Martin's webpage - Publications

Published

M. Magris, A. Iosifidis, “Variational Inference for GARCH-family models”. To appear in: Proceeding of the 4th ACM International Conference on AI in Finance ICAIF’23, 2023.

C.M. Lillelund, M. Magris, and C.F. Pedersen, “Uncertainty Estimation in Deep Bayesian Survival Models”. In: The IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2023. To appear

M. Shabani, M. Magris, G. Tzagakaris, J. Kanniainen, and A. Iosifidis, “Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots”. In: Neural Computing with Applications, 2023: 1-13.

M. Magris, M. Shabani, and A. Iosifidis. “Bayesian bilinear neural network for predicting the mid-price dynamics in limit-order book markets”. Journal of Forecasting, 2023, 1– 22.

M. Magris, M. Shabani, and A. Iosifidis, “Bayesian Learning for Neural Networks: and algorithmic survey”. To appear in Artificial Intelligence Reviews, 2023. Preprint: arxiv.org/abs/2210.14598.

M. Shabani, M. Magris, D.T. Tran, J. Kanniainen, and A. Iosifidis. “Multi-head Temporal Attention Bilinear Network for Limit Order Books”. 30th European Signal Processing Conference (EUSIPCO), 2022, pp.1487-1491.

M. Magris, "Volatility modeling and limit-order book analytics with high-frequency data", (2019). Doctoral dissertation. Available at trepo.tuni.fi/handle/10024/116373.

A. Ntakaris, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis. “Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods”. In Journal of Forecasting 37.8 (2018): 852-866.

M. Magris, M., J. Kim, E. Räsänen, and J. Kanniainen. “Long-range auto-correlations in limit order book markets: Inter-and cross-event analysis”. In IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1-7.

D. T. Tran, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis. “Tensor representation in high-frequency financial data for price change prediction”. In IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1-7.

Under Review

M. Magris, M. Shabani, and A. Iosifidis, “Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning”. Preprint available at: arxiv.org/abs/2205.11568.

M. Magris, M. Shabani and A. Iosifidis, “Exact Manifold Gaussian Variational Bayes”. Preprint available at: arxiv.org/abs/2210.14598.

Abstracts in conferences

1. M. Magris, and A. Iosifidis. “Approximate Bayes factors for unit root testing”. International Association for Applied Econometrics 2021, (2021).

2. M. Magris, P. Bärholm, and J. Kanniainen. “Implied volatility smile dynamics in the presence of jumps”, (2017). Preprint: arxiv.org/abs/1711.02925.

3. J. Kanniainen, M. Magris. “Option market (in)efficiency and implied volatility dynamics after return jumps”. International Association for Applied Econometrics 2021, (2019). Preprint available at: arxiv.org/abs/1810.12200

Technical reports

4. M. Magris. “On the simulation of the Hawkes process via Lambert-W functions”, (2019). Preprint available at: arxiv.org/abs/1907.09162.

5. M. Magris. “A Vine-copula extension for the HAR model", (2019). Preprint available at: arxiv.org/abs/ 907.08522.

6. J. Kanniainen, and M. Magris. “Detecting Intra-Day Jumps in Stock Prices with High-Frequency Option Data”, (2020). Preprint: papers.ssrn.com/sol3/papers.cfm?abstract_id=3727234.