Dynamic asset–liability management in a Markov market with stochastic cash flows

Haixiang Yao, Xun Li, Zhifeng Hao, Yong Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


This paper provides a general model to investigate an asset–liability management (ALM) problem in a Markov regime-switching market in a multi-period mean–variance (M–V) framework. Emphasis is placed on the stochastic cash flows in both wealth and liability dynamic processes, and the optimal investment and liquidity management strategies in achieving the M–V bi-objective of terminal surplus are evaluated. In this model, not only the asset returns and liability returns, but also the cash flows depend on the stochastic market states, which are assumed to follow a discrete-time Markov chain. Adopting the dynamic programming approach, the matrix theory and the Lagrange dual principle, we obtain closed-form expressions for the efficient investment strategy. Our proposed model is examined through empirical studies of a defined contribution pension fund. In-sample results show that, given the same risk level, an ALM investor (a) starting in a bear market can expect a higher return compared to beginning in a bull market and (b) has a lower expected return when there are major cash flow problems. The effects of the investment horizon and state-switching probability on the efficient frontier are also discussed. Out-of-sample analyses show the dynamic optimal liquidity management process. An ALM investor using our model can achieve his or her surplus objective in advance and with a minimum variance close to zero.
Original languageEnglish
Pages (from-to)1575-1597
Number of pages23
JournalQuantitative Finance
Issue number10
Publication statusPublished - 2 Oct 2016


  • Asset–liability management
  • Efficient investment strategy
  • Markov regime-switching
  • Multi-period mean–variance model
  • Stochastic cash flow

ASJC Scopus subject areas

  • Finance
  • Economics, Econometrics and Finance(all)


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