Volatility Forecasting in Emerging Markets
Kontsas, Emma Katariina (2020-12-20)
20.12.2020
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20201220101569
https://urn.fi/URN:NBN:fi-fe20201220101569
Tiivistelmä
This thesis examines the forecasting accuracy of implied volatility and GARCH(1,1) model volatility in the context of emerging equity markets. As a measure of risk volatility is a key factor in risk management and investing. Financial markets have become more global and the importance of volatility forecasting in emerging markets has increased. Emerging equity markets have more different risks than developed stock markets. As risk affects the potential return it is important to test and study how volatility models are able to forecast future volatility in emerging markets. The purpose of this thesis is to study the forecasting abilities and limitations of option implied volatility and GARCH(1,1) in the riskier emerging market environment.
The majority of previous studies on volatility forecasting are focused on developed markets. Previous results suggest that in developed equity markets implied volatility provides an accurate short-term future volatility forecast whereas GARCH models offer a better long-term volatility forecast. The previous results in emerging market context have been in rather inconclusive. However, there is more evidence of GARCH(1,1) volatility being the most accurate future volatility forecaster. The main motivation behind this thesis is to examine which models is best suited for volatility forecasting in emerging equity markets.
The forecasting accuracy of option implied volatility and GARCH(1,1) volatility is tested with an OLS regression model. The data consist of MSCI Emerging Market Price index data and corresponding option data from 1.1.2015 to 31.12.2019. In this thesis the daily closing prices of the index and option are used to compute daily and monthly implied volatility and GARCH(1,1) model volatility forecasts. Loss functions are applied to test the fit of the models.
The results suggest that both models contain information about one-day future volatility as the explanatory power of both models is statistically significant for daily and monthly forecasts. The GARCH(1,1) volatility is a more accurate future volatility estimate than implied volatility for both daily and monthly volatilities. The monthly volatility forecast is more accurate for both models than the daily forecast. The results indicate that in both daily and monthly values GARCH(1,1) volatility is a more accurate estimate for future volatility than implied volatility. The GARCH(1,1) monthly volatility offers the best fit for future volatility with the highest predictive power and lowest error measures, suggesting that it is the most appropriate fit for future volatility forecasting in emerging equity markets.
The majority of previous studies on volatility forecasting are focused on developed markets. Previous results suggest that in developed equity markets implied volatility provides an accurate short-term future volatility forecast whereas GARCH models offer a better long-term volatility forecast. The previous results in emerging market context have been in rather inconclusive. However, there is more evidence of GARCH(1,1) volatility being the most accurate future volatility forecaster. The main motivation behind this thesis is to examine which models is best suited for volatility forecasting in emerging equity markets.
The forecasting accuracy of option implied volatility and GARCH(1,1) volatility is tested with an OLS regression model. The data consist of MSCI Emerging Market Price index data and corresponding option data from 1.1.2015 to 31.12.2019. In this thesis the daily closing prices of the index and option are used to compute daily and monthly implied volatility and GARCH(1,1) model volatility forecasts. Loss functions are applied to test the fit of the models.
The results suggest that both models contain information about one-day future volatility as the explanatory power of both models is statistically significant for daily and monthly forecasts. The GARCH(1,1) volatility is a more accurate future volatility estimate than implied volatility for both daily and monthly volatilities. The monthly volatility forecast is more accurate for both models than the daily forecast. The results indicate that in both daily and monthly values GARCH(1,1) volatility is a more accurate estimate for future volatility than implied volatility. The GARCH(1,1) monthly volatility offers the best fit for future volatility with the highest predictive power and lowest error measures, suggesting that it is the most appropriate fit for future volatility forecasting in emerging equity markets.