Optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility

In this final year project, we further study the dynamic mean-variance problem with constrained risk control on reinsurance and investment (no-shorting) strategy for insurers with unknown expected terminal wealth. This project will fi rst solve the problem under traditional Black-Scholes model, whe...

Full description

Saved in:
Bibliographic Details
Main Author: Sun, Jingya
Other Authors: PUN Chi Seng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/146121
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-146121
record_format dspace
spelling sg-ntu-dr.10356-1461212023-02-28T23:18:51Z Optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility Sun, Jingya PUN Chi Seng School of Physical and Mathematical Sciences cspun@ntu.edu.sg Science::Mathematics::Applied mathematics::Operational research Business::Finance::Portfolio management In this final year project, we further study the dynamic mean-variance problem with constrained risk control on reinsurance and investment (no-shorting) strategy for insurers with unknown expected terminal wealth. This project will fi rst solve the problem under traditional Black-Scholes model, where the problem is first embedded into an auxiliary stochastic linear-quadratic (LQ) control problem. Then a viscosity solution of Hamilton-Jacobi-Bellman (HJB) equations is identifi ed so as to derive the e fficient frontier and e fficient strategies explicitly by a verfi cation theorem. An extension on solving the problem under the stochastic volatility model will be studied as well. Bachelor of Science in Mathematical Sciences 2021-01-27T02:10:26Z 2021-01-27T02:10:26Z 2018 Final Year Project (FYP) https://hdl.handle.net/10356/146121 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Mathematics::Applied mathematics::Operational research
Business::Finance::Portfolio management
spellingShingle Science::Mathematics::Applied mathematics::Operational research
Business::Finance::Portfolio management
Sun, Jingya
Optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility
description In this final year project, we further study the dynamic mean-variance problem with constrained risk control on reinsurance and investment (no-shorting) strategy for insurers with unknown expected terminal wealth. This project will fi rst solve the problem under traditional Black-Scholes model, where the problem is first embedded into an auxiliary stochastic linear-quadratic (LQ) control problem. Then a viscosity solution of Hamilton-Jacobi-Bellman (HJB) equations is identifi ed so as to derive the e fficient frontier and e fficient strategies explicitly by a verfi cation theorem. An extension on solving the problem under the stochastic volatility model will be studied as well.
author2 PUN Chi Seng
author_facet PUN Chi Seng
Sun, Jingya
format Final Year Project
author Sun, Jingya
author_sort Sun, Jingya
title Optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility
title_short Optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility
title_full Optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility
title_fullStr Optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility
title_full_unstemmed Optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility
title_sort optimal investment-reinsurance strategy on dynamic mean-variance problem with stochastic volatility
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/146121
_version_ 1759857908046102528