Particle filters and Bayesian inference in financial econometrics
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- Haakon Kavli & Kevin Kotzé, 2014. "Spillovers in Exchange Rates and the Effects of Global Shocks on Emerging Market Currencies," South African Journal of Economics, Economic Society of South Africa, vol. 82(2), pages 209-238, June.
- repec:eee:csdana:v:114:y:2017:i:c:p:38-46 is not listed on IDEAS
- Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
- Galeano San Miguel, Pedro & Ausín Olivera, María Concepción & Virbickaite, Audrone & Lopes, Hedibert F., 2014. "Particle learning for Bayesian non-parametric Markov Switching Stochastic Volatility model," DES - Working Papers. Statistics and Econometrics. WS ws142819, Universidad Carlos III de Madrid. Departamento de Estadística.
- Gordy, Michael B. & Szerszen, Pawel J., 2015. "Bayesian Estimation of Time-Changed Default Intensity Models," Finance and Economics Discussion Series 2015-2, Board of Governors of the Federal Reserve System (U.S.).
- Nuno Silva, 2015. "Industry based equity premium forecasts," GEMF Working Papers 2015-19, GEMF, Faculty of Economics, University of Coimbra.
- Jondeau, Eric & Lahaye, Jérôme & Rockinger, Michael, 2015. "Estimating the price impact of trades in a high-frequency microstructure model with jumps," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 205-224.
- Paul Gaskell & Frank McGroarty & Thanassis Tiropanis, 2014. "Signal Diffusion Mapping: Optimal Forecasting with Time Varying Lags," Papers 1409.6443, arXiv.org.
- Michele Bianchi & Frank Fabozzi, 2015. "Investigating the Performance of Non-Gaussian Stochastic Intensity Models in the Calibration of Credit Default Swap Spreads," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 243-273, August.
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Keywords
particle learning ; sequential Monte Carlo ; Markov chain Monte Carlo ; stochastic volatility ; realized volatility ; Nelson–Siegel model ;Statistics
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