{"title":"Taming the Crouching Tigers and Hidden Dragons of Asian Finance: A Crash Course (Presentation Slides)","authors":"R. Kashyap","doi":"10.2139/ssrn.3133966","DOIUrl":"https://doi.org/10.2139/ssrn.3133966","url":null,"abstract":"","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116864619","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}
S. Gugushvili, F. Meulen, Moritz Schauer, P. Spreij
{"title":"Nonparametric Bayesian Volatility Estimation","authors":"S. Gugushvili, F. Meulen, Moritz Schauer, P. Spreij","doi":"10.1007/978-3-030-04161-8_19","DOIUrl":"https://doi.org/10.1007/978-3-030-04161-8_19","url":null,"abstract":"","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125441400","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}
Omar El Euch, M. Fukasawa, Jim Gatheral, M. Rosenbaum
{"title":"Short-Term at-the-Money Asymptotics Under Stochastic Volatility Models","authors":"Omar El Euch, M. Fukasawa, Jim Gatheral, M. Rosenbaum","doi":"10.2139/ssrn.3111471","DOIUrl":"https://doi.org/10.2139/ssrn.3111471","url":null,"abstract":"A small-time Edgeworth expansion of the density of an asset price is given under a general stochastic volatility model, from which asymptotic expansions of put option prices and at-the-money implied volatilities follow. A limit theorem for at-the-money implied volatility skew and curvature is also given as a corollary. The rough Bergomi model is treated as an example.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130179568","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}
{"title":"Hedging and Pricing Early-Exercise Options With Complex Fourier Series Expansion","authors":"Ron T. L. Chan","doi":"10.2139/ssrn.3108693","DOIUrl":"https://doi.org/10.2139/ssrn.3108693","url":null,"abstract":"Abstract We introduce a new numerical method called the complex Fourier series (CFS) method proposed by Chan (2017) to price options with an early-exercise feature—American, Bermudan and discretely monitored barrier options—under exponential Levy asset dynamics. This new method allows us to quickly and accurately compute the values of early-exercise options and their Greeks. We also provide an error analysis to demonstrate that, in many cases, we can achieve an exponential convergence rate in the pricing method as long as we choose the correct truncated computational interval. Our numerical analysis indicates that the CFS method is computationally more comparable or favourable than the methods currently available. Finally, the superiority of the CFS method is illustrated with real financial data by considering Standard & Poor’s depositary receipts (SPDR) exchange-traded fund (ETF) on the S&P 500® index options, which are American options traded from November 2017 to February 2018 and from 30 January 2019 to 21 June 2019.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122590698","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}
{"title":"How Long Memory Affects True Dependence Structure? Evidence From the Dow Jones Islamic Sub-indexes","authors":"Sana Braiek, Rihab Bedoui, L. Belkacem","doi":"10.2139/ssrn.3100890","DOIUrl":"https://doi.org/10.2139/ssrn.3100890","url":null,"abstract":"Long memory is a stylized fact found in most financial return series. In this paper, we seek to examine the impact of the presence of long memory on the dependence structure. First, we fit the multivariate dependence structure using R-vine copulas for pairs of raw and filtered returns. Second, we discuss whether or not changes in the fitted dependence may be driven by long memory. Based on the comparison results, we found that filtered returns of Dow Jones sub-indexes capture higher dependence in the conditional dependence structure. However, they capture lower dependence in bivariate dependence. Thus, the true degree of dependence is masked and such information is crucial for hedging and portfolios management.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132764631","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}
{"title":"Confidence Set for Group Membership","authors":"Andreas Dzemski, R. Okui","doi":"10.2139/ssrn.3133878","DOIUrl":"https://doi.org/10.2139/ssrn.3133878","url":null,"abstract":"We develop new procedures to quantify the statistical uncertainty of data-driven clustering algorithms. In our panel setting, each unit belongs to one of a finite number of latent groups with group-specific regression curves. We propose methods for computing unit-wise and joint confidence sets for group membership. The unit-wise sets give possible group memberships for a given unit and the joint sets give possible vectors of group memberships for all units. We also propose an algorithm that can improve the power of our procedures by detecting units that are easy to classify. The confidence sets invert a test for group membership that is based on a characterization of the true group memberships by a system of moment inequalities. To construct the joint confidence, we solve a high-dimensional testing problem that tests group membership simultaneously for all units. We justify this procedure under $N, T to infty$ asymptotics where we allow $T$ to be much smaller than $N$. As part of our theoretical arguments, we develop new simultaneous anti-concentration inequalities for the MAX and the QLR statistics. Monte Carlo results indicate that our confidence sets have adequate coverage and are informative. We illustrate the practical relevance of our confidence sets in two applications.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128838911","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}
