Abstract
Big data is being generated by everything around us at all times. The massive amount and corresponding data of assets in the financial market naturally form a big data set. In this paper, we tackle the multi-period mean-variance portfolio of asset-liability management using the parameterized method addressed in Li et al. (SIAM J. Control Optim. 40:1540–1555, 2002) and the state variable transformation technique. By this simple yet efficient method, we derive the analytical optimal strategies and efficient frontiers accurately. A numerical example is presented to shed light on the results established in this work.
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Acknowledgements
This work was partially supported by Research Grants Council of Hong Kong under grants 519913, 15209614 and 15224215, by National Natural Science Foundation of China (Nos. 71231008, 71471045), by China Postdoctoral Science Foundation (No. 2014M560658 and No. 2016M592505), and by Characteristic and Innovation Foundation of Guangdong Colleges and Universities (Humanity and Social Science Type).
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Li, X., Li, Z., Wu, X., Yao, H. (2017). A Parameterized Method for Optimal Multi-Period Mean-Variance Portfolio Selection with Liability. In: Choi, TM., Gao, J., Lambert, J., Ng, CK., Wang, J. (eds) Optimization and Control for Systems in the Big-Data Era. International Series in Operations Research & Management Science, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-319-53518-0_9
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DOI: https://doi.org/10.1007/978-3-319-53518-0_9
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