Engineering Faculty

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    FORECASTING RETURN VOLATILITY OF CRUDE OIL FUTURE PRICES USING ARTIFICIAL NEURAL NETWORKS; BASED ON INTRA MARKETS VARIABLES AND FOCUS ON THE SPECULATION ACTIVITY
    (2014-05-15) Hasanabadi, Hamed Shafiee; Khan, Saqib; Mayorga, Rene V.
    Considering the strong linkages between commodity and equity markets during the few past years, the motivation of the study in this Chapter is to forecast the crude oil future prices return volatilities of based on the information from the intra markets variables.
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    A NOVEL METHOD FOR ESTIMATING THE INVERSE FUNCTION OF BLACK-SCHOLES OPTION PRICING MODEL USING ARTIFICIAL NEURAL NETWORKS
    (2014-05-20) Hasanabadi, Hamed Shafiee; Mayorga, Rene V.
    Black-Scholes (BS) model is a well-known model for pricing options. Option is a derivative financial instrument which gives its owner the right of buying the underlying asset at a pre specified date for a pre specified price. BS model calculates the option price using 5 input variables and parameters including current underlying price, strike price, time to maturity, interest rate and the volatility of the underlying asset price.
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    Estimating the Inverse Function of Compound Options Pricing Model Using Artificial Neural Networks
    (2014-05-20) Hasanabadi, Hamed Shafiee; Mayorga, Rene V.
    Compound options are second order derivatives which give their holders the right for exercising over other derivatives. They are options on options. Compound options have many financial applications. Pricing methods for exotic options such as compounds are much more complex than the regular options. There are different models for pricing compound options. Simulating direct function of compound option pricing model based on the Black-Scholes model needs 7 input variables including current underlying asset price, basic option strike price, the time to expiration of the basic option, the volatility of the underlying asset price, the risk-free interest rate, compound option strike price, and time to expiration of the compound option.
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    Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands
    (Elsevier BV, 2021-06-01) Karimi, Nima; Ng, Kelvin Tsun Wai; Richter, Amy; Williams, Jason; Ibrahim, Hussameldin
    Information on the spatial extent of potential impact areas near disposal sites is vital to the development of a sustainable natural resource management policy. Eight Canadian landfills of various sizes and shapes in different climatic conditions are studied to quantify the spatial extent of their bio-thermal zone. Land surface temperature (LST) and normalized difference vegetation index (NDVI) are examined with respect to different Land Use Land Cover (LULC) classes. Within 1500 m of the sites, LST ranged from 18.3 °C to 29.5 °C and 21.3 °C–29.7 °C for forest land and agricultural land, respectively. Linear regression shows a decreasing LST trend in forest land for five out of seven landfills. A similar trend, however, is not observed for agricultural land. Both the magnitude and the variability of LST are higher in agricultural land. The size of the bio-thermal zone is sensitive to the respective LULC class. The approximate bio-thermal zones for forest class and agricultural classes are about 170 ± 90 m and 180 ± 90 m from the landfill perimeter, respectively. For the forest class, NDVI was negatively correlated with LST at six out of seven Canadian landfills, and stronger relationships are observed in the agricultural class. NDVI data has a considerably larger spread and is less consistent than LST. LST data appears more appropriate for identifying landfill bio-thermal zones. A subtle difference in LST is observed among six LULC classes, averaging from 23.9 °C to 27.4 °C. Geometric shape makes no observable difference in LST in this study; however, larger landfill footprint appears to have higher LST.