|
|
|
|
|
Minh-Quan Vo, Thu Nguyen, Michael A. Riegler and Hugo L. Hammer
Generative models have recently received a lot of attention. However, a challenge with such models is that it is usually not possible to compute the likelihood function, which makes parameter estimation or training of the models challenging. The most com...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wendy Wijaya and Kuntjoro Adji Sidarto
Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A great deal of improvement in portfolio optimization models has been made, including the addition of practical constraints. As the number...
ver más
|
|
|
|
|
|
|
|
|
|
|
Werry Febrianti, Kuntjoro Adji Sidarto and Novriana Sumarti
Portfolio optimization is an activity for balancing return and risk. In this paper, we used mean-variance (M-V) portfolio models with buy-in threshold and cardinality constraints. This model can be formulated as a mixed integer nonlinear programming (MIN...
ver más
|
|
|
|
|
|
|
|
|
|
|
Nikhil Iyengar, Dushhyanth Rajaram and Dimitri Mavris
Uncertainties in the atmosphere and flight conditions can drastically impact the performance of an aircraft and result in certification delays. However, uncertainty propagation in high-fidelity simulations, which have become integral to the design proces...
ver más
|
|
|
|
|
|
|
|
|
|
|
Apichat Chaweewanchon and Rujira Chaysiri
With the advances in time-series prediction, several recent developments in machine learning have shown that integrating prediction methods into portfolio selection is a great opportunity. In this paper, we propose a novel approach to portfolio formation...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jules Clément Mba, Kofi Agyarko Ababio and Samuel Kwaku Agyei
This paper investigates the robustness of the conventional mean-variance (MV) optimization model by making two adjustments within the MV formulation. First, the portfolio selection based on a behavioral decision-making theory that encapsulates the MV sta...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guido Abate, Tommaso Bonafini and Pierpaolo Ferrari
Mean-variance optimization often leads to unreasonable asset allocations. This problem has forced scholars and practitioners alike to introduce portfolio constraints. The scope of our study is to verify which type of constraint is more suitable for achie...
ver más
|
|
|
|
|
|
|
|
|
|
|
Daniel C. Londoño Tamayo, Walter M. Villa-Acevedo and Jesús M. López-Lezama
Optimal reactive power dispatch plays a key role in the safe operation of electric power systems. It consists of the optimal management of the reactive power sources within the system, usually with the aim of reducing system power losses. This paper pres...
ver más
|
|
|
|
|
|
|
|
|
|
|
Massimo Guidolin and Manuela Pedio
In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchm...
ver más
|
|
|
|
|
|
|
|
|
|
|
Martin M. Sánchez-Mora, David Lionel Bernal-Romero, Oscar Danilo Montoya, Walter M. Villa-Acevedo and Jesús M. López-Lezama
The Optimal Reactive Power Dispatch (ORPD) problem consists of finding the optimal settings of reactive power resources within a network, usually with the aim of minimizing active power losses. The ORPD is a nonlinear and nonconvex optimization problem t...
ver más
|
|
|
|
|
|