Quantitative Finance最新文献

{"title":"How does price (in)efficiency influence cryptocurrency portfolios performance? The role of multifractality","authors":"Eduardo Amorim Vilela de Salis, Leandro dos Santos Maciel","doi":"10.1080/14697688.2023.2266448","DOIUrl":"https://doi.org/10.1080/14697688.2023.2266448","url":null,"abstract":"AbstractThis paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better risk–return relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies.Keywords: Portfolio allocationCryptocurrencyMarket efficiencyMF-DFAMultifractalityJEL Classifications: G14G11 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Markiel and Fama (Citation1970) and Titan (Citation2015) are surveys regarding the empirical analysis of the weak form of market efficiency.2 The Hurst exponent, referred to as the ‘index of dependence’ or ‘index of long-range dependence’, is used as a measure of long-term memory of time series. Originally developed in hydrology and commonly studied in fractal geometry, it relates to the autocorrelations of the time series, and the rate at which these decrease as the lag between pairs of values increases (Hurst Citation1951).3 Traditional nonlinear variance ratio tests or autocorrelation functions are not able to identify multifractal structures. Fractal properties are associated to time series that present heavy tails and long memory. As these features are commonly observed in financial asset price returns (stylized facts), the use of MF-DFA appears as a suitable technique to evaluate random walk properties in such series, as stated by the econophysics literature (Arshad et al. Citation2016, Ali et al. Citation2018, Tiwari et al. Citation2019).4 The works of Mensi et al. (Citation2018), Sukpitak and Hengpunya (Citation2016), Dewandaru et al. (Citation2015), Tiwari et al. (Citation2019), Shahzad et al. (Citation2017), Zhu and Zhang (Citation2018) and Rizvi and Arshad (Citation2017) are examples of using MF-DFA to evaluate th","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"17 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266660","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":"Smiles in delta","authors":"Arianna Mingone","doi":"10.1080/14697688.2023.2258932","DOIUrl":"https://doi.org/10.1080/14697688.2023.2258932","url":null,"abstract":"AbstractFukasawa introduced in Fukasawa [The normalizing transformation of the implied volatility smile. Math. Finance, 2012, 22(4), 753–762] two necessary conditions for no butterfly arbitrage on a given implied volatility smile which require that the functions d1 and d2 of the Black–Scholes formula have to be decreasing. In this article, we characterize the set of smiles satisfying these conditions, using the parametrization of the smile in delta. We obtain a parametrization of the set of such smiles via one real number and three positive functions, which can be used by practitioners to calibrate a weak arbitrage-free smile. We also show that such smiles and their symmetric smiles can be transformed into smiles in the strike space by a bijection. Our result motivates the study of the challenging question of characterizing the subset of butterfly arbitrage-free smiles using the parametrization in delta.Keywords: Implied volatilityVolatility smileDeltaButterfly arbitrageJEL Classification: G13C60C63 AcknowledgmentsI sincerely thank Zeliade Systems and especially Claude Martini for giving the opportunity to work with them and daily broaden my knowledge. The results in this paper have been achieved thanks to the concrete need of client CCPs of a volatility calibration in the sigma space which can be converted in the strike space. I thank Stefano De Marco for the precise reading of the article, and for the improvements suggested. I thank Antoine Jacquier who pointed out crucial refinements, and Vladimir Lucic who shared his fundamental knowledge with enthusiasm.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135738482","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":"Quantum-inspired variational algorithms for partial differential equations: application to financial derivative pricing","authors":"Tianchen Zhao, Chuhao Sun, Asaf Cohen, James Stokes, Shravan Veerapaneni","doi":"10.1080/14697688.2023.2259954","DOIUrl":"https://doi.org/10.1080/14697688.2023.2259954","url":null,"abstract":"AbstractVariational quantum Monte Carlo (VMC) combined with neural-network quantum states offers a novel angle of attack on the curse-of-dimensionality encountered in a particular class of partial differential equations (PDEs); namely, the real- and imaginary time-dependent Schrödinger equation. In this paper, we present a simple generalization of VMC applicable to arbitrary time-dependent PDEs, showcasing the technique in the multi-asset Black-Scholes PDE for pricing European options contingent on many correlated underlying assets.Keywords: Variational quantum algorithmsVariational quantum Monte CarloMulti-asset Black-Scholes PDE AcknowledgmentsThe Authors thank the anonymous AE and the referees for their suggestions, which helped to improve our paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 VQAs for mult-asset financial derivative pricing have been subsequently explored in Kubo et al. (Citation2022).2 See appendix 1 for a derivation. Similiar ODEs have been introduced in the neural Galerkin method (Bruna et al. Citation2022).3 This can be considered as a special case of the complex-valued case (Hibat-Allah et al. Citation2020, Sharir et al. Citation2020).Additional informationFundingAuthors gratefully acknowledge support from NSF under grants DMS-2038030 and DMS-2006305. This research was supported in part through computational resources and services provided by the Advanced Research Computing (ARC) at the University of Michigan.