دانلود مقاله ISI انگلیسی شماره 42203
ترجمه فارسی عنوان مقاله

راهبردهای بیمه اتکایی و سرمایه گذاری سازگار با زمان برای بیمه میانگین واریانس تحت عنوان اطلاعات جزئی

عنوان انگلیسی
Time-consistent reinsurance and investment strategies for mean–variance insurer under partial information
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
42203 2015 11 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Insurance: Mathematics and Economics, Volume 65, November 2015, Pages 66–76

ترجمه کلمات کلیدی
قانون کنترل تعادل - استراتژی سازگار با زمان - سرمایه گذاری بیمه اتکایی - اطلاعات جزئی - معیار واریانس - تعویض رژیم
کلمات کلیدی انگلیسی
G11; C61; G3291B30; 91B70; 91B16; 91G10; 93E20IE13; IE12; IM52; IB91; IE53; IE43Equilibrium control law; Time-consistent strategy; Investment–reinsurance; Partial information; Mean–variance criterion; Regime switching
پیش نمایش مقاله
پیش نمایش مقاله  راهبردهای بیمه اتکایی و سرمایه گذاری سازگار با زمان برای بیمه میانگین واریانس تحت عنوان اطلاعات جزئی

چکیده انگلیسی

In this paper, based on equilibrium control law proposed by Björk and Murgoci (2010), we study an optimal investment and reinsurance problem under partial information for insurer with mean–variance utility, where insurer’s risk aversion varies over time. Instead of treating this time-inconsistent problem as pre-committed, we aim to find time-consistent equilibrium strategy within a game theoretic framework. In particular, proportional reinsurance, acquiring new business, investing in financial market are available in the market. The surplus process of insurer is depicted by classical Lundberg model, and the financial market consists of one risk free asset and one risky asset with unobservable Markov-modulated regime switching drift process. By using reduction technique and solving a generalized extended HJB equation, we derive closed-form time-consistent investment–reinsurance strategy and corresponding value function. Moreover, we compare results under partial information with optimal investment–reinsurance strategy when Markov chain is observable. Finally, some numerical illustrations and sensitivity analysis are provided.