Quantitative Finance最新文献

{"title":"A model of dynamic information production for initial public offerings","authors":"Rafiqul Bhuyan, Coşkun Çetin, Burhaneddin İzgi, Bakhtear Talukdar","doi":"10.1080/14697688.2023.2273975","DOIUrl":"https://doi.org/10.1080/14697688.2023.2273975","url":null,"abstract":"We develop a multi-period information-theoretic model of initial public offering (IPO) in the presence of an adverse selection problem that addresses both underpricing in an IPO and subsequent unde...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"163 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531656","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":"Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices","authors":"Damir Filipovic","doi":"10.1080/14697688.2023.2280101","DOIUrl":"https://doi.org/10.1080/14697688.2023.2280101","url":null,"abstract":"Published in Quantitative Finance (Ahead of Print, 2023)","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"8 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543200","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":"Effective stochastic local volatility models","authors":"M. Felpel, J. Kienitz, T.A. McWalter","doi":"10.1080/14697688.2023.2271514","DOIUrl":"https://doi.org/10.1080/14697688.2023.2271514","url":null,"abstract":"If a high degree of accuracy and market consistency is required for option pricing, stochastic local volatility models are often the approach of choice. When calibrating these types of models, one of the major challenges lies in the proper fitting of the leverage function. This often requires an optimization procedure in terms of computationally intensive numerical methods, such as Monte Carlo simulation, or methods not well suited to local volatility formulations, such as Fourier transform pricing. In this article, we provide an alternative approach using an effective stochastic volatility technique, which provides an efficient semi-analytical approximation of the PDE for the density function of the underlying. This approach allows efficient direct calibration of the leverage function for a large class of stochastic local volatility models, which includes stochastic volatility models such as the SABR, ZABR or Heston model as the underlying base model. We provide calibration and computational schemes and illustrate our approach using numerical experiments.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"119 1","pages":"1731 - 1750"},"PeriodicalIF":1.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531646","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":"Principled pasting: attaching tails to risk-neutral probability density functions recovered from option prices","authors":"Thomas R. Bollinger, William R. Melick, Charles P. Thomas","doi":"10.1080/14697688.2023.2272677","DOIUrl":"https://doi.org/10.1080/14697688.2023.2272677","url":null,"abstract":"The popular ‘curve-fitting’ method of using option prices to construct an underlying asset's risk neutral probability density function (RND) first recovers the interior of the density and then attaches left and right tails. Typically, the tails are constructed so that values of the RND and risk neutral cumulative distribution function (RNCDF) from the interior and the tails match at the attachment points. We propose and demonstrate the feasibility of also requiring that the left and right tails accurately price the options with strikes at the attachment points. Our methodology produces a RND that provides superior pricing performance than earlier curve-fitting methods for both those options used in the construction of the RND and those that were not. We also demonstrate that Put-Call Parity complicates the classification of in and out of sample options.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"182 1‐2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135476387","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":"Cryptocurrency factor momentum","authors":"Christian Fieberg, Gerrit Liedtke, Daniel Metko, Adam Zaremba","doi":"10.1080/14697688.2023.2269999","DOIUrl":"https://doi.org/10.1080/14697688.2023.2269999","url":null,"abstract":"AbstractIs there a momentum effect in cryptocurrency anomalies? To answer this, we analyze data from over 3900 coins spanning the years 2014 to 2022 and replicate 34 anomalies in the cross-section of cryptocurrency returns. We document a discernible pattern in factor premia: past winners consistently outperform losers. The effect persists across subperiods, withstands various methodological approaches, and its magnitude parallels that of its stock market counterpart. However, the