{"title":"Two Criteria for Good Measurements In Research: Validity and Reliability","authors":"H. Mohajan","doi":"10.26458/1746","DOIUrl":"https://doi.org/10.26458/1746","url":null,"abstract":"Reliability and validity are the two most important and fundamental features in the evaluation of any measurement instrument or tool for a good research. The purpose of this research is to discuss the validity and reliability of measurement instruments that are used in research. Validity concerns what an instrument measures, and how well it does so. Reliability concerns the faith that one can have in the data obtained from the use of an instrument, that is, the degree to which any measuring tool controls for random error. An attempt has been taken here to review the reliability and validity, and threat to them in some details.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116461805","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}
{"title":"A Testbed Experiment of a (Smart) Market Based, Student Transportation Policy: Non Convexities, Coordination, Non Existence","authors":"H. Lee, Travis Maron, C. Plott, Han Seo","doi":"10.2139/ssrn.3109291","DOIUrl":"https://doi.org/10.2139/ssrn.3109291","url":null,"abstract":"The paper develops and studies a decentralized mechanism for pricing and allocation challenges typically met with administrative processes. Traditional forms of markets are not used due to conditions associated with market failure, such as complex coordination problems, thin markets, non-convexities including and zero marginal cost due to lumpy transportation capacities. The mechanism rests on an assignment process that is guided by a computational process, enforces rules and channels information feedback to participants. Special, testbed experimental methods produce high levels of efficiency when confronted by individual behaviors that are consistent with traditional models of strategic behavior.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127172641","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}
{"title":"Yield Curve Fitting with Artificial Intelligence: A Comparison of Standard Fitting Methods with AI Algorithms","authors":"Dr. Achim Posthaus","doi":"10.2139/ssrn.3089344","DOIUrl":"https://doi.org/10.2139/ssrn.3089344","url":null,"abstract":"The yield curve is one of the fundamental input parameters of pricing theories in capital markets. Information about yields can be observed in a discrete form either directly through traded yield instruments (e.g. Interest Rate SWAP's) or indirectly through prices of bonds (e.g. Government Bonds). Capital markets usually create benchmark yield curves for specific and very liquid market instruments or issuers where many different quotes of individual yield information for specific maturities are observable. The standard methods to construct a continuous yield curve from the discrete observable yield data quotes are either a fit of a mathematical model function or a splines interpolation. This article expands the standard methods to Artificial Intelligence algorithms, which have the advantage to avoid any assumptions for the mathematical model functions of the yield curve and can conceptually adapt easily to any market changes. Nowadays the most widely used \"risk free\" yield curve in capital markets is the OIS curve, which is derived from observable Overnight Index SWAP's and is used in this article as the benchmark curve to derive and compare the different yield curve fits.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132870227","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}
{"title":"Asymptotic Theory for Clustered Samples","authors":"B. Hansen, Seojeong Lee","doi":"10.2139/ssrn.3099187","DOIUrl":"https://doi.org/10.2139/ssrn.3099187","url":null,"abstract":"We provide a complete asymptotic distribution theory for clustered data with a large number of groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous and unbounded cluster sizes. Our conditions cleanly nest the classical results for i.n.i.d. observations, in the sense that our conditions specialize to the classical conditions under independent sampling. We use this theory to develop a full asymptotic distribution theory for estimation based on linear least-squares, 2SLS, nonlinear MLE, and nonlinear GMM.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128109647","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}