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135198920","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":"Household financial health: a machine learning approach for data-driven diagnosis and prescription","authors":"Kyeongbin Kim, Yoontae Hwang, Dongcheol Lim, Suhyeon Kim, Junghye Lee, Yongjae Lee","doi":"10.1080/14697688.2023.2254335","DOIUrl":"https://doi.org/10.1080/14697688.2023.2254335","url":null,"abstract":"AbstractHousehold finances are being threatened by unprecedented social and economic upheavals, including an aging society and slow economic growth. Numerous researchers and practitioners have provided guidelines for improving the financial status of households; however, the challenge of handling heterogeneous households remains nontrivial. In this study, we propose a new data-driven framework for the financial health of households to address the needs for diagnosing and improving financial health. This research extends the concept of healthcare to household finance. We develop a novel deep learning-based diagnostic model for estimating household financial health risk scores from real-world household balance sheet data. The proposed model can successfully manage the heterogeneity of households by extracting useful latent representations of household balance sheet data while incorporating the risk information of each variable. That is, we guide the model to generate higher latent values for households with risky balance sheets. We also show that the gradient of the model can be utilized for prescribing recommendations for improving household financial health. The robustness and validity of the new framework are demonstrated using empirical analyses.Keywords: Household financeFinancial healthHeterogeneityRisk scoringDeep learning Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Note that Indicator 4 follows the opposite direction of the other indicators. For Indicators 1 to 3, having a large value would increase financial risk, while it is the opposite for Indicator 4. Hence, stochastic dominance in Indicator 4 should also be interpreted in the opposite way from the other indicators.2 In Appendix C, we used the Bonferroni post-hoc test to assess the significance of the difference in risk information for each of the input variables to RI-HAE.3 To be more precise, the reciprocal of shadow price represents the amount of money required to increase the financial risk score by one unit estimated under first-order approximation because shadow price is a slope of the linear function tangent to RI-HAE.Additional informationFundingThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2022R1I1A4069163 and No. NRF-2020R1C1C1011063).","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135345246","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":"On prices and returns in commercial prediction markets","authors":"Karl Whelan","doi":"10.1080/14697688.2023.2257756","DOIUrl":"https://doi.org/10.1080/14697688.2023.2257756","url":null,"abstract":"The Commodity Futures Trading Commission (CFTC) has recently licensed a commercial prediction market to operate in the US. With regulatory restrictions lifted, these markets can now play the important role that has been often envisaged for them. For example, investors can use them to hedge various event-related risks directly rather than indirectly via portfolios expected to move a certain way if events occur. Commercial prediction markets charge fees, an element that has not been incorporated into previous theoretical work on these markets. We examine the impact of fees on prediction market prices and returns by introducing them to a model in which the market price equals the true probability when there are no fees. We find that existing fee models mean contract prices for low probability outcomes are below the true probability but the impact of fees means prediction markets feature a form of favorite-longshot bias: Post-fee loss rates depend negatively on the probability of the event being backed. We show this result holds even if prediction market operators set a fee structure that is more generous to contracts with a low probability of success.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885820","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 transform-based method for pricing Asian options under general two-dimensional models","authors":"Weinan Zhang, Pingping Zeng","doi":"10.1080/14697688.2023.2256358","DOIUrl":"https://doi.org/10.1080/14697688.2023.2256358","url":null,"abstract":"AbstractWe propose a unified transform-based method, which we call the extended double spiral (EDS) method, for pricing arithmetic Asian options under general two-dimensional (2D) models that nest regime-switching Lévy models, stochastic volatility (SV) models with Lévy jumps, and time-changed Lévy models. We first construct a new single backward induction in the state space that relaxes the restriction of the independent increments of the log-asset price. Second, we build an exact and explicit double backward induction in the Fourier space based on this single backward induction, a combination of the 1D Fourier transform method and a key function characterizing the 2D model, and the double spiral method. Third, we develop a unified EDS algorithm to recursively implement this double backward induction via the fast Fourier transform (FFT), various quadrature rules, asymmetric truncation boundaries, and so on. Extensive numerical results across a broad class of 2D models, monitoring frequencies, option moneyness, and