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Welcome to my webpage!

I am a PhD candidate at Universidad Carlos III de Madrid.

My research interests focus on optimization, operational statistics

and quantitative portfolio management. For further information, you may download my academic resume.

Research

-Multiperiod Period Portfolio Optimization with General Transaction Costs (with V. DeMiguel and F.J. Nogales) .Journal of Banking & Finance; Volume 69, August 2016, Pages 108–120.

Abstract: We analyze the properties of the optimal portfolio policy for a multiperiod mean-variance investor facing multiple risky assets in the presence of general transaction costs such as proportional, temporary market impact, and quadratic transaction costs. For proportional transaction costs, we find analytically that a buy-and-hold policy is optimal: if the starting portfolio is outside a parallelogram-shaped no-trade region, then trade to the boundary of the no-trade region at the first period, and hold this portfolio thereafter. For temporary market impact costs, we show that the optimal portfolio policy at each period is to trade to the boundary of a state-dependent rebalancing region. Moreover, we find that the rebalancing region shrinks along the investment horizon, and as a result the investor trades throughout the entire investment horizon. Finally, we show numerically that the utility loss associated with ignoring transaction costs or investing myopically maybe large.

- Portfolio Selection with Proportional Transaction Costs and Predictability(with F.J. Nogales). Journal of Banking & Finance, Volume 94, September 2018, Pages 131-151.

Abstract: We consider the portfolio optimization problem for a multiperiod investor who seeks to maximize her utility of consumption facing multiple risky assets and proportional transaction costs in the presence of return predictability. Due to curse of dimensionality, this problem is very difficult to solve even numerically. In this paper, we propose several feasible policies that are based on optimizing quadratic programs. These proposed feasible policies can be easily computed even for many risky assets. We show how to compute upper bounds and use them to study how the looses associated with using the approximate policies depend on different problem parameters.