Multistage risk-averse asset allocation with transaction costs

This paper deals with asset allocation problems formulated as multistage stochastic programming models. Dynamic models allow rebalancing the portfolio multiple times before the final investment horizon is reached. The CVaR risk measure is used for its favorable properties and time consistent model is developed. The risk-aversion coefficients are separate for every stage, which covers a wide range of investment strategies. For example one could start with an aggressive investment policy, but focus on reducing the risk as the final stage approaches. The stock prices are assumed to be interstage independent and to follow lognormal distribution. The Stochastic Dual Dynamic Programming algorithm is then applied to solve the presented models. An extensive numerical study based on the data from Prague Stock Exchange compares the results obtained from static two stage models with the results from dynamic models having multiple stages. Both cases, with or without the transaction costs, are considered. The computational part of this work is realized in C++ language and uses CPLEX to solve the linear programs.