NYAMACHE, Makori Francis

NYAMACHE, Makori Francis

Student Bio

Francis M. Nyamache, (MSc, Actuarial Science), 2020 University of Nairobi. Francis comes from Nairobi Kenya. Francis has an unmatched interest in financial risk management. Co-authoring with his 4 colleagues, his undergraduate research project focused on Market Risk at the Nairobi Securities Exchange (NSE) 2017. His master thesis, “Range-Based Volatility Modelling and Forecasting Value-at-Risk,” demonstrates how range can be used to model and forecast volatility in the Kenyan market, and consequently other emerging markets. He hopes to continue his research focus on managing risk in the Kenyan market and emerging markets. Other areas of interest include computer and data science with the desire of blending data science and the actuarial field in his professional career.

Project Summary

Project Title: Range-Based Approach to Modelling and Forecasting Value-at-Risk

Project Abstract

The purpose of this thesis is to model and forecast value-at-risk based on range-measuring rather than the commonly acknowledged volatility models that are based on closing prices. The use of close-to-close prices in modelling and forecasting value-at-risk might not capture important intra-day information about the price movement. As a result, crucial price movement information is lost and consequently the model becomes less efficient. This thesis recommends the inclusion or range-measuring, described as the difference between the highest and lowest prices of an underlying stock within a time interval, a day, to compute Value-at-Risk. The project uses data of an NSE-listed and trading company, SASN, between November 2009 and November 2019 on which the predictability of range-based and close-to-close estimates was established. It was observed that the values obtained by range-based models were more accurate than when only the daily closing prices are used. The range-based models successfully capture dynamics of the volatility and achieves improve performance relative to the GARCH-type models. These findings are fairly consistent and can be extended to applications like portfolio optimization.