An analytical approximation of option prices via TGARCH model

Hongwiengjan, Warunya; Thongtha, Dawud

doi:10.1080/1331677x.2020.1805636

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…They revealed that the influence of social variables is weaker than that of financial variables (L opez-Cabarcos et al, 2020). Hongwiengjan and Thongtha (2021) assessed an analytical approximation of option prices using the TGARCH model. For empirical practice, a new efficient method for pricing options in the case of an in-the-money (ITM) option is provided.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Wang

Xiang

Lei

et al. 2021

Economic Research-Ekonomska Istraživanja

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Volatility is integral for the financial market. As an emerging market, the Chinese stock market is acutely volatile. In this study, the data of the Shanghai Composite Index and Shenzhen Component Index returns were selected to conduct an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. We established the autoregressive moving average (ARMA)-GARCH model with t-distribution for the sample series to compare model effects under different distributions and orders. In contrast, we proposed threshold-GARCH (TGARCH) and exponential-GARCH (EGARCH) models to capture the features of the index. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE) and rootmean-squared error (RMSE). The results denote that the ARMA (4,4)-GARCH (1,1) model under Student's t-distribution outperforms other models when forecasting the Shanghai Composite Index return series. For the return series of the Shenzhen Component Index, ARMA(1,1)-TGARCH(1,1) display the best forecasting performance among all models. This study could provide an effective information reference for the macro decision-making of the government, the operation of listed companies and investors' investment decision-making.

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Section: Literature Reviewmentioning

confidence: 99%

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

Wang

Xiang

Lei

et al. 2021

Economic Research-Ekonomska Istraživanja

View full text Add to dashboard Cite

show abstract

“…According to Ausloos et al (2020), spot price variability of index futures has been argued out as optimally examined using the TGARCH modelling technique. Hongwiengjan and Thongtha (2021.) also reference the literature to differentiate TGARCH from other asymmetric GARCH techniques such as the EGARCH and GJR-GARCH by positing that the TGARCH focuses on standard deviation (to approximate stock index volatility) as against conditional variance for the other variants. Again, and unlike other studies, we examined if there existed autocorrelation in the return of stocks on the GSE, for how long, and at what time such autocorrelations die out in the stock market.…”

Section: Introductionmentioning

confidence: 99%

Commodity price volatility and stock market returns in an emerging economy

et al. 2022

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The paper examined commodity price volatility and stock returns on the Ghana Stock Exchange (GSE). Utilising Threshold Generalised Autoregressive Conditional Heteroskedasticity (TGARCH) estimation technique, the study also evaluated the likely leverage effect on the stock market and further interrogated if autocorrelation existed in the daily stock returns series, for how many days and on what day does it disappear by specifying an autoregressive model as a mean equation. The study concludes that changes in commodity prices and their volatilities on the international market exert a significant level of diverse impact on daily stock returns and return volatility on the GSE. For instance, whilst coffee price changes result in reduction in daily stock returns, changes in crude and gold prices result in an increment in returns on the GSE. The study also confirms the existence of autocorrelation in the daily stock returns for up to seven days, fading out on the eighth day as well as the existence of the leverage effect on the GSE. This result should influence investment decisions in respect to trading on the GSE. Observance of changes in price of these key commodities could enhance predictions and lead to the formulation of strategies to maximise revenues on the GSE 1

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“…In risk management, financial institutions often use the volatility of return to measure risk. In asset pricing, the option price depends on a precise forecast of the underlying asset volatility (Hongwiengjan & Thongtha, 2021). In optimal management of asset portfolios, volatility is often used as an indicator to construct optimal portfolio weights.…”

Section: Introductionmentioning

confidence: 99%

Modelling returns volatility: mixed-frequency model based on momentum of predictability

Chen

Shang

2022

Economic Research-Ekonomska Istraživanja

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The estimation and prediction of financial asset volatility are important in terms of theoretical and practical applications. Considering that low-frequency and high-frequency information plays an important role in volatility prediction, this article proposes a mixed-frequency model based on the momentum of predictability (MF-MoP). To illustrate the advantages of the proposed model, comparative research is conducted on the prediction accuracy of volatility among the GARCH model, the Realized GARCH model and the MF-MoP model, by the loss function and MCS test. The empirical results show that the MF-MoP model has higher prediction accuracy than the other two models; especially based on skewed-t distribution, the MF-MoP significantly outperforms the competing models. Moreover, the MF-MoP model can improve the forecasting of volatility, regardless of different lookback periods (including 1, 3, 6 and 9 days), different data (including the CSI 300 index, the N225 index and the KS11 index), and realized measures (including RV, RRV and MedRV), indicating that the model is robust.

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An analytical approximation of option prices via TGARCH model

Cited by 4 publications

References 24 publications

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

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

Commodity price volatility and stock market returns in an emerging economy

Modelling returns volatility: mixed-frequency model based on momentum of predictability

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