Trent University Graduate Thesis Collection

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    tula:etd
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    Copyright for all items in the Trent University Graduate Thesis Collection is held by the author, with all rights reserved, unless otherwise noted.
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    The Application of One-factor Models for Prices of Crops and Option Pricing Process

    Year: 2016, 2016
    Member of: Trent University Graduate Thesis Collection
    Name(s): Creator (cre): Xu, Mengxi, Thesis advisor (ths): Abdella, Kenzu, Thesis advisor (ths): Pollanen, Marco, Degree granting institution (dgg): Trent University
    Abstract: <p>This thesis is intended to support dependent-on-crops farmers to hedge the price risks of their crops. Firstly, we applied one-factor model, which incorporated a deterministic function and a stochastic process, to predict the future prices of crops (soybean). A discrete form was employed for one-month-ahead prediction. For general prediction, de-trending and de-cyclicality were used to… more

    Modeling drought derivatives in arid regions: a case study in Qatar

    Year: 2016, 2016
    Member of: Trent University Graduate Thesis Collection
    Name(s): Creator (cre): Paek, Jayoeng, Thesis advisor (ths): Pollanen, Marco, Thesis advisor (ths): Abdela, Kenzu, Degree granting institution (dgg): Trent University
    Abstract: <p>We propose a stochastic weather model based on temperature, precipitation, humidity and wind speed for Qatar, as a representative arid region, in order to obtain simulated values for a drought index. As a drought index, the Reconnaissance Drought Index (RDI) is commonly accepted in agriculture and is used to measure drought severity. It can be used to price weather derivatives to help… more

    Smote and Performance Measures for Machine Learning Applied to Real-Time Bidding

    Year: 2016, 2016
    Member of: Trent University Graduate Thesis Collection
    Name(s): Creator (cre): McInroy, Ben P., Thesis advisor (ths): Feng, Wenying, Degree committee member (dgc): Patrick, Brian, Degree committee member (dgc): Pollanen, Marco, Degree granting institution (dgg): Trent University
    Abstract: <p>In the context of Real-Time Bidding (RTB) the machine learning problems of</p><p>imbalanced classes and model selection are investigated. Synthetic Minority Oversampling Technique (SMOTE) is commonly used to combat imbalanced classes but a shortcoming is identified. Use of a distance threshold is identified as a solution and testing in a live RTB environment shows significant… more