scholarly journals Volatility Forecasting Performance of Smooth Transition Exponential Smoothing Method: Evidence from Mutual Fund Indices in Malaysia

2021 ◽  
Vol 11 (10) ◽  
pp. 829-859
Author(s):  
Wan Cheong Kin ◽  
Choo Wei Chong ◽  
Annuar Md Nassir ◽  
Muzafar Shah Habibullah ◽  
Zulkornain Yusop
Keyword(s):  
Mutual Funds ◽  
Mutual Fund ◽  
Ad Hoc ◽  
Garch Model ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Smooth Transition ◽  
Volatility Forecasting ◽  
Forecasting Performance ◽  
Model Confidence Set

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.

Prediction on Fund Volatility Based on SVRGM-GARCH Model

2011 ◽  
Vol 403-408 ◽  
pp. 3763-3768
Author(s):  
Li Yan Geng ◽  
Yi Gang Liang
Keyword(s):  
Garch Model ◽  
Estimation Algorithm ◽  
Parameters Estimation ◽  
Support Vector ◽  
Volatility Forecasting ◽  
Grey Forecasting ◽  
Volatility Model ◽  
Vector Machines ◽  
Forecasting Performance ◽  
Random Error Term

GM-GARCH model is a new hybrid volatility model which integrates grey forecasting model (GM (1,1)) into GARCH model. As for the limitation of the parameters estimation algorithm of GM (1,1) model, a SVRGM-GARCH model is established to enhance volatility forecasting performance further. Firstly, support vector machines for regression (SVR) is utilized to estimate the parameters of GM (1,1) model (SVRGM). Then, the SVRGM model is used to modify the random error term sequence of GARCH model. An empirical research is performed on SSE Fund Index and SZSE Fund Index. The result shows that the SVRGM-GARCH model outperforms the GM-GARCH models and GARCH model, which indicates the model proposed in this study is an effective method for volatility forecasting.

The Study of the Determinants Affecting the Performance of Mutual Funds in Indiaequity Mutual Fund

2019 ◽  
Vol 54 (5) ◽  
pp. 58
Author(s):  
Preeta Sinha ◽  
Tamal Taru Roy ◽  
Debi Prasad Lahiri
Keyword(s):  
Mutual Funds ◽  
Mutual Fund

How Many Mutual Funds Constitute a Diversified Mutual Fund Portfolio?

CFA Digest ◽  
1997 ◽  
Vol 27 (4) ◽  
pp. 35-37
Author(s):  
John H. Earl
Keyword(s):  
Mutual Funds ◽  
Mutual Fund ◽  
Fund Portfolio

A Critical Analysis of Selected Public and Private Mutual Fund Schemes in India

2019 ◽  
Vol 118 (8) ◽  
pp. 28-34
Author(s):  
Dr. V. Murali Krishna ◽  
Dr T. Hima Bindu ◽  
Dr. Ravikumar Gunakala
Keyword(s):  
Mutual Funds ◽  
Capital Market ◽  
Mutual Fund ◽  
Portfolio Diversification ◽  
Financial Intermediaries ◽  
Public And Private ◽  
Fund Managers ◽  
Fixed Base ◽  
Banking Institutions ◽  
Different Levels

Mutual Fund Industry is one of the emerged dominant financial intermediaries in Indian Capital Market. The main objective of investing in a mutual fund is to diversify risk. Though the mutual fund invests in diversified portfolio, the fund managers take different levels of risk in order to achieve the schemes objectives. Mutual funds allow portfolio diversification and relative risk management through collection of funds from the savers/investors, the same investing in equity and debt stocks. This type of invested funds is managed by professional experts called as fund managers Funds are categorized as income should fixed base in India are a kind of mutual fund which makes investment in debt securities that have been issued to the corporate, banking institutions and to government in general

A Study on Investment Decision of Investors With Reference to Penetration of Mutual Funds in India

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Ratish C Gupta ◽  
Dr. Manish Mittal
Keyword(s):  
Decision Making ◽  
Real Estate ◽  
Mutual Funds ◽  
Capital Markets ◽  
Mutual Fund ◽  
Financial Services ◽  
Investment Decision ◽  
Fund Industry ◽  
Mutual Fund Industry ◽  
The Impact

The Indian mutual fund industry is one of the fastest growing and most competitive segments of the financial sector. The extent of under-penetration in the market is a sore point with the financial services industry, with a large amount of savings being channelized into fixed deposits, gold and real estate rather than the capital markets. The mutual fund industry is yet to spread its reach beyond Tier I cities. The top fifteen cities contribute to 85% of the pie, with the remaining 15% distributed among other cities. The study seeks to determine the impact of decision making of investors on current situation of mutual fund industry.

Robust Benchmarking of Indian Mutual Funds-A Partial Frontier Approach

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
Ram Pratap Sinha
Keyword(s):  
Decision Making ◽  
Performance Evaluation ◽  
Mutual Funds ◽  
Mutual Fund ◽  
Multi Criteria Decision Making ◽  
Risk Framework ◽  
Programming Techniques ◽  
Risk Free Rate ◽  
Risk Ratios ◽  
Free Rate

Performance analysis of mutual funds is usually made on the basis of return-risk framework. Traditionally, excess return (over risk-free rate) to risk ratios were used for the purpose mutual fund evaluation. Subsequently, the application of non-parametric mathematical programming techniques in the context of performance evaluation facilitated multi-criteria decision making. However,the estimates of performance on the basis of conventional programming techniques like DEA and FDH are affected by the presence of outliers in the sample observations. The present, accordingly uses more robust benchmarking techniques for evaluating the performance od sectoral mutual fund schemes based on observations for the second half of 2010. The USP of the present study is that it uses two partial frontier techniques (Order-m and Order- a) which are less susceptible to the problem of extreme data.

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