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[42150] Artykuł:

A New Approach to the Construction of the APF Algorithm by Applying the Pearson Curves Technique

Czasopismo: Applied Mathematics & Information Sciences   Tom: 10, Zeszyt: 3, Strony: 1-8
ISSN:  1935-0090
Opublikowano: Czerwiec 2016
Liczba arkuszy wydawniczych:  0.50
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Procent
udziału		 Liczba
punktów
Antoni Leon Dawidowicz50.00  
Katarzyna Brzozowska-Rup orcid logoWZiMKKatedra Ekonomii i Finansów*505.00  

Grupa MNiSW:  Recenzowana publikacja w języku innym niż polski w zagranicznym czasopiśmie spoza listy
Punkty MNiSW: 5

Keywords:

Sequential Monte Carlo methods  state-space models  stochastic volatility process SV  Pearsons curves technique 


Abstract:

We consider the theoretical question concerning time series which arises when the distribution of the observed variable is in fact a conditional distribution. The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However, when state or measurement, or both, are highly non-linear, and posterior probability distribution of the state is non-Gaussian, the optimal linear filter and its modifications do not provide satisfactory results. The Sequential Monte Carlo method (SMC) have become one of the familiar tools that allowed the Bayesian paradigm to be applied to approximation of sophisticated models. In this paper we propose a novel construction of an auxiliary particle filter (APF) algorithm using the Pearson curves technique (PC) for approximation of importance weights of simulated particles. The effectiveness of the method is discussed and illustrated by numerical results based on the simulated stochastic volatility process SV.


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