Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market

Volatility forecasting has become a crucial process in risk management over recent decades. With the second largest stock market by market capitalization in 2019, China has gained increasing attention from recent research. This study aims at providing better volatility forecasts by investigating...

Full description

Saved in:

Bibliographic Details
Main Author:	Liu, Min
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Stock price forecasting Options (Finance) Investment analysis
Online Access:	http://psasir.upm.edu.my/id/eprint/105534/1/SPE%202022%2030%20UPM%20IR.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-upm-ir.105534
record_format	uketd_dc
spelling	my-upm-ir.1055342024-02-13T01:21:32Z Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market 2021-11 Liu, Min Volatility forecasting has become a crucial process in risk management over recent decades. With the second largest stock market by market capitalization in 2019, China has gained increasing attention from recent research. This study aims at providing better volatility forecasts by investigating the role of high-frequency data, distribution assumption and trading volume in volatility forecasting based on the China stock market. The behavior of high-frequency data in financial markets highly relates to market efficiency and information flow. The heterogeneous market hypothesis (HMH) is in response to the behavior of non-homogeneous market participants. In contrast to Efficient Market Hypothesis (EMH), HMH states that investors interpret information flow differently. Particularly, on a short-term basis, such as minute to minute, speculative behavior dominates the markets. In this regard, the study investigates the role of intraday data in volatility forecasting by using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Besides, regarding the non-normal distribution of financial time series, a variety of distribution assumptions are incorporated in application. Furthermore, to examine the role of trading volume in volatility forecasting and test the validity of two conflicting hypotheses: the Mixture of Distribution Hypothesis (MDH) and the Sequential Information Arrival Hypothesis (SIH), trading volume is regarded as both long-run and short-run predictors by this research. The considered methods contain the GARCH family model, the Heterogeneous Autoregressive (HAR) family model, the Smooth Transition Exponential Smoothing (STES), the Autoregressive Fractionally Integrated Moving Average (ARFIMA), and the GARCH-MIDAS model. In particular, in GARCH application, both intraday returns and daily returns are used and estimated under normal and non-normal distribution ii assumptions. The contributions of this study are that: (1) it provides clear evidence to support that the superiority of traditional time series models in volatility forecasting remains by taking advantage of high-frequency data; (2) it incorporates different distribution assumptions in GARCH models to capture the stylized facts of highfrequency data; (3) it makes the first attempt to evaluate the performance of STES in volatility forecasting by using RV as the proxy of actual volatility; (4) it provides a more consistent comparison to evaluate the forecasting ability of a mixed data sampling approach; (5) it extends the literature on the forecasting performance of trading volume to the GARCH-MIDAS approach. The empirical results show that: (1) data frequency in GARCH application substantially influence the accuracy of volatility forecasting, as the higher the frequency is of the return series, the better are the forecasts provided; (2) non-normal distributions are more capable at reproducing the stylized facts of both intraday and daily return series than normal distribution; (3) GARCH estimated by 5-min returns not only outperforms other GARCH alternatives, but also considerably beats RV-based models and STES at volatility forecasting; (4) no clear evidence appears that SIH holds in the China stock market; (5) GARCH-MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictors sampled at different frequencies. Stock price forecasting Options (Finance) Investment analysis 2021-11 Thesis http://psasir.upm.edu.my/id/eprint/105534/ http://psasir.upm.edu.my/id/eprint/105534/1/SPE%202022%2030%20UPM%20IR.pdf text en public doctoral Universiti Putra Malaysia Stock price forecasting Options (Finance) Investment analysis Choo, Wei-Chong
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Choo, Wei-Chong
topic	Stock price forecasting Options (Finance) Investment analysis
spellingShingle	Stock price forecasting Options (Finance) Investment analysis Liu, Min Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market
description	Volatility forecasting has become a crucial process in risk management over recent decades. With the second largest stock market by market capitalization in 2019, China has gained increasing attention from recent research. This study aims at providing better volatility forecasts by investigating the role of high-frequency data, distribution assumption and trading volume in volatility forecasting based on the China stock market. The behavior of high-frequency data in financial markets highly relates to market efficiency and information flow. The heterogeneous market hypothesis (HMH) is in response to the behavior of non-homogeneous market participants. In contrast to Efficient Market Hypothesis (EMH), HMH states that investors interpret information flow differently. Particularly, on a short-term basis, such as minute to minute, speculative behavior dominates the markets. In this regard, the study investigates the role of intraday data in volatility forecasting by using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Besides, regarding the non-normal distribution of financial time series, a variety of distribution assumptions are incorporated in application. Furthermore, to examine the role of trading volume in volatility forecasting and test the validity of two conflicting hypotheses: the Mixture of Distribution Hypothesis (MDH) and the Sequential Information Arrival Hypothesis (SIH), trading volume is regarded as both long-run and short-run predictors by this research. The considered methods contain the GARCH family model, the Heterogeneous Autoregressive (HAR) family model, the Smooth Transition Exponential Smoothing (STES), the Autoregressive Fractionally Integrated Moving Average (ARFIMA), and the GARCH-MIDAS model. In particular, in GARCH application, both intraday returns and daily returns are used and estimated under normal and non-normal distribution ii assumptions. The contributions of this study are that: (1) it provides clear evidence to support that the superiority of traditional time series models in volatility forecasting remains by taking advantage of high-frequency data; (2) it incorporates different distribution assumptions in GARCH models to capture the stylized facts of highfrequency data; (3) it makes the first attempt to evaluate the performance of STES in volatility forecasting by using RV as the proxy of actual volatility; (4) it provides a more consistent comparison to evaluate the forecasting ability of a mixed data sampling approach; (5) it extends the literature on the forecasting performance of trading volume to the GARCH-MIDAS approach. The empirical results show that: (1) data frequency in GARCH application substantially influence the accuracy of volatility forecasting, as the higher the frequency is of the return series, the better are the forecasts provided; (2) non-normal distributions are more capable at reproducing the stylized facts of both intraday and daily return series than normal distribution; (3) GARCH estimated by 5-min returns not only outperforms other GARCH alternatives, but also considerably beats RV-based models and STES at volatility forecasting; (4) no clear evidence appears that SIH holds in the China stock market; (5) GARCH-MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictors sampled at different frequencies.
format	Thesis
qualification_level	Doctorate
author	Liu, Min
author_facet	Liu, Min
author_sort	Liu, Min
title	Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market
title_short	Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market
title_full	Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market
title_fullStr	Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market
title_full_unstemmed	Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market
title_sort	role of high-frequency data, distribution assumption and trading volume in volatility forecasting in china stock market
granting_institution	Universiti Putra Malaysia
publishDate	2021
url	http://psasir.upm.edu.my/id/eprint/105534/1/SPE%202022%2030%20UPM%20IR.pdf
_version_	1794018906154729472

Role of high-frequency data, distribution assumption and trading volume in volatility forecasting in China stock market

Similar Items