Value at risk estimation of the SET50 index: Comparison between stock exchange of Thailand and Thailand futures exchange

Jongadsayakul, Woradee

doi:10.14254/2071-8330.2021/14-1/16

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…If the personal tax rate for income received in the form of dividends is greater than the personal income tax rate for capital gains, then shareholders will prefer the company not to pay dividends. They will be more profitable if the funds remaining with the company are paid for through repurchases of outstanding shares (Jamei, 2017;Jongadsayakul, 2021;Nurohman et al, 2016;Roscoe, 2022;Widuri et al, 2019). A company's stock repurchase can be seen as another form of dividend, because both are the company's main way of distributing the cash it already has.…”

Section: Introductionmentioning

confidence: 99%

The Effect Of Tax Rates And Dividend Policy On Share Price In The Banking Sector Listed On The Indonesia Stock Exchange For The 2017-2019 Period

Lestari,

Abubakar

2023

Economics

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One of the main types of investment that promises substantial returns for investors is investing in stocks. However, investing in stocks also carries risks. This dividend policy is a decision in determining the largest profit contribution with the lowest investment risk. The amount of tax rate that will be borne will affect the rise and fall of share prices in each company. Based on the results of the simultaneous regression test involving the independent variables Tax Rate and Dividend Policy on the dependent variable Stock Price, it was found that the f-count value is greater than the f-table with a significance level of F less than 0.05. This shows that the Tax Rate and Dividend Policy variables together have a significant effect on the Share Price variable.

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Section: Introductionmentioning

confidence: 99%

The Effect Of Tax Rates And Dividend Policy On Share Price In The Banking Sector Listed On The Indonesia Stock Exchange For The 2017-2019 Period

Lestari,

Abubakar

2023

Economics

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“…In general, a GARCH (1,1) model and its TARCH (1,1) and EGARCH (1,1) extensions are often sufficient for the empirical modelling of financial time series (see for example, Lim & Sek, 2013;Zabiulla, 2015;Ausloos et al, 2020;Jongadsayakul, 2021;Wang, 2021). To capture the effect of night trading session on the USD Futures volatility, all models have a dummy variable (NTt) in the conditional variance equation.…”

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confidence: 99%

Impact of night trading sessions on volatility of USD futures market in Thailand

Jongadsayakul

2024

Journal of International Studies

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Prior to September 2021, USD Futures could only be traded during the regular hours in Thailand Futures Exchange (TFEX). An additional trading session at night, while trading in both London and New York exchanges is active, enables investors to better handle their foreign exchange exposure or speculative needs of the moment. However, understanding volatility behavior is crucial for achieving successful trading. Therefore, this study investigates the impact of night trading sessions on the USD Futures volatility using GARCH family models. The USD Futures market volatility is examined through comparative analysis before and after the introduction of the night trading session. Both TARCH and EGARCH models have revealed no existence of leverage effect over the sample period - from January 2, 2020 to December 30, 2022. The GARCH model has proved to be the most accurate model for describing USD Futures volatility. Following the launch of nighttime trading, USD Futures market has experienced higher and more persistent volatility. In response to an increase in the volatility of USD Futures, TFEX should increase its margin requirement and monitor the speculative movements in futures market for their possible destabilizing effect. Investors should also adjust their hedge ratio to manage risk more appropriately and incorporate an extended period of increased uncertainty into their trading strategies.

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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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Value at risk estimation of the SET50 index: Comparison between stock exchange of Thailand and Thailand futures exchange

Cited by 3 publications

References 28 publications

The Effect Of Tax Rates And Dividend Policy On Share Price In The Banking Sector Listed On The Indonesia Stock Exchange For The 2017-2019 Period

The Effect Of Tax Rates And Dividend Policy On Share Price In The Banking Sector Listed On The Indonesia Stock Exchange For The 2017-2019 Period

Impact of night trading sessions on volatility of USD futures market in Thailand

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

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