autocorrelation in factor returns is not widespread and primarily stems from size and volatility anomalies. Additionally, unlike in stocks, cryptocurrency factor momentum originates from price momentum, which subsequently transfers to the factor level.Keywords: Factor momentumCryptocurrency anomaliesThe cross-section of cryptocurrency returnsReturn predictabilityJEL Classifications: G12G14G11G10 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Note that the mean return of the cross-sectional factor momentum strategy is half of the difference between its long and short legs.2 See Table IA.I on page 3 in the Internet Appendix to Ehsani and Linnainmaa (Citation2022).3 Specifically, at the beginning of each portfolio holding period, we include only those cryptocurrencies with an Amihud (Citation2002) measure below the 50% and 25% percentile (see table 2 for the variable description). Note that the cryptocurrency market is extremely skewed as a few large cryptocurrencies account for the majority of the aggregate market capitalization. By only looking at the 50% and 25% most liquid cryptocurrencies, we still cover, on average, 98.6% and 97.1% of the aggregate market capitalization, respectively. Therefore, this restricted sample is of high relevance for real-world cryptocurrency trading.Additional informationFundingThis work was supported by Narodowe Centrum Nauki [2021/41/B/HS4/02443].","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135476533","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 basket half full: sparse portfolios","authors":"Ekaterina Seregina","doi":"10.1080/14697688.2023.2269997","DOIUrl":"https://doi.org/10.1080/14697688.2023.2269997","url":null,"abstract":"AbstractThe existing approaches to sparse wealth allocations (1) are limited to low-dimensional setup when the number of assets is less than the sample size; (2) lack theoretical analysis of sparse wealth allocations and their impact on portfolio exposure; (3) are suboptimal due to the bias induced by an ℓ1-penalty. We address these shortcomings and develop an approach to construct sparse portfolios in high dimensions. Our contribution is twofold: from the theoretical perspective, we establish the oracle bounds of sparse weight estimators and provide guidance regarding their distribution. From the empirical perspective, we examine the merit of sparse portfolios during different market scenarios. We find that in contrast to non-sparse counterparts, our strategy is robust to recessions and can be used as a hedging vehicle during such times.Keywords: High dimensionalityPortfolio optimizationFactor investingDe-biasingPost-LassoApproximate factor modelJEL Classifications: C13C55C58G11G17 AcknowledgmentsI greatly appreciate thoughtful comments and immense support from Tae-Hwy Lee, Jean Helwege, Jang-Ting Guo, Aman Ullah, Matthew Lyle, Varlam Kutateladze and UC Riverside Finance faculty. I also thank seminar participants at the 14th International CFE Conference (virtual), 2021 Southwestern Finance Association Annual Meeting, and Vilnius University.The author would like to thank the editor and two anonymous referees for their helpful and constructive comments on the paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 MSCI ITALY, MSCI SPAIN, MSCI PORTUGAL, MSCI FRANCE, MSCI GERMANY, MSCI AUSTRIA, MSCI DENMARK, MSCI FINLAND, MSCI NETHERLANDS, MSCI SWEDEN, MSCI SWITZERLAND, MSCI TURKEY, MSCI CANADA, MSCI BRAZIL, MSCI MEXICO, MSCI COLOMBIA, MSCI ARGENTINA, MSCI PERU, MSCI CHILE, MSCI CHINA, MSCI INDIA, MSCI INDONESIA, MSCI RUSSIA, MSCI JAPAN, MSCI MALAYSIA, MSCI SINGAPORE, MSCI TAIWAN, MSCI SOUTH AFRICA, MSCI AUSTRALIA, MSCI KOREA, MSCI US.2 Since the optimization problem with a cardinality constraint is not convex, we find a solution using the Lagrangian relaxation procedure of Shaw et al. (Citation2008)3 Our empirical results suggest that the unbiased estimator θˆ=((T−p−2)mˆ′Σˆ−1mˆ−p)/T is oftentimes negative even after using the adjusted estimator defined in Kan and Zhou (Citation2007, p. 2906).4 Note that we cannot directly apply Theorem 2.2 of van de Geer et al. (Citation2014) since y needs to be estimated and we first need to show consistency of the respective estimator.5 The results for larger degrees of freedom do not provide any additional insight, hence we do not report them here. However, they are available upon request.6 The conclusions from using daily data are the same as those for monthly returns, hence we do not report them in the main manuscript text. However, they are available upon request.