BAYESIAN MODEL SELECTION VIA FILTERING FOR A CLASS OF MICRO-MOVEMENT MODELS OF ASSET PRICE

DOI10.1142/S0219024905002883zbMath1100.91046OpenAlexW2027158222MaRDI QIDQ4675834

Michael A. Kouritzin, Yong Zeng

Publication date: 6 May 2005

Published in: International Journal of Theoretical and Applied Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219024905002883

zbMATH Keywords

model selectionBayes factorfilteringcounting processMarkov chain approximation methodprice clusteringTransaction data

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Stochastic analysis (60H99)

Related Items

Residual and stratified branching particle filters ⋮ Microstructure models with short-term inertia and stochastic volatility ⋮ Adaptive Meshfree Backward SDE Filter ⋮ EXPLICIT HESTON SOLUTIONS AND STOCHASTIC APPROXIMATION FOR PATH-DEPENDENT OPTION PRICING ⋮ Bayesian Inference via Filtering Equations for Ultrahigh Frequency Data (I): Model and Estimation ⋮ Bayesian Inference via Filtering Equations for Ultrahigh Frequency Data (II): Model Selection ⋮ Stability of the filter with Poisson observations ⋮ Convergence rates for residual branching particle filters ⋮ Risk Minimization for a Filtering Micromovement Model of Asset Price ⋮ Weak convergence for a type of conditional expectation: application to the inference for a class of asset price models ⋮ Bayesian inference via filtering for a class of counting processes: Application to the micromovement of asset price

Cites Work

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• Weak limit theorems for stochastic integrals and stochastic differential equations
• A NONLINEAR FILTERING APPROACH TO VOLATILITY ESTIMATION WITH A VIEW TOWARDS HIGH FREQUENCY DATA
• The Econometrics of Ultra-high-frequency Data
• A Partially Observed Model for Micromovement of Asset Prices with Bayes Estimation via Filtering
• Estimating Stochastic Volatility via Filtering for the Micromovement of Asset Prices
• A nonlinear autoregressive conditional duration model with applications to financial transaction data

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