We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective...

A data driven Neural Network (NN) optimization framework is proposed to determine optimal asset allocation during the accumulation phase of a defined contribution pension scheme. In contrast to parametric model based solutions computed by a partial differential equation approach, the proposed...

Increasing attention has been focused on the analysis of the realized volatility, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged...

Increasing attention has been focused on the analysis of the realized volatility, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged...

A numerical technique based on the embedding technique proposed in [21, 33] for dynamic mean-variance (MV) optimization problems may yield spurious points, i.e. points which are not on the efficient frontier. In [27], it is shown that spurious points can be eliminated by examining the left upper...

Wind environment research in the populated urban area is an important topic on wind potential evaluation as well as wind comfort analysis. This study took measurement of 57 crossroad spots in the campus in North China University of Technology in Beijing. The data of wind speed and direction were...

The objective of this study is to examine the market valuation of environmental capital expenditure investment related to pollution abatement in the pulp and paper industry. The total environmental capital expenditure of $8.7 billion by our sample firms during 1989-2000 supports the focus on...

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three...

The collapse of the Californian electricity market system in 2001 has highlighted urgency in research in intelligent electricity trading systems and strategies involving both suppliers and customs. In their trading systems, power generation companies under the new electricity trading arrangement...