Juho Kanniainen

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• 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)
• 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)
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)
• 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)
• 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)
• 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)