jcf.2023.008","url":null,"abstract":"The no-butterfly-arbitrage domain of the Gatheral stochastic-volatility-inspired (SVI) five-parameter formula for the volatility smile has recently been described. It requires in general a numerical minimization of two functions together with a few root-finding procedures. We study here the case of the famous surface SVI (SSVI) model with three parameters, to which we apply the SVI results in order to provide the nobutterfly- arbitrage domain. As side results, we prove that, under simple requirements on parameters, SSVI slices always satisfy Fukasawa’s weak conditions of no arbitrage (ie, the corresponding Black–Scholes functions d1 and d2 are always decreasing), and we find a simple subdomain of no arbitrage for the SSVI model that we compare with the well-known subdomain of Gatheral and Jacquier. We simplify the obtained no-arbitrage domain into a parameterization that requires only one immediate numerical procedure, leading to an easy-to-implement calibration algorithm. Finally, we show that the long-term Heston SVI model is in fact an SSVI model, and we characterize the horizon beyond which it is arbitrage free.","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136003058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Toward a unified implementation of regression Monte Carlo algorithms","authors":"M. Ludkovski","doi":"10.21314/jcf.2023.004","DOIUrl":"https://doi.org/10.21314/jcf.2023.004","url":null,"abstract":"","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67702916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patrick Gierjatowicz, Marc Sabaté-Vidales, D. Šiška, Łukasz Szpruch, Zan Zuric
{"title":"Robust pricing and hedging via neural stochastic differential equations","authors":"Patrick Gierjatowicz, Marc Sabaté-Vidales, D. Šiška, Łukasz Szpruch, Zan Zuric","doi":"10.21314/jcf.2022.025","DOIUrl":"https://doi.org/10.21314/jcf.2022.025","url":null,"abstract":"","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"130 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67703046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Papanicolaou, Hau Fu, Prasanth Krishnamurthy, B. Healy, F. Khorrami
{"title":"An optimal control strategy for execution of large stock orders using long short-term memory networks","authors":"A. Papanicolaou, Hau Fu, Prasanth Krishnamurthy, B. Healy, F. Khorrami","doi":"10.21314/jcf.2023.003","DOIUrl":"https://doi.org/10.21314/jcf.2023.003","url":null,"abstract":"","PeriodicalId":51731,"journal":{"name":"Journal of Computational Finance","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67703281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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