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   In: MPRA Paper.
   RePEc:pra:mprapa:122528.

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2. Default dependence in the insurance and banking sectors: A copula approach. (2024). Zhao, Yang ; Zhang, Xuan ; Kim, Minjoo ; Yan, Cheng.
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   RePEc:eee:intfin:v:91:y:2024:i:c:s1042443123001798.

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3. Bellman filtering and smoothing for state–space models. (2024). Lange, Rutger-Jan.
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4. Review of Statistical Approaches for Modeling High-Frequency Trading Data. (2023). Basu, Sumanta ; Ravishanker, Nalini ; Karpman, Kara ; Dutta, Chiranjit.
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5. The Impact of News Related Covid-19 on Exchange Rate Volatility:A New Evidence From Generalized Autoregressive Score Model. (2023). Erer, Deniz.
   In: EKOIST Journal of Econometrics and Statistics.
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6. High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model. (2022). Li, Zhicheng ; Xing, Haipeng.
   In: Mathematics.
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7. Bellman filtering for state-space models. (2021). Lange, Rutger-Jan.
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9. Integer-valued stochastic volatility. (2019). Dimitrakopoulos, Stefanos ; Touche, Nassim ; Aknouche, Abdelhakim.
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10. Does a bank levy increase frictions on the interbank market?. (2018). Mielus, Piotr ; Snarska, Malgorzata ; Skorulska, Karolina ; Hryckiewicz, Aneta.
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11. Estimation of Dynamic Mixed Hitting Time Model Using Characteristic Function Based Moments. (2018). Ekaputra, Irwan ; Husodo, Zaafri Ananto ; Purwono, Yogo.
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12. Patent propensity, R&D and market competition: Dynamic spillovers of innovation leaders and followers. (2016). Escribano, Alvaro ; Blazsek, Szabolcs.
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14. Forecasting Interest Rates Using Geostatistical Techniques. (2015). Di Marcantonio, Michele ; arbia, giuseppe.
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17. “Every move you make, every step you take, I’ll be watching you” – the quest for hidden orders in the interbank FX spot market. (2014). Bień-Barkowska, Katarzyna.
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21. Who moves first? An intensity-based measure for information flows across stock exchanges. (2013). Kehrle, Kerstin ; Peter, Franziska J..
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23. Monte Carlo likelihood inference for marked doubly stochastic Poisson processes with intensity driven by marked point processes. (2012). Minozzo, Marco ; Centanni, Silvia.
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24. Where does Information Processing in a Fragmented Market Take Place? – Evidence from the Swiss Stock Market after MiFID. (2012). von Wyss, Rico ; Kohler, Alexander.
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25. Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models. (2012). Lucas, Andre ; Koopman, Siem Jan ; Scharth, Marcel.
    In: Tinbergen Institute Discussion Papers.
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27. On the intraday periodicity duration adjustment of high-frequency data. (2012). Wu, Zhengxiao.
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29. Extreme value models in a conditional duration intensity framework. (2011). Herrera, Rodrigo ; Schipp, Bernhard.
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30. Extreme value models in a conditional duration intensity framework. (2011). Herrera, Rodrigo ; Schipp, Bernhard.
    In: SFB 649 Discussion Papers.
    RePEc:hum:wpaper:sfb649dp2011-022.

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31. Shrinkage estimation of semiparametric multiplicative error models. (2011). Gallo, Giampiero ; Brownlees, Christian.
    In: International Journal of Forecasting.
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32. Shrinkage estimation of semiparametric multiplicative error models. (2011). Gallo, Giampiero ; Brownlees, Christian.
    In: International Journal of Forecasting.
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34. Knowledge spillovers in U.S. patents: A dynamic patent intensity model with secret common innovation factors. (2010). Escribano, Alvaro ; Blazsek, Szabolcs.
    In: Post-Print.
    RePEc:hal:journl:hal-00732533.

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35. Knowledge spillovers in US patents: A dynamic patent intensity model with secret common innovation factors. (2010). Escribano, Alvaro ; Blazsek, Szabolcs.
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36. Nonparametric density estimation for positive time series. (2010). Rombouts, Jeroen ; Bouezmarni, Taoufik ; Rombouts, Jeroen V. K., .
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37. Analysis of ultra-high-frequency financial data using advanced Fourier transforms. (2009). Ng, Wing Lon ; Constantinou, Nick ; Giampaoli, Iacopo .
    In: Finance Research Letters.
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38. Efficient importance sampling for ML estimation of SCD models. (2009). Galli, Fausto ; Bauwens, Luc.
    In: Computational Statistics & Data Analysis.
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39. Knowledge spillovers in U.S. patents: a dynamic patent intensity model with secret common innovation factors. (2009). Escribano, Alvaro ; Blazsek, Szabolcs.
    In: UC3M Working papers. Economics.
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40. A General Framework for Observation Driven Time-Varying Parameter Models. (2008). Lucas, Andre ; Koopman, Siem Jan ; Creal, Drew ; André Lucas, .
    In: Tinbergen Institute Discussion Papers.
    RePEc:tin:wpaper:20080108.

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41. Parameter Driven Multi-state Duration Models: Simulated vs. Approximate Maximum Likelihood Estimation. (2008). André A. Monteiro, .
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    RePEc:tin:wpaper:20080021.

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42. Sequential numerical integration in nonlinear state space models for microeconometric panel data. (2008). Heiss, Florian.
    In: Journal of Applied Econometrics.
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43. Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model. (2008). Hautsch, Nikolaus.
    In: Journal of Economic Dynamics and Control.
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44. Improving MCMC, using efficient importance sampling. (2008). Richard, Jean-Francois ; Liesenfeld, Roman.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:53:y:2008:i:2:p:272-288.

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45. Panel intensity models with latent factors: An application to the trading dynamics on the foreign exchange market. (2007). Voev, Valeri ; Nolte, Ingmar.
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46. Modelling security market events in continuous time: Intensity based, multivariate point process models. (2007). Bowsher, Clive.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:141:y:2007:i:2:p:876-912.

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47. Efficient high-dimensional importance sampling. (2007). Richard, Jean-Francois ; Zhang, Wei.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:141:y:2007:i:2:p:1385-1411.

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48. Modelling Financial High Frequency Data Using Point Processes. (2006). Hautsch, Nikolaus ; Bauwens, Luc ; Nikolaus, HAUTSCH ; Luc, Bauwens.
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49. Modelling financial high frequency data using point processes. (2006). Hautsch, Nikolaus ; Bauwens, Luc.
    In: LIDAM Discussion Papers CORE.
    RePEc:cor:louvco:2006080.

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