model parameters demonstrate that our method is remarkably accurate, efficient, robust, simple to implement, and widely applicable.Keywords: Arithmetic Asian optionsTwo-dimensional modelsExtended double spiral methodFast Fourier transformJEL Classifications: C00C63G13 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Here and subsequently, the state space and the Fourier space refer to the component Y.2 As a remark, in the continuous case, we need the transformation from ν′ to ln⁡nu′ to calculate (Equation17(17) Qh,M,M~(i)g(β,ν):=12π∫E(∑m=−M~MΓ¯(i)(β−mh)g(αi)(mh,ν′)h)×ΨΔ(−β+iα3−i;ν,ν′)dν′(17) ) when the left tail of the density function of the process v in the function Ψ grows rapidly.3 The derivations originate from a manuscript by Cai and Zeng (Citation2023).Additional informationFundingPingping Zeng would like to acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 11701266 and 12171228), and the Philosophy and Social Science Planning Project of Guangdong Province, China (Grant No. GD20XGL31).","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885956","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":"Can volatility solve the naive portfolio puzzle?","authors":"Michael Curran, Ryan Zalla","doi":"10.1080/14697688.2023.2249996","DOIUrl":"https://doi.org/10.1080/14697688.2023.2249996","url":null,"abstract":"AbstractWe investigate whether sophisticated volatility estimation improves the out-of-sample performance of mean-variance portfolio strategies relative to the naive 1/N strategy. The portfolio strategies rely solely upon second moments. Using a diverse group of portfolios and econometric models across multiple datasets, most models achieve higher Sharpe ratios and lower portfolio volatility that are statistically and economically significant relative to the naive rule, even after controlling for turnover costs. Our results suggest benefits to employing more sophisticated econometric models than the sample covariance matrix, and that mean-variance strategies often outperform the naive portfolio across multiple datasets and assessment criteria.Keywords: Mean-varianceNaive portfoliovolatilityJEL: G11G17 AcknowledgmentsWe thank Caitlin Dannhauser, Jesús Fernández-Villaverde, Alejandro Lopez-Lira, Rabih Moussawi, Michael Pagano, Nikolai Roussanov, Paul Scanlon, Frank Schorfheide, John Sedunov, Raman Uppal, and Raisa Velthuis for helpful comments. Christopher Antonello provided diligent research assistance.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/10.1080/14697688.2023.2249996.Notes1 Instead of the portfolio strategy, our innovation explores a wide variety of econometric models. DeMiguel et al. (Citation2009b) find that the minimum-variance portfolio, though performing well relative to other portfolio strategies, significantly beats the 1/N strategy for only 1 in 7 of their datasets. Jagannathan and Ma (Citation2003) and Kirby and Ostdiek (Citation2012) innovate on the portfolio strategy, illustrating that short-sale constrained minimum-variance strategies and volatility-timing strategies enhance performance.2 We consider a wide range of mostly parametric econometric models. Non-parametric models using higher-frequency data (DeMiguel et al. Citation2013) and shrinkage approaches (Ledoit and Wolf Citation2017) also improve the accuracy of estimation. Daily frequency option-implied volatility reduces portfolio volatility, but never statistically significantly improves the Sharpe ratio relative to the 1/N strategy (DeMiguel et al. Citation2013). Although Johannes et al. (Citation2014) account for both estimation risk and time-varying volatility through eight variations of a similar class of constant and stochastic volatility models, we expand to more varied classes of volatility types with 14 econometric models. Initial investigations reveal our results to be at least as strong as Ledoit and Wolf (Citation2017).3 Our econometric estimation strategies yield improvements beyond the period and frequency differences.4 A portfolio strategy, whose covariance is estimated using a given econometric model, weakly dominates the naive benchmark if, for each performance criterion, the portfolio strategy performs at least as well a","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903424","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":"Optimal portfolio choice of couples with tax-deferred accounts and survival-contingent products","authors":"Sanghyeon Bae, Yongjae Lee, Woo Chang Kim","doi":"10.1080/14697688.2023.2252852","DOIUrl":"https://doi.org/10.1080/14697688.2023.2252852","url":null,"abstract":"AbstractFinancial products for retirement planning generally have complex taxation structures and death conditions. In particular, tax-deferred accounts (TDAs) can provide tax-sheltered wealth accumulation by deferring taxes, even with the same financial products. Additionally, various survival-contingent products (SCPs), such as annuity products and life insurance contracts, have different payouts for policyholders. In this study, considering both the TDA and SCPs, we formulate and solve a couple’s lifetime portfolio choice problem using a multistage stochastic programming model. Owing to its high-dimensional state space and lifelong planning periods, stochastic dual dynamic programming (SDDP) was used to solve this problem. We find some interesting results; when both the TDA and SCPs are available, the portfolio is less concentrated in annuity holdings than when the TDA is unavailable. Moreover, the couple ends their contribution to the TA earlier than when SCPs are unavailable.Keywords: Retirement planningTax-deferred accountsSurvival-contingent productsLife