arXiv - QuantFin - Pricing of Securities最新文献_第7页

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-10-24 DOI: arxiv-2311.10738

Andres M. Alonso, Zehang Li

引用次数: 0

American Option Pricing using Self-Attention GRU and Shapley Value Interpretation 基于自关注GRU和Shapley价值解释的美式期权定价

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-10-19 DOI: arxiv-2310.12500

Yanhui Shen

{"title":"American Option Pricing using Self-Attention GRU and Shapley Value Interpretation","authors":"Yanhui Shen","doi":"arxiv-2310.12500","DOIUrl":"https://doi.org/arxiv-2310.12500","url":null,"abstract":"Options, serving as a crucial financial instrument, are used by investors to\u0000manage and mitigate their investment risks within the securities market.\u0000Precisely predicting the present price of an option enables investors to make\u0000informed and efficient decisions. In this paper, we propose a machine learning\u0000method for forecasting the prices of SPY (ETF) option based on gated recurrent\u0000unit (GRU) and self-attention mechanism. We first partitioned the raw dataset\u0000into 15 subsets according to moneyness and days to maturity criteria. For each\u0000subset, we matched the corresponding U.S. government bond rates and Implied\u0000Volatility Indices. This segmentation allows for a more insightful exploration\u0000of the impacts of risk-free rates and underlying volatility on option pricing.\u0000Next, we built four different machine learning models, including multilayer\u0000perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and\u0000self-attention GRU in comparison to the traditional binomial model. The\u0000empirical result shows that self-attention GRU with historical data outperforms\u0000other models due to its ability to capture complex temporal dependencies and\u0000leverage the contextual information embedded in the historical data. Finally,\u0000in order to unveil the \"black box\" of artificial intelligence, we employed the\u0000SHapley Additive exPlanations (SHAP) method to interpret and analyze the\u0000prediction results of the self-attention GRU model with historical data. This\u0000provides insights into the significance and contributions of different input\u0000features on the pricing of American-style options.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522559","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

Perpetual Futures Pricing 永久期货定价

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-10-18 DOI: arxiv-2310.11771

Damien Ackerer, Julien Hugonnier, Urban Jermann

引用次数: 0

Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model 跳跃扩散和市场情绪模型下的比特币期权估值神经网络

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-10-14 DOI: arxiv-2310.09622

Edson Pindza, Jules Clement Mba, Sutene Mwambi, Nneka Umeorah

{"title":"Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model","authors":"Edson Pindza, Jules Clement Mba, Sutene Mwambi, Nneka Umeorah","doi":"arxiv-2310.09622","DOIUrl":"https://doi.org/arxiv-2310.09622","url":null,"abstract":"Cryptocurrencies and Bitcoin, in particular, are prone to wild swings\u0000resulting in frequent jumps in prices, making them historically popular for\u0000traders to speculate. A better understanding of these fluctuations can greatly\u0000benefit crypto investors by allowing them to make informed decisions. It is\u0000claimed in recent literature that Bitcoin price is influenced by sentiment\u0000about the Bitcoin system. Transaction, as well as the popularity, have shown\u0000positive evidence as potential drivers of Bitcoin price. This study considers a\u0000bivariate jump-diffusion model to describe Bitcoin price dynamics and the\u0000number of Google searches affecting the price, representing a sentiment\u0000indicator. We obtain a closed formula for the Bitcoin price and derive the\u0000Black-Scholes equation for Bitcoin options. We first solve the corresponding\u0000Bitcoin option partial differential equation for the pricing process by\u0000introducing artificial neural networks and incorporating multi-layer perceptron\u0000techniques. The prediction performance and the model validation using various\u0000high-volatile stocks were assessed.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522486","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

Valuation Duration of the Stock Market 股票市场的估值持续时间

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-10-11 DOI: arxiv-2310.07110

Ye Li, Chen Wang

{"title":"Valuation Duration of the Stock Market","authors":"Ye Li, Chen Wang","doi":"arxiv-2310.07110","DOIUrl":"https://doi.org/arxiv-2310.07110","url":null,"abstract":"At the peak of the tech bubble, only 0.57% of market valuation comes from\u0000dividends in the next year. Taking the ratio of total market value to the value\u0000of one-year dividends, we obtain a valuation-based duration of 175 years. In\u0000contrast, at the height of the global financial crisis, more than 2.2% of\u0000market value is from dividends in the next year, implying a duration of 46\u0000years. What drives valuation duration? We find that market participants have\u0000limited information about cash flow beyond one year. Therefore, an increase in\u0000valuation duration is due to a decrease in the discount rate rather than good\u0000news about long-term growth. Accordingly, valuation duration negatively\u0000predicts annual market return with an out-of-sample R2 of 15%, robustly\u0000outperforming other predictors in the literature. While the price-dividend\u0000ratio reflects the overall valuation level, our valuation-based measure of\u0000duration captures the slope of the valuation term structure. We show that\u0000valuation duration, as a discount rate proxy, is a critical state variable that\u0000augments the price-dividend ratio in spanning the (latent) state space for\u0000stock-market dynamics.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522487","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