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"3 1‐4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135544447","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":"Rule-based trading on an order-driven exchange: a reassessment","authors":"Alan G. Isaac, Vasudeva Ramaswamy","doi":"10.1080/14697688.2023.2270711","DOIUrl":"https://doi.org/10.1080/14697688.2023.2270711","url":null,"abstract":"AbstractA core research area of computational behavioral finance investigates emergent price dynamics when heterogeneous traders follow a mix of rule-based strategies and interact indirectly through a limit order book. This paper offers a detailed specification of such a model in order to raise questions about some previous findings. The questions force a comprehensive reconsideration of the price dynamics of a well-known model. This leads to a surprising clarification of the contributions of various trading strategies to market outcomes: a popular characterization of chartism proves largely irrelevant for price dynamics. We also shed new light on the volume-volatility relationship, and provide improved visualizations to expose market behavior.Keywords: ChartismPrice dynamicsReturn volatilityTrade volumeMarket microstructureLimit order bookComputational behavioral financeJEL Classifications: G12G17C63 Open ScholarshipThis article has earned the Center for Open Science badges for Open Data and Open Materials through Open Practices Disclosure. The data and materials are openly accessible at https://figshare.com/s/e02cbb7790ab902eb72e.AcknowledgmentsEqual authorship; the authors are in alphabetical order. We thank Ben Dempe, two anonymous referees, and an Associate Editor for helpful suggestions. We particularly thank Blake LeBaron for useful discussions and kind encouragement.Disclosure statementNo potential conflict of interest was reported by the author(s).Supplemental dataSupplemental data for this article can be accessed online at http://dx.doi.org/10.1080/14697688.2023.2270711.Notes1 If all floating point prices were acceptable, the market would not see common prices across orders. However, a market in a security typically has a tick size, which is the minimal price increment. In addition, order prices outside an acceptable (wide) trading range are typically rejected. Chiarella and Iori (Citation2002, p. 348) address this by introducing a pre-specified grid of possible prices, based on the tick size (Δ). (Unfortunately, they do not document the minimum and maximum values of this grid.) We follow this practice, specifying a (wide) range of possible prices, from 1% to 200% of the reference fundamental price.2 In order to produce the plausible price dynamics required of a replication, their reported parameterization must be substantially rescaled during the price-forecast computation, as exposed by Chiarella et al. (Citation2009) and especially Pellizzari and Westerhoff (Citation2009). In addition, Chiarella et al. (Citation2009) constrain the weights to be positive, thereby removing contrarians from the chartist traders. The consequences of such a change are discussed in the supplement to our paper.3 This paper uses the fundamental price to initialize the price history. Results with a random initial price history are similar, so the model appears robust to this choice.4 The original code is unavailable (G. Iori, personal communication, 2020). Fi","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679624","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":"Mind the cap!—constrained portfolio optimisation in Heston's stochastic volatility model","authors":"M. Escobar-Anel, M. Kschonnek, R. Zagst","doi":"10.1080/14697688.2023.2271223","DOIUrl":"https://doi.org/10.1080/14697688.2023.2271223","url":null,"abstract":"AbstractWe consider a portfolio optimisation problem for a utility-maximising investor who faces convex constraints on his portfolio allocation in Heston's stochastic volatility model. We apply existing duality methods to obtain a closed-form expression for the optimal portfolio allocation. In doing so, we observe that allocation constraints impact the optimal constrained portfolio allocation in a fundamentally different way in Heston's stochastic volatility model than in the Black Scholes model. In particular, the optimal constrained portfolio may be different from the naive ‘capped’ portfolio, which caps off the optimal unconstrained portfolio at the boundaries of the constraints. Despite this difference, we illustrate by way of a numerical analysis that in most realistic scenarios the capped portfolio leads to slim annual wealth equivalent losses compared to the optimal constrained portfolio. During a financial crisis, however, a capped solution might lead to compelling annual