Development and calibration of a currency trading strategy using global optimization

We have developed a new financial indicator—called the Interest Rate Differentials Adjusted for Volatility (IRDAV) measure—to assist investors in currency markets. On a monthly basis, we rank currency pairs according to this measure and then select a basket of pairs with the highest IRDAV values. Under positive market conditions, an IRDAV based investment strategy (buying a currency with high interest rate and simultaneously selling a currency with low interest rate, after adjusting for volatility of the currency pairs in question) can generate significant returns. However, when the markets turn for the worse and crisis situations evolve, investors exit such money-making strategies suddenly, and—as a result—significant losses can occur. In an effort to minimize these potential losses, we also propose an aggregated Risk Metric that estimates the total risk by looking at various financial indicators across different markets. These risk indicators are used to get timely signals of evolving crises and to flip the strategy from long to short in a timely fashion, to prevent losses and make further gains even during crisis periods. Since our proprietary model is implemented in Excel as a highly nonlinear “black box” computational procedure, we use suitable global optimization methodology and software—the Lipschitz Global Optimizer solver suite linked to Excel—to maximize the performance of the currency basket, based on our selection of key decision variables. After the introduction of the new currency trading model and its implementation, we present numerical results based on actual market data. Our results clearly show the advantages of using global optimization based parameter settings, compared to the typically used “expert estimates” of the key model parameters.post-prin

Development of a robust and efficient biogas processor for hydrogen production. Part 1: Modelling and simulation

SSCI-VIDE+ING+FRM:NOG:YSCInternational audienceThe present work deals with the modelling and simulation of a biogas Demo-processor for green hydrogen production via Autothermal reforming (ATR) process aimed at covering a wide span of potential applications, from fuel cells feed up to the production of pure hydrogen. The biogas ATR unit is composed of a structured catalyst support close coupled to a wall-flow filter that retain soot particles that can be formed during the ATR reaction. Modelling and simulation (CFD and FEM) were carried out to select the innovative catalyst support with promising results for the fuel processor. 3D digital sample reconstruction was performed for the selection of the appropriate porous structures commercially available for the soot filtration and furthermore, 2D CFD analysis was also used to examine flow uniformity issues due to soot trap integration downstream to the ATR. Moreover, the inherent flexibility of the model performed allowed its application in the assessment of the Demonstration plant operating in real conditions. Besides, Aspen simulation has demonstrated that the ATR process is the most promising process to hydrogen production compared to other types of reforming process

Development of a robust and efficient biogas processor for hydrogen production. Part 2: Experimental campaign

SSCI-VIDE+ING+FRM:NOG:YSCInternational audienceHydrogen productio