Efficient Nested Simulation for Conditional Tail Expectation of Variable Annuities

Dang, Ou; Feng, Mingbin; Hardy, Mary R.

doi:10.1080/10920277.2019.1636399

Cited by 18 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In section 4, we describe the more specific assumptions used for numerical illustrations. The description here is similar to that in Dang et al (2020), but we have extended the notation to allow for the extension to a dynamic methodology. For more information on VA contracts and different types of guarantees, see for example Hardy (2003).…”

Section: Dynamic Hedging For Variable Annuities Via Nested Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic importance allocated nested simulation for variable annuity risk measurement

2022

Self Cite

View full text Add to dashboard Cite

Estimating tail risk measures for portfolios of complex variable annuities is an important enterprise risk management task which usually requires nested simulation. In the nested simulation, the outer simulation stage involves projecting scenarios of key risk factors under the real-world measure, while the inner simulations are used to value pay-offs under guarantees of varying complexity, under a risk-neutral measure. In this paper, we propose and analyse an efficient simulation approach that dynamically allocates the inner simulations to the specific outer scenarios that are most likely to generate larger losses. These scenarios are identified using a proxy calculation that is used only to rank the outer scenarios, not to estimate the tail risk measure directly. As the proxy ranking will not generally provide a perfect match to the true ranking of outer scenarios, we calculate a measure based on the concomitant of order statistics to test whether further tail scenarios are required to ensure, with given confidence, that the true tail scenarios are captured. This procedure, which we call the dynamic importance allocated nested simulation approach, automatically adjusts for the relationship between the proxy calculations and the true valuations and also signals when the proxy is not sufficiently accurate.

show abstract

Section: Dynamic Hedging For Variable Annuities Via Nested Simulationmentioning

confidence: 99%

“…In this paper, we introduce a dynamic importance allocated nested simulation (DIANS) methodology, which is an extension of the IANS method introduced in Dang et al (2020). The method combines elements of the proxy model approach with elements of the dynamic allocation approach.…”

Section: Introductionmentioning

confidence: 99%

Dynamic importance allocated nested simulation for variable annuity risk measurement

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…See, for instance, Gan and Lin (2015), Hejazi and Jackson (2016), Gan and Valdez (2018), Gan (2018), Gan and Valdez (2020), Gweon et al (2020), Liu and Tan (2020), Lin and Yang (2020), Feng et al (2020), and Quan et al (2021). Similar idea has also been applied to the calculation of Greeks and risk measures of a portfolio of variable annuities; see Gan and Lin (2017), Gan and Valdez (2017), Xu et al (2018), and Dang et al (2020). All of the above literature applying the machine learning methods involve the supervised learning (SL), which requires a pre-labelled dataset (in this case, it is the set of fair prices of the representative contracts) to train a predictive model.…”

Section: Introductionmentioning

confidence: 99%

Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning

Chong¹,

Cui²,

Li³

2021

Preprint

View full text Add to dashboard Cite

This paper applies a deep reinforcement learning approach to revisit the hedging problem of variable annuities. Instead of assuming actuarial and financial dualmarket model a priori, the reinforcement learning agent learns how to hedge by collecting anchor-hedging reward signals through interactions with the market. By the recently advanced proximal policy optimization, the pseudo-model-free reinforcement learning agent performs equally well as the correct Delta, while outperforms the misspecified Deltas. The reinforcement learning agent is also integrated with online learning to demonstrate its full adaptive capability to the market.

show abstract

“…Two representative examples of quantities of the form (1) stem from financial risk management (Lan et al 2010, Hong et al 2017, Dang et al 2020) and input uncertainty quantification for stochastic simulation (Barton 2012.…”

Section: Introductionmentioning

confidence: 99%

Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions

Wang¹,

Wang²,

Zhang³

2022

Preprint

View full text Add to dashboard Cite

Nested simulation concerns estimating functionals of a conditional expectation via simulation. In this paper, we propose a new method based on kernel ridge regression to exploit the smoothness of the conditional expectation as a function of the multidimensional conditioning variable. Asymptotic analysis shows that the proposed method can effectively alleviate the curse of dimensionality on the convergence rate as the simulation budget increases, provided that the conditional expectation is sufficiently smooth. The smoothness bridges the gap between the cubic root convergence rate (that is, the optimal rate for the standard nested simulation) and the square root convergence rate (that is, the canonical rate for the standard Monte Carlo simulation). We demonstrate the performance of the proposed method via numerical examples from portfolio risk management and input uncertainty quantification.

show abstract

Efficient Nested Simulation for Conditional Tail Expectation of Variable Annuities

Cited by 18 publications

References 19 publications

Dynamic importance allocated nested simulation for variable annuity risk measurement

Dynamic importance allocated nested simulation for variable annuity risk measurement

Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning

Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions

Contact Info

Product

Resources

About