Prof Juho Kanniainen's pages  Juho Kanniainen is Professor of Financial Engineering at the Department of Computing Sciences in Tampere University (previously in at the laboratory of Information and Industrial Management in Tampere University of Technology). Juho's research agenda is focused on statistical computing and data science in finance and risk management. He has published on derivative pricing, financial econometrics, order book dynamics and liquidity, and financial networks. In his research, he is not only using traditional stochastic calculus, but also modern data science approaches, namely complex networks techniques and machine learning methods with rich data sets. Dr. Kanniainen has published in prestigious journals in Finance and IT, including Review of Finance, Journal of Banking and Finance, and IEEE Transactions on Neural Networks and Learning Systems. He has been coordinating two international EU projects, BigDataFinance and HPCFinance. Recent Highlights  Trading Too Expensively in the FX Market? To appear in Quantitative Finance With Milla Siikanen and Ulrich Nögel "We empirically show that, on average, traders obtain a relatively tight spread with four or five streams; the use of more streams yields a marginal benefit only. Moreover, most of the traders could—at least in theory—reduce the average spread by more than half with the optimal combination of streams, and a trader could save significantly, even up to 0.18 basis points in dollars per 1EUR traded." [read more]  Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis IEEE Transactions on Neural Networks and Learning Systems with Dat Thanh Tran, Alexandros Iosifidis, Moncef Gabbouj "Superior neural network layer architecture for predicting mid-price movements with limit order book data" [read more at IEEE or arXiv]  New book! High-Performance Computing in Finance: Problems, Methods, and Solutions Chapman and Hall/CRC Financial Mathematics Series Edited by M. Dempster, J. Kanniainen, J. Keane, and E. Vynckier Finance Research Letters with Milla Siikanen, Kęstutis Baltakys, Ravi Vatrapub, Raghava Mukkamala, and Abid Hussain  Jump and Volatility Dynamics for the S&P 500: Evidence for Infinite-Activity Jumps with Non-Affine Volatility Dynamics from Stock and Option Markets Review of Finance with Hanxue Yang "Use infinite-activity jumps with non-affine volatility dynamics to price options accurately" [Read more]  Limit Order Books and Liquidity around Scheduled and Non-Scheduled Announcements: Empirical Evidence from NASDAQ Nordic Finance Research Letterswith Milla Siikanen and Jaakko Valli  Estimating and Using GARCH Models with VIX Data for Option Valuation Journal of Banking and Finance with Binghuan Lin and Hanxue Yang "Use VIX to improve the performance of option pricing models" [Read more]  A Fast Universal Self-tuned Sampler within Gibbs sampling Digital Signal Processing with L Martino, H Yang, D Luengo and J Corander Project Highlights:  BigDataFinance EU project: New quantitative and econometric methods for empirical finance and risk management with large and complex datasets by exploiting big data techniques  HPCFinance EU project: At the fertile crossroads of Financial Engineering and High Performance Computing providing robust solutions to managing financial risks |