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Finite Sample Optimality of Score-Driven Volatility Models
Blasques, Francisco; Lucas, André; van Vlodrop, Andries - 2017
We study optimality properties in finite samples for time-varying volatility models driven by the score of the …-driven volatility models have optimality properties when they matter most. Score-driven models perform best when the data is fat … predictive likelihood function. Available optimality results for this class of models suffer from two drawbacks. First, they are …
Persistent link: https://www.econbiz.de/10011819504
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Finite Sample Optimality of Score-Driven Volatility Models
Blasques, Francisco - 2017
We study optimality properties in finite samples for time-varying volatility models driven by the score of the …-driven volatility models have optimality properties when they matter most. Score-driven models perform best when the data is fat … predictive likelihood function. Available optimality results for this class of models suffer from two drawbacks. First, they are …
Persistent link: https://www.econbiz.de/10012942866
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The dynamic Skellam model with applications
Koopman, Siem Jan; Lit, Rutger; Lucas, André - 2014
extension of numerically accelerated importance sampling techniques. We illustrate the new model by two empirical studies and …
Persistent link: https://www.econbiz.de/10010253460
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Credible Granger-causality inference with modest sample lengths : a cross-sample validation approach
Ashley, Richard A.; Tsang, Kwok Ping - In: Econometrics : open access journal 2 (2014) 1, pp. 72-91
Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an "in-sample"...
Persistent link: https://www.econbiz.de/10010336194
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Time Series Forecasting With a Learning Algorithm : An Approximate Dynamic Programming Approach
Collado, Ricardo - 2019
dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers …
Persistent link: https://www.econbiz.de/10012894079
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Olonite Observation Extract (OOEX)
Olonite, Oluyemi Ayodele - 2022
Many researchers have only focused on a single way of collecting data when other methods are available. This study proposes a template for researchers who intend to use the qualitative means in extracting data for analysis. The template shows the Olonite Observation Extract Template (OOEXT)
Persistent link: https://www.econbiz.de/10014081026
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A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series
Webel, Karsten - 2022
Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal...
Persistent link: https://www.econbiz.de/10013336397
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Towards seasonal adjustment of infra-monthly time series with JDemetra+
Webel, Karsten; Smyk, Anna - 2023
Infra-monthly economic time series have become increasingly popular in official statistics in recent years. This evolution has been largely fostered by official statistics’ digital transformation during the last decade. The COVID-19 pandemic outbreak in 2020 has added fuel to the fire as many...
Persistent link: https://www.econbiz.de/10014336194
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Empirical evidences on the interconnectedness between sampling and asset returns' distributions
Orlando, Guiseppe; Bufalo, Michele - In: Risks : open access journal 9 (2021) 5, pp. 1-35
The aim of this work was to test how returns are distributed across multiple asset classes, markets and sampling …. Finally, we show how sampling and distribution of returns are strictly connected. This is of great importance as, for example …
Persistent link: https://www.econbiz.de/10012596311
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Partial sample regressions
Czasonis, Megan; Kritzman, Mark; Turkington, David - 2019 - This version: November 13, 2019
Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant...
Persistent link: https://www.econbiz.de/10012225139