Pub Date : 2024-09-10DOI: 10.1080/14697688.2024.2394220
Álvaro Guinea Juliá, Alet Roux
We present an approximation method based on the mixing formula [Hull, J. and White, A., The pricing of options on assets with stochastic volatilities. J. Finance, 1987, 42, 281–300; Romano, M. and ...
我们提出了一种基于混合公式的近似方法[Hull, J. 和 White, A., The pricing of options on assets with stochastic volatilities.J. Finance, 1987, 42, 281-300; Romano, M. and ...
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Pub Date : 2024-09-05eCollection Date: 2024-01-01DOI: 10.1080/14697688.2024.2387222
Fernando Moreno-Pino, Stefan Zohren
Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical findings demonstrate that dilated convolutional filters are highly effective at extracting relevant information from intraday financial time-series, proving that this architecture can effectively leverage predictive information present in high-frequency data that would otherwise be lost if realised measures were precomputed. Simultaneously, dilated convolutional filters trained with intraday high-frequency data help us avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data, resulting in more accurate predictions compared to traditional methodologies and producing more accurate risk measures.
{"title":"DeepVol: volatility forecasting from high-frequency data with dilated causal convolutions.","authors":"Fernando Moreno-Pino, Stefan Zohren","doi":"10.1080/14697688.2024.2387222","DOIUrl":"https://doi.org/10.1080/14697688.2024.2387222","url":null,"abstract":"<p><p>Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical findings demonstrate that dilated convolutional filters are highly effective at extracting relevant information from intraday financial time-series, proving that this architecture can effectively leverage predictive information present in high-frequency data that would otherwise be lost if realised measures were precomputed. Simultaneously, dilated convolutional filters trained with intraday high-frequency data help us avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data, resulting in more accurate predictions compared to traditional methodologies and producing more accurate risk measures.</p>","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"24 8","pages":"1105-1127"},"PeriodicalIF":1.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11473055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142473227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1080/14697688.2024.2391523
Christian Bayer, Simon Breneis
We provide an efficient and accurate simulation scheme for the rough Heston model in the standard (H>0) as well as the hyper-rough regime (H>−1/2). The scheme is based on low-dimensional Markovian ...
{"title":"Efficient option pricing in the rough Heston model using weak simulation schemes","authors":"Christian Bayer, Simon Breneis","doi":"10.1080/14697688.2024.2391523","DOIUrl":"https://doi.org/10.1080/14697688.2024.2391523","url":null,"abstract":"We provide an efficient and accurate simulation scheme for the rough Heston model in the standard (H>0) as well as the hyper-rough regime (H>−1/2). The scheme is based on low-dimensional Markovian ...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"3 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188773","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}
Pub Date : 2024-08-30DOI: 10.1080/14697688.2024.2386323
Ellie Papavassiliou, Nikolas Topaloglou, Stavros A. Zenios
Using stochastic spanning tests without any distributional assumptions on returns, we show that the two classes of GDP-linked bonds, floaters and linkers, are not spanned by a broad benchmark set o...
我们使用随机跨度测试，在不对回报率进行任何分布假设的情况下，证明浮动债券和挂钩债券这两类与 GDP 挂钩的债券没有被一组广泛的基准债券跨度所跨度。
{"title":"GDP-linked bonds as a new asset class","authors":"Ellie Papavassiliou, Nikolas Topaloglou, Stavros A. Zenios","doi":"10.1080/14697688.2024.2386323","DOIUrl":"https://doi.org/10.1080/14697688.2024.2386323","url":null,"abstract":"Using stochastic spanning tests without any distributional assumptions on returns, we show that the two classes of GDP-linked bonds, floaters and linkers, are not spanned by a broad benchmark set o...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"19 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188780","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}
Pub Date : 2024-08-29DOI: 10.1080/14697688.2024.2390947
Matteo Michielon, Diogo Franquinho, Alessandro Gentile, Asma Khedher, Peter Spreij
In the article at hand neural networks are used to model liquidity in financial markets, under conic finance settings, in two different contexts. That is, on the one hand this paper illustrates how...
