Open AccessVolatility Forecasting Performance of Smooth Transition Exponential Smoothing Method: Evidence from Mutual Fund Indices in Malaysia
Author(s) -
Cheong Kin Wan,
Choo Wei Chong,
Annuar Nassir,
Muzafar Shah Habibullah,
Zulkornain Yusop
Publication year - 2021
Publication title -
asian economic and financial review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.215
H-Index - 10
eISSN - 2305-2147
pISSN - 2222-6737
DOI - 10.18488/journal.aefr.2021.1110.829.859
Subject(s) - econometrics , autoregressive conditional heteroskedasticity , exponential smoothing , volatility (finance) , economics , mutual fund , stochastic volatility , stylized fact , implied volatility , financial models with long tailed distributions and volatility clustering , forward volatility , financial economics , finance , macroeconomics
This paper aims to empirically compare the performance of the smooth transition exponential smoothing (STES) method against the well-known generalized autoregressive conditional heteroskedasticity (GARCH) model in one-step-ahead volatility forecasting. While the GARCH model captured most of the stylized facts of the financial time series, threats of outliers in the leptokurtic distributed series remain unresolved. The study compared volatility forecasting performance of a total of 22 models and methods comprising STES, GARCH, and some ad-hoc forecasting. The daily returns of seven mutual fund indices (derived from 57 individual equity mutual funds) under two different economic conditions (sub-periods) were applied across all competing models. Findings revealed that the STES method with error and absolute error as transition variables emerged as the best post-sample volatility forecasting model in both sub-periods with and without financial crisis impact, as verified by model confidence set (MCS) procedure. The implications based on the results are: (1) both the sign and size of yesterday’s news shock have an impact on today’s volatility; (2) the STES method is resilient to outliers, and hence superior to GARCH and other volatility forecasting approaches examined. This study contributes an empirical approach in forecasting the risk of mutual funds investment for investors and fund managers, as well as extending the scope of volatility forecasting literature into the less explored mutual funds.