arXiv - QuantFin - Pricing of Securities最新文献_第3页

Pricing of European Calls with the Quantum Fourier Transform 用量子傅立叶变换为欧洲通话定价

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-04-22 DOI: arxiv-2404.14115

Tom Ewen

{"title":"Pricing of European Calls with the Quantum Fourier Transform","authors":"Tom Ewen","doi":"arxiv-2404.14115","DOIUrl":"https://doi.org/arxiv-2404.14115","url":null,"abstract":"The accurate valuation of financial derivatives plays a pivotal role in the\u0000finance industry. Although closed formulas for pricing are available for\u0000certain models and option types, exemplified by the European Call and Put\u0000options in the Black-Scholes Model, the use of either more complex models or\u0000more sophisticated options precludes the existence of such formulas, thereby\u0000requiring alternative approaches. The Monte Carlo simulation, an alternative\u0000approach effective in nearly all scenarios, has already been challenged by\u0000quantum computing techniques that leverage Amplitude Estimation. Despite its\u0000theoretical promise, this approach currently faces limitations due to the\u0000constraints of hardware in the Noisy Intermediate-Scale Quantum (NISQ) era. In this study, we introduce and analyze a quantum algorithm for pricing\u0000European call options across a broad spectrum of asset models. This method\u0000transforms a classical approach, which utilizes the Fast Fourier Transform\u0000(FFT), into a quantum algorithm, leveraging the efficiency of the Quantum\u0000Fourier Transform (QFT). Furthermore, we compare this novel algorithm with\u0000existing quantum algorithms for option pricing.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140804241","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}

引用次数: 0

An Asymmetric Capital Asset Pricing Model 非对称资本资产定价模型

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-04-22 DOI: arxiv-2404.14137

Abdulnasser Hatemi-J

引用次数: 0

Enhancing Valuation of Variable Annuities in Lévy Models with Stochastic Interest Rate 在具有随机利率的莱维模型中加强变额年金的估值

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-04-11 DOI: arxiv-2404.07658

Ludovic Goudenège, Andrea Molent, Xiao Wei, Antonino Zanette

{"title":"Enhancing Valuation of Variable Annuities in Lévy Models with Stochastic Interest Rate","authors":"Ludovic Goudenège, Andrea Molent, Xiao Wei, Antonino Zanette","doi":"arxiv-2404.07658","DOIUrl":"https://doi.org/arxiv-2404.07658","url":null,"abstract":"This paper extends the valuation and optimal surrender framework for variable\u0000annuities with guaranteed minimum benefits in a L'evy equity market\u0000environment by incorporating a stochastic interest rate described by the\u0000Hull-White model. This approach frames a more dynamic and realistic financial\u0000setting compared to previous literature. We exploit a robust valuation\u0000mechanism employing a hybrid numerical method that merges tree methods for\u0000interest rate modeling with finite difference techniques for the underlying\u0000asset price. This method is particularly effective for addressing the\u0000complexities of variable annuities, where periodic fees and mortality risks are\u0000significant factors. Our findings reveal the influence of stochastic interest\u0000rates on the strategic decision-making process concerning the surrender of\u0000these financial instruments. Through comprehensive numerical experiments, and\u0000by comparing our results with those obtained through the Longstaff-Schwartz\u0000Monte Carlo method, we illustrate how our refined model can guide insurers in\u0000designing contracts that equitably balance the interests of both parties. This\u0000is particularly relevant in discouraging premature surrenders while adapting to\u0000the realistic fluctuations of financial markets. Lastly, a comparative statics\u0000analysis with varying interest rate parameters underscores the impact of\u0000interest rates on the cost of the optimal surrender strategy, emphasizing the\u0000importance of accurately modeling stochastic interest rates.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578599","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}

引用次数: 0

Social Media Emotions and Market Behavior 社交媒体情感与市场行为

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-04-04 DOI: arxiv-2404.03792

