Multi-population mortality models: A factor copula approach

Chen, Hua; MacMinn, Richard D.; Sun, Tao

doi:10.1016/j.insmatheco.2015.03.022

Cited by 43 publications

(45 citation statements)

References 46 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We examine the best JO-LSHAC model (Table 3). For competing models, in addition to the Student's t copula, we also consider the factor copula model proposed by Chen, MacMinn, and Sun (2015). 6 We perform the backtesting using the following three steps: different models are all very close to the true values of the survival index.…”

Section: Multicountry Survivor Index Swapsmentioning

confidence: 99%

“…Many innovative contracts have payoffs that are linked to broad-based population mortality indices; hence, a more sophisticated stochastic mortality model for multipopulation is critical for pricing these longevity-linked securities as well as for reducing population basis risk (Blake et al, 2013). Chen, MacMinn, and Sun (2015) introduce factor copula into multipopulation mortality modeling. They employ a two-stage procedure based on the time series analysis and a factor copula approach.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Multicountry Longevity Risk With Mortality Dependence: A Lévy Subordinated Hierarchical Archimedean Copulas Approach

Zhu¹,

Tan²,

Wang³

2017

J of Risk & Insurance

View full text Add to dashboard Cite

This article proposes a new copula model known as the Lévy subordinated hierarchical Archimedean copulas (LSHAC) for multicountry mortality dependence modeling. To the best of our knowledge, this is the first article to apply the LSHAC model to mortality studies. Through an extensive empirical analysis on modeling mortality experiences of 13 countries, we demonstrate that the LSHAC model, which has the advantage of capturing the geographical structure of mortality data, yields better fit, compared to the elliptical copulas. In addition, the proposed LSHAC model generates out‐of‐sample forecasts with smaller standard deviations, when compared to other benchmark copula models. The LSHAC model also confirms that there is an association between geographical locations and dependence of the overall mortality improvement. These results yield new insights into future longevity risk management. Finally, the model is used to price a hypothetical survival index swap written on a weighted mortality index. The results highlight the importance of dependence modeling in managing longevity risk and reducing population basis risk.

show abstract

Section: Multicountry Survivor Index Swapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Multicountry Longevity Risk With Mortality Dependence: A Lévy Subordinated Hierarchical Archimedean Copulas Approach

Zhu¹,

Tan²,

Wang³

2017

J of Risk & Insurance

View full text Add to dashboard Cite

show abstract

“…OnceQ has been obtained, the corresponding near-optimal valuer of r can be found insertingQ into Equation (9). We would finally remark that the approximation procedure has allowed the derivation of a near-optimal solution (in Q) in closed form.…”

Section: An Approximated Procedures To Solve Problem (P2)mentioning

confidence: 99%

“…Taking into account some of the most recent works, we can cite the following contexts: urban planning [6]; queueing systems [7]; structural dynamics [8]; insurance [9]; computer vision and multimedia [10]; computer algebra [11]; geology [12]; biomechanics [13]; signal processing [14]. The literature gives some reviews concerning applications in ecology, finance, physics, chemistry, and biology [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle

Castellano

2015

Entropy

View full text Add to dashboard Cite

This paper takes into account the continuous-review reorder point-lot size (r,Q) inventory model under stochastic demand, with the backorders-lost sales mixture. Moreover, to reflect the practical circumstance in which full information about the demand distribution lacks, we assume that only an estimate of the mean and of the variance is available. Contrarily to the typical approach in which the lead-time demand is supposed Gaussian or is obtained according to the so-called minimax procedure, we take a different perspective. That is, we adopt the maximum entropy principle to model the lead-time demand distribution. In particular, we consider the density that maximizes the entropy over all distributions with given mean and variance. With the aim of minimizing the expected total cost per time unit, we then propose an exact algorithm and a heuristic procedure. The heuristic method exploits an approximated expression of the total cost function achieved by means of an ad hoc first-order Taylor polynomial. We finally carry out numerical experiments with a twofold objective. On the one hand we examine the efficiency of the approximated solution procedure. On the other hand we investigate the performance of the maximum entropy principle in approximating the true lead-time demand distribution.

show abstract

“…We refer interested readers to Dowd et al (2011). infancy stage. To the best of our knowledge, only a few articles (e.g., Lin, Liu, and Yu, 2013;Chen, MacMinn, and Sun, 2015;Wang, Yang, and Huang, 2015) fall into this category. Lin et al (2013) develop a jump diffusion mortality model for six countries where mortality dependence comes from common jumps and the correlation between idiosyncratic risks.…”

Section: Introductionmentioning

confidence: 99%

Mortality Dependence and Longevity Bond Pricing: A Dynamic Factor Copula Mortality Model With the GAS Structure

Chen

MacMinn

Sun

2017

J of Risk & Insurance

Self Cite

View full text Add to dashboard Cite

Modeling mortality dependence for multiple populations has significant implications for mortality/longevity risk management. A natural way to assess multivariate dependence is to use copula models. The application of copula models in the multipopulation mortality analysis, however, is still in its infancy. In this article, we present a dynamic multipopulation mortality model based on a two‐factor copula and capture the time‐varying dependence using the generalized autoregressive score (GAS) framework. Our model is simple and flexible in terms of model specification and is widely applicable to high dimension data. Using the Swiss Re Kortis longevity trend bond as an example, we use our model to estimate the probability distribution of principal reduction and some risk measures such as probability of first loss, conditional expected loss, and expected loss. Due to the similarity in the structure and design of CAT bonds and mortality/longevity bonds, we borrow CAT bond pricing techniques for mortality/longevity bond pricing. We find that our pricing model generates par spreads that are close to the actual spreads of previously issued mortality/longevity bonds.

show abstract

Multi-population mortality models: A factor copula approach

Cited by 43 publications

References 46 publications

Modeling Multicountry Longevity Risk With Mortality Dependence: A Lévy Subordinated Hierarchical Archimedean Copulas Approach

Modeling Multicountry Longevity Risk With Mortality Dependence: A Lévy Subordinated Hierarchical Archimedean Copulas Approach

Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle

Mortality Dependence and Longevity Bond Pricing: A Dynamic Factor Copula Mortality Model With the GAS Structure

Contact Info

Product

Resources

About