wealth equivalent losses.Keywords: Portfolio optimisationAllocation constraintsDynamic programmingHeston's stochastic volatility modelIncomplete marketsJEL Classifications: G11C61 Disclosure statementNo potential conflict of interest was reported by the author(s).Supplemental dataSupplemental data for this article can be accessed online at http://dx.doi.org/10.1080/14697688.2023.2271223.Notes1 Note that obtaining and formally verifying the optimality of a candidate portfolio process requires more than just a solution to the associated HJB PDE, as pointed out by Korn and Kraft (Citation2004).2 As any π∈Λ can only take finite values L[0,T]⊗Q-a.s., we do not need to distinguish between (−∞,β] and [−∞,β] or [α,∞) and [α,∞] for any −∞≤α,β≤∞.3 Technically, one can formulate this assumption less restrictively by expressing ‘No Blow-Up’ in terms of the time spent in each of the zones Z−, Z0 and Z+. However, as this would significantly complicate the presentation without adding major additional insights, it is omitted here.4 If ρ=0 all of these transition times will be infinite.5 Using a similar separation with respect to the zones Z−, Z0 and Z+ and equation (B6), it is also possible to determine a closed-form expression for A from lemma 2.1.6 Equation (Equation18(18) b1−bη(κρσ+η2)<κ22σ2,(18) ) corresponds to part (i) of Assumption 2.4. In the setting of Kraft (Citation2005), part (ii) of Assumption 2.4 is also implied by (Equation18(18) b1−bη(κρσ+η2)<κ22σ2,(18) ) and so does not have to be mentioned explicitly.7 Note that this is different from classic mean-variance optimisation, where the variance of the terminal portfolio wealth Vv0,π(T) is constrained.8 Q.ai (Citation2022) reported that the average length of an S&P500 bear market (defined as a period with drawdown in excess of 20%) was 289 days.9 Since we exclusively work with power utility functions in this paper, we may without loss of generality assume that the WEL is independent of wealth.10 If π is deterministic and Jπ i","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"19 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679626","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":"Islamic Banking and Finance, Second Edition <b>Islamic Banking and Finance, Second Edition</b> , by Zubair Hasan, Routledge (2023). Hardcover. ISBN 978-1-032-36064-5. E-book. ISBN 978-1-003-36697-3.","authors":"Muhammad Ash-Shiddiqy, None Mujtahid, None Khamim","doi":"10.1080/14697688.2023.2270495","DOIUrl":"https://doi.org/10.1080/14697688.2023.2270495","url":null,"abstract":"","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"38 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868344","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":"Dynamic core-satellite investing using higher order moments: an explicit solution","authors":"Yanfeng Wang, Wanbo Lu, Kris Boudt","doi":"10.1080/14697688.2023.2269987","DOIUrl":"https://doi.org/10.1080/14697688.2023.2269987","url":null,"abstract":"AbstractThe goal of core-satellite investing is to optimally balance the portfolio allocation between a core and satellite investment. This paper provides an explicit solution when the investor's optimality criterion is the third-order and fourth-order expansion of the expected utility function, respectively. Based on a numeric example, we document the sensitivity of the proposed weights to coskewness and cokurtosis components. Finally, we use ETFs to examine the portfolio performance of the core-satellite strategy with higher order moments. We document that integrating the higher order moment in core-satellite investing can improve the financial performance of a portfolio.Keywords: Higher order momentsExplicit solutionCore-satellite investingSensitivityJEL Classifications: G11C61 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 For more convenient expression, we report the moments for the percentage log return in percentage point, but in the subsequent analysis, the moments of the log return are used.Additional informationFunding This work was partially supported by the Characteristic & Preponderant Discipline of Key Construction Universities in Zhejiang Province (Zhejiang Gongshang University-Statistics) and the Collaborative Innovation Center of Statistical Data Engineering Technology & Application. National Natural Science Foundation of China [grant number 71771187, 72011530149, 72163029] and Fundamental Research Funds for the Central Universities in China [grant number JBK190602].","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"39 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863982","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}