Domonkos F. Vamossy

{"title":"Social Media Emotions and Market Behavior","authors":"Domonkos F. Vamossy","doi":"arxiv-2404.03792","DOIUrl":"https://doi.org/arxiv-2404.03792","url":null,"abstract":"I explore the relationship between investor emotions expressed on social\u0000media and asset prices. The field has seen a proliferation of models aimed at\u0000extracting firm-level sentiment from social media data, though the behavior of\u0000these models often remains uncertain. Against this backdrop, my study employs\u0000EmTract, an open-source emotion model, to test whether the emotional responses\u0000identified on social media platforms align with expectations derived from\u0000controlled laboratory settings. This step is crucial in validating the\u0000reliability of digital platforms in reflecting genuine investor sentiment. My\u0000findings reveal that firm-specific investor emotions behave similarly to lab\u0000experiments and can forecast daily asset price movements. These impacts are\u0000larger when liquidity is lower or short interest is higher. My findings on the\u0000persistent influence of sadness on subsequent returns, along with the\u0000insignificance of the one-dimensional valence metric, underscores the\u0000importance of dissecting emotional states. This approach allows for a deeper\u0000and more accurate understanding of the intricate ways in which investor\u0000sentiments drive market movements.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140579240","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}

引用次数: 0

Improved model-free bounds for multi-asset options using option-implied information and deep learning 利用期权推断信息和深度学习改进多资产期权的无模型界限

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-04-02 DOI: arxiv-2404.02343

Evangelia Dragazi, Shuaiqiang Liu, Antonis Papapantoleon

{"title":"Improved model-free bounds for multi-asset options using option-implied information and deep learning","authors":"Evangelia Dragazi, Shuaiqiang Liu, Antonis Papapantoleon","doi":"arxiv-2404.02343","DOIUrl":"https://doi.org/arxiv-2404.02343","url":null,"abstract":"We consider the computation of model-free bounds for multi-asset options in a\u0000setting that combines dependence uncertainty with additional information on the\u0000dependence structure. More specifically, we consider the setting where the\u0000marginal distributions are known and partial information, in the form of known\u0000prices for multi-asset options, is also available in the market. We provide a\u0000fundamental theorem of asset pricing in this setting, as well as a superhedging\u0000duality that allows to transform the maximization problem over probability\u0000measures in a more tractable minimization problem over trading strategies. The\u0000latter is solved using a penalization approach combined with a deep learning\u0000approximation using artificial neural networks. The numerical method is fast\u0000and the computational time scales linearly with respect to the number of traded\u0000assets. We finally examine the significance of various pieces of additional\u0000information. Empirical evidence suggests that \"relevant\" information, i.e.\u0000prices of derivatives with the same payoff structure as the target payoff, are\u0000more useful that other information, and should be prioritized in view of the\u0000trade-off between accuracy and computational efficiency.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578598","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}

引用次数: 0

Alternatives to classical option pricing 经典期权定价的替代方案

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-03-25 DOI: arxiv-2403.17187

W. Brent Lindquist, Svetlozar T. Rachev

引用次数: 0

Crypto Inverse-Power Options and Fractional Stochastic Volatility 加密货币反向权力期权和分数随机波动率

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-03-24 DOI: arxiv-2403.16006

Boyi Li, Weixuan Xia

{"title":"Crypto Inverse-Power Options and Fractional Stochastic Volatility","authors":"Boyi Li, Weixuan Xia","doi":"arxiv-2403.16006","DOIUrl":"https://doi.org/arxiv-2403.16006","url":null,"abstract":"Recent empirical evidence has highlighted the crucial role of jumps in both\u0000price and volatility within the cryptocurrency market. In this paper, we\u0000introduce an analytical model framework featuring fractional stochastic\u0000volatility, accommodating price--volatility co-jumps and volatility short-term\u0000dependency concurrently. We particularly focus on inverse options, including\u0000the emerging Quanto inverse options and their power-type generalizations, aimed\u0000at mitigating cryptocurrency exchange rate risk and adjusting inherent risk\u0000exposure. Characteristic function-based pricing--hedging formulas are derived\u0000for these inverse options. The general model framework is then applied to\u0000asymmetric Laplace jump-diffusions and Gaussian-mixed tempered stable-type\u0000processes, employing three types of fractional kernels, for an extensive\u0000empirical analysis involving model calibration on two independent Bitcoin\u0000options data sets, during and after the COVID-19 pandemic. Key insights from\u0000our theoretical analysis and empirical findings include: (1) the superior\u0000performance of fractional stochastic-volatility models compared to various\u0000benchmark models, including those incorporating jumps and stochastic\u0000volatility, (2) the practical necessity of jumps in both price and volatility,\u0000along with their co-jumps and rough volatility, in the cryptocurrency market,\u0000(3) stability of calibrated parameter values in line with stylized facts, and\u0000(4) the suggestion that a piecewise kernel offers much higher computational\u0000efficiency relative to the commonly used Riemann--Liouville kernel in\u0000constructing fractional models, yet maintaining the same accuracy level, thanks\u0000to its potential for obtaining explicit model characteristic functions.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140301772","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}

