A DCC-GARCH multi-population mortality model and its applications to pricing catastrophic mortality bonds

Wang, Zihe; Li, Johnny Siu-Hang

doi:10.1016/j.frl.2015.10.004

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…The next step towards building the multi-population mortality model is to select a marginal model for each of the period component series. Since several researchers, including Chen et al [2015], Chen et al [2017], Lin et al [2015] and Wang and Li. [2016], detect volatility heteroscedasticity in period components, we consider an ARIMA(p, d, q)-GARCH(P, Q) model for each estimated {κ (i) t } series.…”

Section: Marginal Model For the Estimated Period Componentsmentioning

confidence: 99%

Modelling mortality dependence: An application of dynamic vine copula

Zhou

2021

Insurance: Mathematics and Economics

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Section: Marginal Model For the Estimated Period Componentsmentioning

confidence: 99%

Modelling mortality dependence: An application of dynamic vine copula

Zhou

2021

Insurance: Mathematics and Economics

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“…• Design and pricing of longevity bonds and other longevity-linked products (e.g. Blake et al, 2006aBlake et al, , 2006bBlake et al, , 2014Bauer, 2006;Bauer & Ruß, 2006;Antolin & Bloomestein, 2007;Bauer & Kramer, 2007Barbarin, 2008;Bauer et al, 2010b;Chen & Cummins, 2010;Kogure & Kurachi, 2010;Bravo, 2011;Dowd et al, 2011a;Mayhew & Smith, 2011;Zhou et al, 2011Zhou et al, , 2013Chen et al, 2013;Shen & Siu, 2013;Denuit et al, 2015;Hunt & Blake, 2015;Milevsky & Salisbury, 2015;Yang et al, 2015;Wang & Li, 2016;Chen et al, 2017;Lin et al, 2017b ;Leung et al, 2018;MacMinn & Richter, 2018;. • Design and pricing of longevity-linked derivatives (e.g.…”

Section: • Landg Executed Buy-ins With the Pearson Pension Plan (£500m mentioning

confidence: 99%

Longevity risk and capital markets: the 2018–19 update

Blake

Cairns²

2020

Ann. actuar. sci.

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“…In Eqn. (10), R t is the dynamic conditional correlation matrix of the sequence established by DCC-GARCH model [38][39].…”

Section: The T-copula Dcc-garch Modelmentioning

confidence: 99%

Dynamic correlation and volatility spillover between the stock markets of Shenzhen and Hong Kong

Rao

Meng

2020

Scientia Iranica

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Considering the two-way spillovers of market information, this paper establishes multivariate GARCH models to study the impact of Shenzhen-Hong Kong Stock Connect (SHSC) on the complex co-movements relation between the stock markets of Shenzhen and Hong Kong from the aspects of dynamic correlation and volatility spillover. On the one hand, a t-Copula DCC-GARCH model which combines the Copula function with the DCC-GARCH model is established to model the return rate series of stock index in different stages, and the characteristic that the dynamic correlation coefficient changes with time is analyzed emphatically. On the other hand, a BEKK-GARCH model is established to measure the changes of the volatility spillover effect between the stock markets in Shenzhen and Hong Kong. The results show that the opening of SHSC has gradually increased the dynamic correlation coefficient of the two stock markets, and the openness degree of the two stock markets has increased. At the same time, the volatility spillovers of stock markets in Shenzhen and Hong Kong have shifted from one-way spillover to two-way spillovers, which indicates that the SHSC mechanism has strengthened the correlation degree and has improved the ability of risk spillover in the two stock markets.

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A DCC-GARCH multi-population mortality model and its applications to pricing catastrophic mortality bonds

Cited by 12 publications

References 16 publications

Modelling mortality dependence: An application of dynamic vine copula

Modelling mortality dependence: An application of dynamic vine copula

Longevity risk and capital markets: the 2018–19 update

Dynamic correlation and volatility spillover between the stock markets of Shenzhen and Hong Kong

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