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dc.contributor.authorNgige, Isabel
dc.date.accessioned2016-03-17T10:04:38Z
dc.date.available2016-03-17T10:04:38Z
dc.date.issued2015-12
dc.identifier.urihttp://hdl.handle.net/11071/4322
dc.descriptionResearch Project submitted in partial fulfillment of the requirements for the Degree of Bachelor of Business Science in Actuarial Science at Strathmore Universityen_US
dc.description.abstractThis study is a comparative test of the forecasting ability of the half-life purchasing power parity model and the portfolio-hybrid model used for forecasting exchange rates in Kenya. The United States Dollar and Ugandan Shilling exchange rates are analyzed for the period between 2005 and 2015. The models' forecasting precision assessment is based on three statistical tests; First Diebold and Mariano (1995) null test of equal forecasting ability is used to determine the superior half-life model among the three, four and five year half-life durations of which there is none, the root mean squared forecasting error and Thiel's inequality co-efficient tests are finally used between the three year half-life model for the Ugandan Shilling, the five year half-life model for the United States Dollar and the portfolio-hybrid model. The study concludes that the half-life purchasing power parity model is more precise in forecasting the United States Dollar and Ugandan Shilling exchange rates, albeit in sample. The conclusion of the superiority of the half-life model, which is relatively simple to run, requires no prior forecasts of the economic independent variables and is parsimonious, impacts industry players largely affected by movements in exchange rates who require an efficient exchange rate forecasting model such as portfolio managers.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectHalf-life purchasing power parityen_US
dc.subjectForecasting precisionen_US
dc.subjectPortfolio-hybrid modelen_US
dc.titleForeign exchange rate modeling for the Kenyan market - half - life model approachen_US
dc.typeOtheren_US


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