Conditional density and value‐at‐risk prediction of Asian currency exchange rates

By Stefan Mittnik, Marc S. Paolella. 2000

194 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/7792605999f3706669bfba52d7145e9a743817ca

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Citations in the corpus

MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model (2004, methodology): “…in option pricing, Chunhachinda et al. (1997) show that the introduction of skewness affects significantly the construction of the optimal portfolio, Mittnik and Paolella (2000) argue that skewness and heavy tails should be taken into account explicitly in Value-at-Risk forecasts, Peiró (1999)…”
A generalized class of skew distributions and associated robust quantile regression models (2014, background): “…including versions of skew normal, skew Student’s-t, and skew Laplace; see Azzalini (2008) for a review of skew normal and related distributions; Mittnik & Paolella (2000), Li, Villani, & Kohn (2010), and Zhu & Galbraith (2010) for some skew Student’s-t distributions; Arslan & Genç (2009) for…”
A New Class of Multivariate Skew Densities, with Application to GARCH Models (2002, background): “Note that, since the distribution assumption is crucial to obtain accurate VaR forecast in a univariate framework (see Mittnik and Paolella 2000 or Giot and Laurent 2003 among others), one can conjecture this will also be true in the multivariate case, which calls for the use of a flexible density…”
A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models (2005, background): “Note that because the distribution assumption is crucial to obtaining accurate VaR forecasts in a univariate framework (see Mittnik and Paolella 2000; Giot and Laurent 2003, among others), one can conjecture that this will also be true in the multivariate case, which calls for the use of a flexible…”
Value-at-Risk for long and short trading positions (2003, methodology): “…to model left and right tail VaRs with a single parametric method for a wide range of values for α. 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. and Paolella, 2000, focus on long VaR only).”
Market risk in commodity markets: a VaR approach (2003, methodology): “Indeed, for daily stock returns, Giot and Laurent (2002) and Mittnik and Paolella (2000) show that a normal APARCH model delivers poor (in-sample and out-of-sample) VaR forecasts and that accounting for the observed skewness and kurtosis is crucial. Therefore, we propose to use an adaptive method to”
A comprehensive review of Value at Risk methodologies (2014, background): “Laurent (2004) and Mittnik and Paolella (2000) among others.”
Mixed Normal Conditional Heteroskedasticity (2004, methodology): “This was done using the noncentral t distribution, as suggested for use in a financial modeling context by Harvey and Siddique (1999); the asymmetric generalized t distribution in Mittnik and Paolella (2000); and the stable Paretian distribution (see, e.g., Mittnik, Paolella and Rachev, 2000).”
Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions (2003, unknown): “The latter restraint can actually be overcome if more general Student’s t–like distributions are used (Paolella, 1999; Mittnik and Paolella, 2000), but these suggestions, while often providing admirable in– and out–of–sample fits, do not possess the theoretical property of summability, common only…”
Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, unknown): “GARCH-type models coupled with the skewed t distribution have been frequently found to-deliver excellent forecast results; see, for example, Mittnik and Paolella (2000), Giot and Laurent (2004), and the references therein.”
Stable distributions in the Black–Litterman approach to asset allocation (2007, background): “[8], Rachev and Mittnik [28] and the references therein, Mittnick and Paolella [22], Panorska et al.”