Risk valuation of quanto derivatives on temperature and electricity 温度和电力的量子衍生品风险评估

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-10-10 DOI: arxiv-2310.07692

Aurélien Alfonsi, Nerea Vadillo

引用次数: 0

Actuarial Implications and Modeling of Yellow Virus on Sugar Beet After the EU's Ban on Neonicotinoids and Climate Change 欧盟禁止新烟碱和气候变化后甜菜黄病毒的精算影响和建模

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-10-03 DOI: arxiv-2310.01869

Martial Phélippé-GuinvarcGAINS, Jean Cordier

引用次数: 0

The ATM implied skew in the ADO-Heston model ATM隐含了ADO-Heston模型的倾斜

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-09-26 DOI: arxiv-2309.15044

Andrey Itkin

{"title":"The ATM implied skew in the ADO-Heston model","authors":"Andrey Itkin","doi":"arxiv-2309.15044","DOIUrl":"https://doi.org/arxiv-2309.15044","url":null,"abstract":"In this paper similar to [P. Carr, A. Itkin, 2019] we construct another\u0000Markovian approximation of the rough Heston-like volatility model - the\u0000ADO-Heston model. The characteristic function (CF) of the model is derived\u0000under both risk-neutral and real measures which is an unsteady\u0000three-dimensional PDE with some coefficients being functions of the time $t$\u0000and the Hurst exponent $H$. To replicate known behavior of the market implied\u0000skew we proceed with a wise choice of the market price of risk, and then find a\u0000closed form expression for the CF of the log-price and the ATM implied skew.\u0000Based on the provided example, we claim that the ADO-Heston model (which is a\u0000pure diffusion model but with a stochastic mean-reversion speed of the variance\u0000process, or a Markovian approximation of the rough Heston model) is able\u0000(approximately) to reproduce the known behavior of the vanilla implied skew at\u0000small $T$. We conclude that the behavior of our implied volatility skew curve\u0000${cal S}(T) propto a(H) T^{bcdot (H-1/2)}, , b = const$, is not exactly\u0000same as in rough volatility models since $b ne 1$, but seems to be close\u0000enough for all practical values of $T$. Thus, the proposed Markovian model is\u0000able to replicate some properties of the corresponding rough volatility model.\u0000Similar analysis is provided for the forward starting options where we found\u0000that the ATM implied skew for the forward starting options can blow-up for any\u0000$s > t$ when $T to s$. This result, however, contradicts to the observation of\u0000[E. Alos, D.G. Lorite, 2021] that Markovian approximation is not able to catch\u0000this behavior, so remains the question on which one is closer to reality.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522555","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 Markovian empirical model for the VIX index and the pricing of the corresponding derivatives VIX指数及其衍生品定价的马尔可夫经验模型

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-09-15 DOI: arxiv-2309.08175

Ying-Li Wang, Cheng-Long Xu, Ping He

引用次数: 0

Applying Deep Learning to Calibrate Stochastic Volatility Models 应用深度学习校准随机波动率模型

arXiv - QuantFin - Pricing of Securities Pub Date : 2023-09-14 DOI: arxiv-2309.07843

Abir Sridi, Paul Bilokon

{"title":"Applying Deep Learning to Calibrate Stochastic Volatility Models","authors":"Abir Sridi, Paul Bilokon","doi":"arxiv-2309.07843","DOIUrl":"https://doi.org/arxiv-2309.07843","url":null,"abstract":"Stochastic volatility models, where the volatility is a stochastic process,\u0000can capture most of the essential stylized facts of implied volatility surfaces\u0000and give more realistic dynamics of the volatility smile or skew. However, they\u0000come with the significant issue that they take too long to calibrate. Alternative calibration methods based on Deep Learning (DL) techniques have\u0000been recently used to build fast and accurate solutions to the calibration\u0000problem. Huge and Savine developed a Differential Deep Learning (DDL) approach,\u0000where Machine Learning models are trained on samples of not only features and\u0000labels but also differentials of labels to features. The present work aims to\u0000apply the DDL technique to price vanilla European options (i.e. the calibration\u0000instruments), more specifically, puts when the underlying asset follows a\u0000Heston model and then calibrate the model on the trained network. DDL allows\u0000for fast training and accurate pricing. The trained neural network dramatically\u0000reduces Heston calibration's computation time. In this work, we also introduce different regularisation techniques, and we\u0000apply them notably in the case of the DDL. We compare their performance in\u0000reducing overfitting and improving the generalisation error. The DDL\u0000performance is also compared to the classical DL (without differentiation) one\u0000in the case of Feed-Forward Neural Networks. We show that the DDL outperforms\u0000the DL.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522566","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