Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market

Wang, Yuling; Xiang, Yunshuang; Lei, Xinyu; Zhou, Yucheng

doi:10.1080/1331677x.2021.1967771

Cited by 22 publications

(8 citation statements)

References 37 publications

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“…GARCH models can estimate the trend of volatility clustering by considering conditional variance in estimating time-varying volatility. In the GARCH model, conditional variance depends on the square of the error terms' past values and the past conditional depends on variances (Wang et al ., 2022).…”

Section: Methodsmentioning

confidence: 99%

The impact of unproved reserve news on the energy stock volatility: an empirical investigation on Turkey

Arzova

Koy

Şahin

2023

RBF

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PurposeThis study investigates the effect of unproven energy reserve news on the volatility of energy firms' stocks. Thus, investors' perception of unproven energy reserves is revealed. Additionally, the study aims to determine whether the effect of the news changes according to time and volatility level.Design/methodology/approachThe general autoregressive conditional heteroskedasticity (GARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models consist of the energy reserve exploration news in Turkey for the period 2009–2022 and the volatility of 14 energy stocks.FindingsThe results indicate energy exploration news's negative and significant effect on volatility. According to empirical results, energy stock volatility is most affected in the first ten days. Besides, the results show that the significant models of energy reserve news in low-volatility stocks are proportionally higher than in high-volatility stocks.Research limitations/implicationsOnly unproved reserve news is included in the analysis, as sufficient confirmed reserves could not be reached during the sampling period. Further studies can compare proven and unproved reserve news effects. Additionally, a similar analysis can be conducted between Turkey and another country with a similar socio-economic character to examine different investor behaviors.Practical implicationsThis research includes indications on managing investors' reactions to unproven energy reserve news.Originality/valueThis study contributes to the literature by analyzing unproven reserves. Contrary to previous studies, examining stock volatility also makes the study unique.

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Section: Methodsmentioning

confidence: 99%

The impact of unproved reserve news on the energy stock volatility: an empirical investigation on Turkey

Arzova

Koy

Şahin

2023

RBF

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“…By distinguishing these volatility components, the csGARCH model offers a nuanced analysis of persistent and ephemeral market changes. Its ability to capture the complex interplay and fluctuations in financial data significantly enhances our understanding of the underlying forces driving financial markets (Wang et al 2022;Chu et al 2017;Engle and Lee 1999). The csGARCH model is mathematically formulated as:…”

Section: Csgarch (11) Var: Unveiling Long-term and Short-term Volatil...mentioning

confidence: 99%

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

Likitratcharoen,

Suwannamalik

2024

Risks

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The Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of potential investment losses. Predominantly employed within financial sectors, it aids in adhering to regulatory mandates and in devising capital reserve strategies. Nonetheless, the predictive precision of VaR models frequently faces scrutiny, particularly during crises and heightened uncertainty phases. Phenomena like volatility clustering impinge on the accuracy of these models. To mitigate such constraints, conditional volatility models are integrated to augment the robustness and adaptability of VaR approaches. This study critically evaluates the efficacy of GARCH-type VaR models within the transportation sector amidst the Thai stock market’s volatility during the COVID-19 pandemic. The dataset encompasses daily price fluctuations in the Transportation Sector index (TRANS), the Service Industry index (SERVICE), and 17 pertinent stocks within the Stock Exchange of Thailand, spanning from 28 December 2018 to 28 December 2023, thereby encapsulating the pandemic era. The employed GARCH-type VaR models include GARCH (1,1) VaR, ARMA (1,1)—GARCH (1,1) VaR, GARCH (1,1)—M VaR, IGARCH (1,1) VaR, EWMA VaR, and csGARCH (1,1) VaR. These are juxtaposed with more traditional, less computationally intensive models like the Historical Simulation VaR and Delta Normal VaR. The backtesting methodologies encompass Kupiec’s POF test, the Independence Test, and Christoffersen’s Interval Forecast test. Intriguingly, the findings reveal that the Historical Simulation VaR model surpasses GARCH-type VaR models in failure rate accuracy. Within the GARCH-type category, the EWMA VaR model exhibited superior failure rate accuracy. The csGARCH (1,1) VaR and EWMA VaR models emerged as notably robust. These findings bear significant implications for managerial decision-making in financial risk management.

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“…The increased volatility (fluctuation) of the exchange rate is a signal for investors to withdraw their funds from the foreign exchange market because it is considered to reduce their returns. In the end, investors will switch to other investments that provide higher returns (Almansour, 2021;Fakhfekh et al, 2021;Nugroho et al, 2021;Wang et al, 2021).…”

Section: Msci India Islamic Index (Msini)mentioning

confidence: 99%

Sensitivity Islamic Stock Return in Asia: The Effect of Exchange Rate Volatility

Dewanti

Rusmita

Samad³

2022

JEBIS

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The current study focuses on the existence of exchange rate volatility that impacts the return of MSCI Islamic stocks in Asia emerging countries. The study also sees comparisons between Islamic emerging markets in Asia (India, China, and Korea) and Southeast Asia regions (Indonesia, Malaysia, Thailand, and the Philippines). This study's daily time series data is from January 2015 to June 2020, employing time series regression as an analysis technique. The EGARCH method is used to indicate the asymmetric model and examines the impact of both good and bad shocks generated. The results highlight that the volatility of the MSINI is statistically positive, whereas MSCNI and MSKRI show a significant adverse effect on volatility. On the other hand, Southeast Asia (MSIDI, MSMYI, MSTHI, and MSPH) has no significant effect on exchange volatility. It is interpreted that the currencies of Southeast Asian countries are not strong enough because they still depend on other exchange rates. This result can be utilized to invest in the Islamic stock market in Southeast Asia because it is not affected by exchange rate volatility. Investors can quickly react to sensitive stocks to avoid uncertainty in stock returns. The government must be advised to increase the fiscal stimulus space and provide ease of business in the real sector.

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Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market

Cited by 22 publications

References 37 publications

The impact of unproved reserve news on the energy stock volatility: an empirical investigation on Turkey

The impact of unproved reserve news on the energy stock volatility: an empirical investigation on Turkey

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

Sensitivity Islamic Stock Return in Asia: The Effect of Exchange Rate Volatility

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