{"title":"Deep limit order book forecasting: a microstructural guide.","authors":"Antonio Briola, Silvia Bartolucci, Tomaso Aste","doi":"10.1080/14697688.2025.2522911","DOIUrl":"10.1080/14697688.2025.2522911","url":null,"abstract":"<p><p>We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we release 'LOBFrame', an open-source code base to efficiently process large-scale Limit Order Book data and quantitatively assess state-of-the-art deep learning models' forecasting capabilities. Our results are twofold. We demonstrate that the stocks' microstructural characteristics influence the efficacy of deep learning methods and that their high forecasting power does not necessarily correspond to actionable trading signals. We argue that traditional machine learning metrics fail to adequately assess the quality of forecasts in the Limit Order Book context. As an alternative, we propose an innovative operational framework that evaluates predictions' practicality by focusing on the probability of accurately forecasting complete transactions. This work offers academics and practitioners an avenue to make informed and robust decisions on the application of deep learning techniques, their scope and limitations, effectively exploiting emergent statistical properties of the Limit Order Book.</p>","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":" ","pages":"1-31"},"PeriodicalIF":1.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12315853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144776010","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}
{"title":"Higher order approximation of option prices in Barndorff-Nielsen and Shephard models","authors":"Álvaro Guinea Juliá, Alet Roux","doi":"10.1080/14697688.2024.2394220","DOIUrl":"https://doi.org/10.1080/14697688.2024.2394220","url":null,"abstract":"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 ...","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"190 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269541","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}
Quantitative FinancePub Date : 2024-09-05eCollection Date: 2024-01-01DOI: 10.1080/14697688.2024.2387222
Fernando Moreno-Pino, Stefan Zohren
{"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}
{"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}
Ellie Papavassiliou, Nikolas Topaloglou, Stavros A. Zenios
{"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}
Matteo Michielon, Diogo Franquinho, Alessandro Gentile, Asma Khedher, Peter Spreij
{"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}
{"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}
{"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}
{"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}
{"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}