引用次数: 0

A Unifying Approach for the Pricing of Debt Securities 债务证券定价的统一方法

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-03-10 DOI: arxiv-2403.06303

Marie-Claude Vachon, Anne Mackay

{"title":"A Unifying Approach for the Pricing of Debt Securities","authors":"Marie-Claude Vachon, Anne Mackay","doi":"arxiv-2403.06303","DOIUrl":"https://doi.org/arxiv-2403.06303","url":null,"abstract":"We propose a unifying framework for the pricing of debt securities under\u0000general time-inhomogeneous short-rate diffusion processes. The pricing of\u0000bonds, bond options, callable/putable bonds, and convertible bonds (CBs) are\u0000covered. Using continuous-time Markov chain (CTMC) approximation, we obtain\u0000closed-form matrix expressions to approximate the price of bonds and bond\u0000options under general one-dimensional short-rate processes. A simple and\u0000efficient algorithm is also developed to price callable/putable debts. The\u0000availability of a closed-form expression for the price of zero-coupon bonds\u0000allows for the perfect fit of the approximated model to the current market term\u0000structure of interest rates, regardless of the complexity of the underlying\u0000diffusion process selected. We further consider the pricing of CBs under\u0000general bi-dimensional time-inhomogeneous diffusion processes to model equity\u0000and short-rate dynamics. Credit risk is also incorporated into the model using\u0000the approach of Tsiveriotis and Fernandes (1998). Based on a two-layer CTMC\u0000method, an efficient algorithm is developed to approximate the price of\u0000convertible bonds. When conversion is only allowed at maturity, a closed-form\u0000matrix expression is obtained. Numerical experiments show the accuracy and\u0000efficiency of the method across a wide range of model parameters and short-rate\u0000models.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140106430","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}

引用次数: 0

RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction RVRAE：基于变异递归自动编码器的动态因子模型，用于股票回报预测

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-03-04 DOI: arxiv-2403.02500

Yilun Wang, Shengjie Guo

{"title":"RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction","authors":"Yilun Wang, Shengjie Guo","doi":"arxiv-2403.02500","DOIUrl":"https://doi.org/arxiv-2403.02500","url":null,"abstract":"In recent years, the dynamic factor model has emerged as a dominant tool in\u0000economics and finance, particularly for investment strategies. This model\u0000offers improved handling of complex, nonlinear, and noisy market conditions\u0000compared to traditional static factor models. The advancement of machine\u0000learning, especially in dealing with nonlinear data, has further enhanced asset\u0000pricing methodologies. This paper introduces a groundbreaking dynamic factor\u0000model named RVRAE. This model is a probabilistic approach that addresses the\u0000temporal dependencies and noise in market data. RVRAE ingeniously combines the\u0000principles of dynamic factor modeling with the variational recurrent\u0000autoencoder (VRAE) from deep learning. A key feature of RVRAE is its use of a\u0000prior-posterior learning method. This method fine-tunes the model's learning\u0000process by seeking an optimal posterior factor model informed by future data.\u0000Notably, RVRAE is adept at risk modeling in volatile stock markets, estimating\u0000variances from latent space distributions while also predicting returns. Our\u0000empirical tests with real stock market data underscore RVRAE's superior\u0000performance compared to various established baseline methods.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140044548","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}

引用次数: 0

Stochastic expansion for the pricing of Asian options 亚洲期权定价的随机扩展

arXiv - QuantFin - Pricing of Securities Pub Date : 2024-02-27 DOI: arxiv-2402.17684

Fabien Le Floc'h

引用次数: 0