Dynamic hedging of foreign exchange risk using stochastic model predictive control

Noorian, Farzad; Leong, Philip H. W.

doi:10.1109/cifer.2014.6924107

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Linear regression is widely used in all kinds of machine learning projects. It is also simple enough to be used as a performance baseline (Noorian, 2015;Preisler et al, 2015;Yao et al, 2018). Therefore, the authors chose linear regression as the first model for analysis.…”

Section: Proposed Modelsmentioning

confidence: 99%

Wildfire Air Quality Prediction

Dhar,

Gao

2023

International Journal of Disaster Response and Emergency Management

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Wildfires are extremely harmful to the environment. While producing gaseous pollutants and particles that cause smoke, wildfires also release carbon dioxide (CO2), a greenhouse gas that will continue to warm the planet after the wildfire ends. This article delves into the impact of wildfires and air quality on human living conditions. The authors' machine learning models use wildfire data to forecast air quality with detailed indexes and geographic information during a wildfire. The work evaluates the performance of each machine learning model via statistical metrics like mean absolute error (MAE), R-squared (R2), and root mean squared error (RMSE). The experimental results used neural networks to predict a specific value for carbon monoxide (CO), ozone, and PM2.5. These are both promising and accurate, providing meaningful insight into air quality within a region. This work will be useful for cities, governments, citizens, and public safety.

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Section: Proposed Modelsmentioning

confidence: 99%

Wildfire Air Quality Prediction

Dhar,

Gao

2023

International Journal of Disaster Response and Emergency Management

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“…The main contribution of this work is the formulation of a QP based FX risk management strategy which can accommodate stochastic FX rate and client flow models. While the idea of hedging risk, either by using derivatives or by employing a hedging portfolio (Josephy et al, 2013) has been thoroughly explored in the literature, to the best of our knowledge the proposed method has been formally introduced only in previous work by the authors (Noorian and Leong, 2014). This paper extends that work by imposing limit constraints on positions, more realistic volatility models and time-varying bid-ask spreads, further analysis and simplification of the analytic cost function, and finally the formalization of a scenario generation oracle with exponential decay.…”

Section: Introductionmentioning

confidence: 99%

Stochastic receding horizon control for short-term risk management in foreign exchange

Noorian

Flower²,

Leong

2016

JOR

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Foreign exchange (FX) dealers are exposed to currency risk through both market and counterparty activities. Research in FX risk management has mainly focused on long-term risks, yet trading costs associated with longterm strategies make them undesirable for short-term risk hedging. In this paper, a short-term risk management system for FX dealers is described in which the optimal risk-cost profiles are obtained through dynamic control of the dealer's positions on the spot market. This approach is formulated as a stochastic receding horizon control (SRHC) problem, incorporating elements which model client flow, transaction cost, market impact, exchange rate volatility and fluctuations caused by macroeconomic announcements. The proposed technique is backtested using both synthetic and historical client trade data. The results obtained outperform three benchmark hedging strategies on a risk-cost Pareto frontier, achieving up to 47.6% cost improvement over benchmark strategies. A flexible scenario generation oracle is also introduced and used to quantify the effects of predictive model quality on risk management.

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