Remarks on a Multivariate Transformation
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This article is published in Annals of Mathematical Statistics.The article was published on 1952-09-01 and is currently open access. It has received 2735 citations till now. The article focuses on the topics: Transformation (function).read more
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A multi-horizon comparison of density forecasts for the S&P 500 using index returns and option prices
TL;DR: In this paper, the authors compare density forecasts of the SP and the ranking criterion is the out-of-sample likelihood of observed index levels, and they find that Mixtures of the real-world and historical densities have higher likelihoods than both components for short forecast horizons.
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Polynomial chaos expansions for dependent random variables
TL;DR: GSO is used to build PCE using a non-intrusive stochastic collocation method, and it is shown that this approach produces PCE which are orders of magnitude more accurate than PCE constructed using mapping or dominating support methods.
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Inverse regression-based uncertainty quantification algorithms for high-dimensional models
Weixuan Li,Guang Lin,Bing Li +2 more
TL;DR: Two inverse regression-based UQ algorithms (IRUQ) are proposed, which use inverse regression to convert the original high-dimensional problem to a low-dimensional one, which is then efficiently solved by building a response surface for the reduced model, for example via the polynomial chaos expansion.
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Dependence structures in Chinese and US financial markets: a time-varying conditional copula approach
TL;DR: In this article, the authors used a time-varying conditional copula approach to model Chinese and US stock markets' dependence structures with other financial markets, and they found that the upper tail dependence is much higher than the lower tail dependence in some short periods.
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Global sensitivity of structural variability by random sampling
TL;DR: An efficient sampling-based algorithm for the estimation of the upper bounds of the total sensitivity indices, adopting a very efficient Monte Carlo procedure, along the points generated from Markov-chains.