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 : 2024-01-10DOI: 10.1080/14697688.2023.2281529
Marlon Fritz, Thomas Gries, Lukas Wiechers
We propose an indicator for detecting anomalous stock market valuation in real time such that market participants receive timely signals so as to be able to take stabilizing action. Unlike existing...
{"title":"An early indicator for anomalous stock market performance","authors":"Marlon Fritz, Thomas Gries, Lukas Wiechers","doi":"10.1080/14697688.2023.2281529","DOIUrl":"https://doi.org/10.1080/14697688.2023.2281529","url":null,"abstract":"We propose an indicator for detecting anomalous stock market valuation in real time such that market participants receive timely signals so as to be able to take stabilizing action. Unlike existing...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"52 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411865","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 : 2024-01-09DOI: 10.1080/14697688.2023.2294799
Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, Youngjoon Hong
Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The dynamics of the volatility surface...
{"title":"Physics-informed convolutional transformer for predicting volatility surface","authors":"Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, Youngjoon Hong","doi":"10.1080/14697688.2023.2294799","DOIUrl":"https://doi.org/10.1080/14697688.2023.2294799","url":null,"abstract":"Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The dynamics of the volatility surface...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"69 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411830","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 : 2024-01-04DOI: 10.1080/14697688.2023.2278502
Matteo Pelagatti, Giacomo Sbrana
The paper introduces a semiparametric estimator of the correlations among elliptically distributed random variables invariant to any form of heteroscedasticity, robust to outliers, and asymptotical...
{"title":"Estimating correlations among elliptically distributed random variables under any form of heteroskedasticity","authors":"Matteo Pelagatti, Giacomo Sbrana","doi":"10.1080/14697688.2023.2278502","DOIUrl":"https://doi.org/10.1080/14697688.2023.2278502","url":null,"abstract":"The paper introduces a semiparametric estimator of the correlations among elliptically distributed random variables invariant to any form of heteroscedasticity, robust to outliers, and asymptotical...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"11 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139374094","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 : 2024-01-04DOI: 10.1080/14697688.2023.2286351
Álvaro Arroyo, Álvaro Cartea, Fernando Moreno-Pino, Stefan Zohren
One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Esse...
{"title":"Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers","authors":"Álvaro Arroyo, Álvaro Cartea, Fernando Moreno-Pino, Stefan Zohren","doi":"10.1080/14697688.2023.2286351","DOIUrl":"https://doi.org/10.1080/14697688.2023.2286351","url":null,"abstract":"One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Esse...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"22 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139373991","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 : 2023-12-19DOI: 10.1080/14697688.2023.2287134
Sini Guo, Jia-Wen Gu, Wai-Ki Ching, Benmeng Lyu
Online portfolio selection is attracting increasing attention in both artificial intelligence and finance communities due to its efficiency and practicability in deriving optimal investment strateg...
在线投资组合选择因其在推导最佳投资策略方面的高效性和实用性而日益受到人工智能和金融界的关注。
{"title":"Adaptive online mean-variance portfolio selection with transaction costs","authors":"Sini Guo, Jia-Wen Gu, Wai-Ki Ching, Benmeng Lyu","doi":"10.1080/14697688.2023.2287134","DOIUrl":"https://doi.org/10.1080/14697688.2023.2287134","url":null,"abstract":"Online portfolio selection is attracting increasing attention in both artificial intelligence and finance communities due to its efficiency and practicability in deriving optimal investment strateg...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"33 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138821087","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 : 2023-12-19DOI: 10.1080/14697688.2023.2282657
Tao Chen, Mike Ludkovski, Moritz Voß
We investigate optimal order execution problems in discrete time with instantaneous price impact and stochastic resilience. First, in the setting of linear transient price impact we derive a closed...
{"title":"On parametric optimal execution and machine learning surrogates","authors":"Tao Chen, Mike Ludkovski, Moritz Voß","doi":"10.1080/14697688.2023.2282657","DOIUrl":"https://doi.org/10.1080/14697688.2023.2282657","url":null,"abstract":"We investigate optimal order execution problems in discrete time with instantaneous price impact and stochastic resilience. First, in the setting of linear transient price impact we derive a closed...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"87 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139028442","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 : 2023-12-19DOI: 10.1080/14697688.2023.2288871
Andreas Celary, Zehra Eksi-Altay, Paul Krühner
We consider an HJM model setting for Markov-chain modulated forward rates. The underlying Markov chain is assumed to induce regime switches on the forward curve dynamics. Our primary focus is on th...
{"title":"Regime-switching affine term structures","authors":"Andreas Celary, Zehra Eksi-Altay, Paul Krühner","doi":"10.1080/14697688.2023.2288871","DOIUrl":"https://doi.org/10.1080/14697688.2023.2288871","url":null,"abstract":"We consider an HJM model setting for Markov-chain modulated forward rates. The underlying Markov chain is assumed to induce regime switches on the forward curve dynamics. Our primary focus is on th...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139028674","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 : 2023-12-03DOI: 10.1080/14697688.2023.2273414
O. Bonesini, G. Callegaro, A. Jacquier
We develop a product functional quantization of rough volatility. Since the optimal quantizers can be computed offline, this new technique, built on the insightful works by [Luschgy, H. and Pagès, ...
{"title":"Functional quantization of rough volatility and applications to volatility derivatives","authors":"O. Bonesini, G. Callegaro, A. Jacquier","doi":"10.1080/14697688.2023.2273414","DOIUrl":"https://doi.org/10.1080/14697688.2023.2273414","url":null,"abstract":"We develop a product functional quantization of rough volatility. Since the optimal quantizers can be computed offline, this new technique, built on the insightful works by [Luschgy, H. and Pagès, ...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"23 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531654","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 : 2023-12-01DOI: 10.1080/14697688.2023.2283200
Teguh Ahmad Asparill, Rossy Lambelanova, Andi Pitono
Published in Quantitative Finance (Ahead of Print, 2023)
发表于《定量金融》(2023年出版前)
{"title":"The Politics of Financial Control: The Role of the House of Commons","authors":"Teguh Ahmad Asparill, Rossy Lambelanova, Andi Pitono","doi":"10.1080/14697688.2023.2283200","DOIUrl":"https://doi.org/10.1080/14697688.2023.2283200","url":null,"abstract":"Published in Quantitative Finance (Ahead of Print, 2023)","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"163 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531658","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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