#### Filter Results:

- Full text PDF available (8)

#### Publication Year

2007

2017

- This year (3)
- Last 5 years (7)
- Last 10 years (12)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Luca Martino, H. Yang, David Luengo, Juho Kanniainen, Jukka Corander
- Digital Signal Processing
- 2015

Bayesian inference often requires efficient numerical approximation algorithms, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods. The Gibbs sampler is a well-known MCMC technique, widely applied in many signal processing problems. Drawing samples from univariate full-conditional distributions efficiently is essential for the… (More)

- Juho Kanniainen, Saku J. Mäkinen, Robert Piché, Alok Chakrabarti
- IEEE Trans. Engineering Management
- 2011

Presently, managing prediction of metrics in high frequency financial markets is a challenging task. An efficient way to do it is by monitoring the dynamics of a limit order book and try to identify the information edge. This paper describes a new benchmark dataset of high-frequency limit order markets for mid-price prediction. We make publicly available… (More)

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based… (More)

- Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
- 2017 IEEE 19th Conference on Business Informatics…
- 2017

In today's financial markets, where most trades are performed in their entirety by electronic means and the largest fraction of them is completely automated, an opportunity has risen from analyzing this vast amount of transactions. Since all the transactions are recorded in great detail, investors can analyze all the generated data and detect repeated… (More)

- Juho Kanniainen
- Math. Meth. of OR
- 2009

Gibbs sampling is a well-known Markov Chain Monte Carlo (MCMC) technique, widely applied to draw samples from multivariate target distributions which appear often in many different fields (machine learning, finance, signal processing, etc.). The application of the Gibbs sampler requires being able to draw efficiently from the univariate full-conditional… (More)

- Juliane Müller, Juho Kanniainen, Robert Piché
- Applied Mathematics and Computation
- 2013

This paper investigates a global optimization algorithm for the calibration of stochastic volatility models. Two GARCH models are considered, namely the Leverage and the Heston-Nandi model. Empirical information on option prices is used to minimize a loss function that reflects the option pricing error. It is shown that commonly used gradient based… (More)

- Robert Piché, Juho Kanniainen
- World Congress on Engineering
- 2007

This paper illustrates the use of the differentiation matrix technique for solving differential equations in finance. The technique provides a compact and unified formulation for a variety of discretisation and time-stepping algorithms for solving problems in one and two dimensions. Using differentiation matrix models, we compare time-stepping algorithms… (More)

- Robert Piché, Juho Kanniainen
- IJMNO
- 2009

Differentiation matrices provide a compact and unified formulation for a variety of differential equation discretisation and timestepping algorithms. This paper illustrates their use for solving three differential equations of finance: the classic Black-Scholes equation (linear initial-boundary value problem), an American option pricing problem (linear… (More)