insuranceImmediate annuitiesStochastic dual dynamic programming Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 In reality, an insurance company calculates the exclusion ratio to determine the proportion of non-taxable annuity payouts for each period. The exclusion ratio is calculated by the expected value of investment (the payouts multiplied by life expectancy of the annuitant) divided by the principal. After the non-taxable parts of all distributed payouts based on the exclusion ratios exceeds the principal (that is, the annuitant outlives his or her life expectancy), the payouts become all taxable. However, to reflect the exclusion ratio, we should record the principal and exclusion ratio in each year (not the dynamics). Hence, for computational tractability, we do not calculate the exclusion ratio and the principal in each year and formulate dynamics of the non-taxable part of the principals as above. We emphasize that although the formulation is slightly different from the reality, to best of our knowledge, this is the first attempt to formulate the non-taxable parts of annuity payouts into the financial optimization problems.2 https://www.ssa.gov/oact/STATS/table4c6_2017_TR2020.html.3 With other parameters fixed, we replace labor income process in section 2.2 with Yti=exp⁡(f(t,Zt))PtiUti, Pti=Pt−1iNti,i∈{x,y}, It=rxItxYtx+ryItyYty, where σux=σuy=0.15, σnx=σny=0.10. We assume that permanent and transitory shocks are iid, but allow correlation between permanent shocks of spouses (transitory shocks, respectively) as in Wu and Krueger (Citation2021). We set correlation coefficients as ρnxny=0.08 and ρuxuy=0.31, where the values are derived from Wu and Krueger (Citation2021), and regenerate a scenario tree as in Appendix 2. The resulting number of branches is 20.Additional informationFundingThis study was supported by a National Research Foundat","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136265054","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 multi-curve HJM factor model for pricing and risk management","authors":"Tobias Bienek, Griselda Deelstra, Andreas Lichtenstern, Rudi Zagst","doi":"10.1080/14697688.2023.2251179","DOIUrl":"https://doi.org/10.1080/14697688.2023.2251179","url":null,"abstract":"AbstractIn this paper, we introduce a multi-curve model under the historical probability based upon multiplicative relative spreads, inspired by the HJM and affine factor approaches, which implies positive and ordered spreads. In particular, we focus upon δi-XIBOR relative (instantaneous) forward rates and appropriate XIBOR HJM drift constraints, and we describe the dynamics of the different forward rates and spreads under different measure changes (including forward measures). We introduce an explicit model satisfying both the XIBOR HJM drift constraints as well as the property of positive and ordered spreads. We demonstrate the flexibility of this model for derivative pricing by focusing upon the price of a caplet and of options with a payoff based upon XIBOR forward prices with different tenors. We perform on one hand a calibration of the model based upon cap prices. On the other hand, we do an estimation of a spread curve in our proposed model under the historical probability by using a Kalman filter approach. Numerical results are included, and they confirm that the model performs very well.Keywords: XIBOR rateMultiple yield curvesMultiplicative spreadAffine processesJEL Classifications: E43G12 AcknowledgmentsThe authors would like to thank the anonymous referees for their useful comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Following the lines of Konikov and McClelland (Citation2020) and the related SSRN-id6073 version, approximate quasi-explicit expressions for swaption prices can also be derived using some usual ‘freezing’ methods and Fourier inversion techniques.2 In figure 3, the notation ln⁡P6mS and s6m has been used since δ1 equals 6 months.Additional informationFundingGriselda Deelstra acknowledges support from the EU Framework Program for Research and Innovation Horizon 2020 (H2020-MSCA-ITN-2018, Project 813261, EID ABC-EU-XVA), as well as of the ARC grant IAPAS.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060228","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":"Bayesian nonparametric portfolio selection with rolling maximum drawdown control","authors":"Xiaoling Mei, Yachong Wang, Weixuan Zhu","doi":"10.1080/14697688.2023.2250386","DOIUrl":"https://doi.org/10.1080/14697688.2023.2250386","url":null,"abstract":"We present a novel approach to the portfolio selection problem for a multiperiod investor facing multiple risky assets, trading constraints, and return predictability. Our objective is to maximize mean-variance utility while addressing the computational challenges arising from the curse of dimensionality associated with dynamic programming in the presence of trading constraints. To overcome this, we employ model predictive control, a computationally efficient method for solving the problem. Additionally, we propose the use of a non-parametric Bayesian model, specifically the hierarchical Dirichlet process based Hidden Markov Model (HDP-HMM), to predict the multiperiod mean and covariance of returns. Then, we consider a time-varying maximum drawdown to adjust the risk aversion, which can effectively cope with the limit loss problems. Through extensive simulation studies and empirical analysis, we demonstrate that trading strategies based on our proposed method outperform existing approaches in out-of-sample performance.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072123","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}