ANALYZING MORTALITY BOND INDEXES VIA HIERARCHICAL FORECAST RECONCILIATION

DOI10.1017/asb.2019.19zbMath1427.91236OpenAlexW2953884533WikidataQ127595598 ScholiaQ127595598MaRDI QIDQ4972126

Han Li, Qi-he Tang

Publication date: 22 November 2019

Published in: ASTIN Bulletin (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/asb.2019.19

zbMATH Keywords

modelingmortalityprobabilistic forecastKortis bondtime-series modelsforecast reconciliation

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Actuarial mathematics (91G05)

Related Items (4)

Pricing extreme mortality risk in the wake of the COVID-19 pandemic ⋮ Dependence bounds for the difference of stop-loss payoffs on the difference of two random variables ⋮ Assessing mortality inequality in the U.S.: what can be said about the future? ⋮ Longevity risk and capital markets: the 2019--20 update

Uses Software

• forecast
• Forecast
• Rugarch
• hts

Cites Work

• Optimal combination forecasts for hierarchical time series
• Supply chain forecasting: theory, practice, their gap and the future
• Modelling longevity bonds: analysing the Swiss Re Kortis bond
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• Securitization of catastrophe mortality risks
• Age-specific copula-AR-GARCH mortality models
• Grouped multivariate and functional time series forecasting: an application to annuity pricing
• A comparison of the Lee-Carter model and AR-ARCH model for forecasting mortality rates
• Pricing Death: Frameworks for the Valuation and Securitization of Mortality Risk
• Aggregation and Proration in Forecasting
• MODELING LONGEVITY RISK WITH GENERALIZED DYNAMIC FACTOR MODELS AND VINE-COPULAE
• Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization
• HEDGING MORTALITY CLAIMS WITH LONGEVITY BONDS
• ANALYZING AND PREDICTING CAT BOND PREMIUMS: A FINANCIAL LOSS PREMIUM PRINCIPLE AND EXTREME VALUE MODELING
• A new look at the statistical model identification

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