Value-at-Risk: a multivariate switching regime approach
By Monica Billio, Loriana Pelizzon. 2000
184 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/589da6c3cf77de91f6c4e8a0439e298557215a5c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Stavros Degiannakis *
- P. Giot
- Sébastien-Yves Laurent
- Juri Marcucci
- Mila Getmansky
- Mingchih Lee
- Hung‐Chun Liu
- Jui-Cheng Hung
In Finance
- Stavros Degiannakis *
- P. Giot
- Sébastien-Yves Laurent
- Juri Marcucci
- Mila Getmansky
- Mingchih Lee
- Hung‐Chun Liu
- Jui-Cheng Hung
Citations in the corpus
- The Use of GARCH Models in VaR Estimation (2004, background): “Billio and Pelizzon [3] introduced a multivariate switching regime model in order to calculate the VaR for 10 Italian stocks and for several portfolios that have been generated by them.”
- Forecasting Stock Market Volatility with Regime-Switching GARCH Models (2005, methodology): “So far in the literature, many volatility models have been put forward, but those that seem to be the most successful are the GARCH (Generalized Autoregressive Conditional Heterskedasticity) models by Bollerslev (1986), who generalizes the seminal idea on ARCH by Engle (1982), and their numerous gen”
- Value-at-Risk for long and short trading positions (2003, methodology): “5See also Billio and Pelizzon (2000) for an application of a multivariate switching regime model to the computation of the VaR of a portfolio of stocks.”
- A comprehensive review of Value at Risk methodologies (2014, methodology): “However other papers, such as those by Angelidis et al. (2007), Guermat and Harris (2002), Billio and Pelizzon (2000), and Angelidis and Benos (2004), report that the t-Student distribution overestimates the proportion of exceptions.”
- Estimation of value-at-risk for energy commodities via fat-tailed GARCH models (2008, methodology): “To explore the possible model specifications to achieve accurate VaR measurement, Billio and Pelizon (2000) found that the model-based VaR estimated by a multivariate regime switching model is more accurate than the RiskMetrics and GARCH-t(1,1) models. The traditional ARCH model proposed by Engle (1”
- Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors (2012, methodology): “…with stationary transition probabilities, summarized by 11 Markov-switching models have been used to investigate systemic by Chan, Getmansky, Haas, and Lo (2006) and to measure value-atby Billio and Pelizzon (2000). ease cite this article as: Billio, M., et al., Econometric measur surance sectors.”