Econometrics of Risk by Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta,

By Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta, Komsan Suriya (eds.)

This edited booklet includes a number of cutting-edge papers dedicated to econometrics of threat. a few papers offer theoretical research of the corresponding mathematical, statistical, computational, and comparatively cheap types. different papers describe functions of the unconventional risk-related econometric options to real-life financial occasions. The ebook provides new equipment constructed only in the near past, particularly, tools utilizing non-Gaussian heavy-tailed distributions, tools utilizing non-Gaussian copulas to correctly take into consideration dependence among diversified amounts, equipment taking into consideration vague ("fuzzy") specialist wisdom, and plenty of different leading edge techniques.

This flexible quantity is helping practitioners to profit easy methods to practice new innovations of econometrics of danger, and researchers to additional enhance the present versions and to return up with new principles on easy methods to most sensible have in mind monetary risks.

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The myth that banks are risk-free was disproved by the 2008 credit crisis. The default of what we used to call too big to fail banks, such as Lehman Brothers and Bear Stearns, which defaulted in the 2008 credit crisis, disproved the myth that banks are risk-free (see Gregory [15]). The 2008 credit crisis also exposed the inadequate management of counterparty credit risk. Counterparty credit risk (also known as default risk) between two parties, is the two-sided risk that one of the counterparties will not pay, as obligated on a trade or a transaction between the two parties.

9. The model has not only removed the serial correlation, as shown in Fig. 10, but has also removed the asymmetry due to the bubble (see Fig. 7). The speculative subperiod is just characterized by a rather standard volatility clustering. An additional autoregressive term can be introduced. We consider a noncausal AR(2) model: Fig. 9 Residuals, noncausal AR(1) 32 A. Hencic and C. Gouriéroux Fig. 894 – yt = ψ1 yt+1 + ψ2 yt+2 + et , (5) and estimate it by the approximated maximum likelihood. 084 and the noncausal AR(2) is stationary.

Com/btc-china-the-worlds-largest-bitcoin-trading-platform-7000023316/ (2013) 29. : Rational expectations and the theory of price movements. Econometrica 29, 315–335 (1961) 30. : The exact likelihood function for a mixed autoregressive-moving average process. Biometrika. 61, 423– 426 (1974) 31. : Testing for Multiple Bubbles, DP Cowles Foundation, 1843 (2012) 32. : Explosive behavior in the 1990s Nasdaq: when did exuberance escalate asset values? Int. Econ. Rev. 52, 201–226 (2011) 33. : Gaussian and Non-Gaussian Linear Time Series and Random Fields.

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