Invited Session Wed.2.H 3027

Wednesday, 13:15 - 14:45 h, Room: H 3027

Cluster 7: Finance & economics [...]

Optimization methodologies in computational finance


Chair: Wei Xu



Wednesday, 13:15 - 13:40 h, Room: H 3027, Talk 1

Cristinca Fulga
Portfolio optimization with a new quantile-based risk measure and prior AHP stock ranking


We develop a portfolio selection method which takes into consideration the recent positive evolution of the risky assets that are not comprised in the portfolio available at the moment of the decision by using the Analytical Hierarchy Process. Our model relies on a new quantile based risk measure which is defined using the modified loss distribution according to the decision maker’s loss aversion preferences. We investigate the practical performance of the proposed Mean-Risk model on a portfolio composed of some of the most representative securities of the Bucharest Stock Exchange.



Wednesday, 13:45 - 14:10 h, Room: H 3027, Talk 2

Wei Xu
A new sampling strategy willow tree method with application to path-dependent option pricing

Coauthor: Zhiwu Hong


Willow tree algorithm, first developed by Curran in 1998,
provides an efficient option pricing procedure. However, it leads to a big bias using Curran's sampling strategy when the number of points at each time step is not large. Thus, in this paper, we propose a new sampling strategy with solving a small nonlinear least square problem. Compared with Curran's sampling strategy, the new strategy gives a much better estimation of the standard normal distribution with small amount of sampling spatial points. Then, we apply the willow tree algorithm with the new sampling strategy to price path-dependent options such as American, Asian and American moving-average options. The numerical results illustrate that the willow tree algorithm is much more efficient than the least square Monte Carlo method and binomial tree method.



Wednesday, 14:15 - 14:40 h, Room: H 3027, Talk 3

Asaf Shupo
Optimal promotion rate in a cash campaign

Coauthors: Dragos Calitoiu, Hasan Mytkolli


Taking care of customers and serving them better by building optimal strategies meeting their financial needs are the most important challenges to maintain existing customers and to remain profitable. In the scenario when a company lends money to its customers, the process of assigning to each offer the optimum interest rate becomes a complex task, considering many other offers from competitors and considering that, in many cases, the goal of lending is not only the profit while reducing risk but also satisfying real needs of customers.
This current research presents the results of implementing our previous reported network optimization approach that uses customer level scores produced by a suite of cash models. This implementation is a real-life application which helps building optimal promotion campaigns by offering the optimal interest rates to each customer. In summary, from the mathematical perspective, this application provides an integer optimal solution which optimizes a goal function subject to some budged and business constraints. The improvement of using this optimization process vs. the classical approach was evident in all campaigns investigated in this research.


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