- Prof. Victor Leiva – Facultad de Ingenieria Y Ciencias – Universidad Adolfo Ibáñez – Chile
- FCUL (DEIO) – Campo Grande – Bloco C6 Piso 4 – Sala: 6.4.30 – 14:30h
- Quarta-feira, 3 de Dezembro de 2014
- Referência Projeto: Pest-OE/MAT/UI0006/2014
The Birnbaum-Saunders distribution is receiving considerable attention due to its good properties. One of its extensions is the class of scale-mixture Birnbaum-Saunders (SBS) distributions, which shares its good properties, but it also has further properties. The autoregressive conditional duration models are the primary family used for analyzing high-frequency financial data. We propose a methodology based on SBS autoregressive conditional duration models, which includes in-sample inference, goodness-of-fit and out-of-sample forecast techniques. We carry out a Monte Carlo study to evaluate its performance and assess its practical usefulness with real-world data of financial transactions from the New York stock exchange.