{"title":"Neural network empowered liquidity pricing in a two-price economy under conic finance settings","authors":"Matteo Michielon, Diogo Franquinho, Alessandro Gentile, Asma Khedher, Peter Spreij","doi":"10.1080/14697688.2024.2390947","DOIUrl":"https://doi.org/10.1080/14697688.2024.2390947","url":null,"abstract":"In the article at hand neural networks are used to model liquidity in financial markets, under conic finance settings, in two different contexts. That is, on the one hand this paper illustrates how...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"45 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188778","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}
Pub Date : 2024-08-27DOI: 10.1080/14697688.2024.2388802
Julien Hok, Alex S.L. Tse
FX Open Forward is a derivative instrument where the contract holder has the obligation to purchase a specific amount of foreign currency under a fixed exchange rate by the contract expiry date. In...
{"title":"FX Open Forward","authors":"Julien Hok, Alex S.L. Tse","doi":"10.1080/14697688.2024.2388802","DOIUrl":"https://doi.org/10.1080/14697688.2024.2388802","url":null,"abstract":"FX Open Forward is a derivative instrument where the contract holder has the obligation to purchase a specific amount of foreign currency under a fixed exchange rate by the contract expiry date. In...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"31 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188777","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}
Pub Date : 2024-08-19DOI: 10.1080/14697688.2024.2387821
Giuliano Curatola
This paper examines equilibrium asset prices and leverage in an exchange economy populated with both retail and institutional investors. Institutional investors influence the price of the stocks th...
{"title":"Asset prices when large investors interact strategically","authors":"Giuliano Curatola","doi":"10.1080/14697688.2024.2387821","DOIUrl":"https://doi.org/10.1080/14697688.2024.2387821","url":null,"abstract":"This paper examines equilibrium asset prices and leverage in an exchange economy populated with both retail and institutional investors. Institutional investors influence the price of the stocks th...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"8 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188779","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}
Pub Date : 2024-08-15DOI: 10.1080/14697688.2024.2375260
Tushar Vaidya
Published in Quantitative Finance (Ahead of Print, 2024)
发表于《定量金融》（2024 年提前出版）
{"title":"Quantum Machine Learning and Optimisation in Finance","authors":"Tushar Vaidya","doi":"10.1080/14697688.2024.2375260","DOIUrl":"https://doi.org/10.1080/14697688.2024.2375260","url":null,"abstract":"Published in Quantitative Finance (Ahead of Print, 2024)","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"73 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188781","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}
Pub Date : 2024-08-15DOI: 10.1080/14697688.2024.2384392
Claudia Ceci, Katia Colaneri
We investigate the optimal investment-and-reinsurance problem for insurance company with partial information on the market price of the risk. Through the use of filtering techniques, we convert the...
{"title":"Portfolio and reinsurance optimization under unknown market price of risk","authors":"Claudia Ceci, Katia Colaneri","doi":"10.1080/14697688.2024.2384392","DOIUrl":"https://doi.org/10.1080/14697688.2024.2384392","url":null,"abstract":"We investigate the optimal investment-and-reinsurance problem for insurance company with partial information on the market price of the risk. Through the use of filtering techniques, we convert the...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"64 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267400","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}
Pub Date : 2024-07-25DOI: 10.1080/14697688.2024.2377735
Natalie Packham
Stress testing refers to the application of adverse financial or macroeconomic scenarios to a portfolio. For this purpose, financial or macroeconomic risk factors are linked with asset returns, typ...
{"title":"Risk factor aggregation and stress testing","authors":"Natalie Packham","doi":"10.1080/14697688.2024.2377735","DOIUrl":"https://doi.org/10.1080/14697688.2024.2377735","url":null,"abstract":"Stress testing refers to the application of adverse financial or macroeconomic scenarios to a portfolio. For this purpose, financial or macroeconomic risk factors are linked with asset returns, typ...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"28 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940715","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}
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