Investors' trading behaviour and stock market volatility during crisis periods: A dual long‐memory model for the Korean Stock Exchange

Caporale, Guglielmo Maria; Karanasos, Menelaos; Yfanti, Stavroula; Kartsaklas, Aris

doi:10.1002/ijfe.2024

Cited by 18 publications

(12 citation statements)

References 92 publications

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“…First, taking advantage of the demand for cheap, labour-intensive and later for capital-intensive goods, its economic growth has 1 https://www.economywatch.com/economic-statistics/Korea/Output_Gap_Percent_of_Potential_GDP/ been export-driven. The case of South Korea captures the attention of researchers with its significant economic performance improvement and much greater capital inflows since 1996 [1]. Korean stock market is considered one of the fastest-growing [2].…”

Section: An Overview Of the South Korean Marketmentioning

confidence: 99%

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Foreign Ownership and Capital Structure in Times of Crises: Case of South Korea

Исламова¹,

Кокорева²

2022

КФ

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This paper presents the results of analysis of the role of foreign investors in capital structure choice. The analysis is conducted using regression analysis of panel data. Operating with data of 809 companies listed in the South Korean market in 1997-2020, we demonstrate that the debt-to-equity ratio of companies with foreign investors is lower than in the rest of thefirms. In this case, foreign investors’ monitoring of a company’s performance can be a substitute for debt financing as a disciplining mechanism for management. Foreign investors also indirectly affect the capital structure by impacting the speed of adjustment to the target leverage. The results of our study show that the speed of adjustment to the target debt-to-equity ratio increases along with a rise in foreign investor participation. The results are most pronounced for over-leveraged firms. Whereas during financial crises, we observe no effect of the presence of foreign investors on the changes in the target debt-to-equity ratio, we find a significant increase in the speed of adjustment to the target leverage in companies withforeign investors. During the current coronavirus crisis, corporate debt of companies with foreign investors is decreasing even more, while the speed of adjustment decreases. We attribute these effects to the non-financial nature of the crisis, which makes the role of foreign investors in monitoring management less meaningful. In practice, results can be used bycompany managers when facing crises or other macroeconomic shocks. The results obtained can also be used by public authorities in shaping policies to stimulate foreign investment.

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Section: An Overview Of the South Korean Marketmentioning

confidence: 99%

“…The country was one of the few countries that successfully overcame the 2008 crisis and quickly pulled out of the recession. According to IMF data, while many OECD economies are still experiencing the consequences of that recession, South Korea entered a positive output gap by 2010 1 .…”

Section: An Overview Of the South Korean Marketmentioning

confidence: 99%

Foreign Ownership and Capital Structure in Times of Crises: Case of South Korea

Исламова¹,

Кокорева²

2022

КФ

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“…Estimation of d in financial data series is different from 0 and 0.5. Caporale [40] pointed out the presence of long-term memory in the US Stock Exchange. The parametric estimation of ARFIMA process for the FFC company is shown in Table 3.…”

Section: B Arfima Modelmentioning

confidence: 99%

Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting

et al. 2020

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Forecasting of fast fluctuated and high-frequency financial data is always a challenging problem in the field of economics and modelling. In this study, a novel hybrid model with the strength of fractional order derivative is presented with their dynamical features of deep learning, long-short term memory (LSTM) networks, to predict the abrupt stochastic variation of the financial market. Stock market prices are dynamic, highly sensitive, nonlinear and chaotic. There are different techniques for forecast prices in the time-variant domain and due to variability and uncertain behavior in stock prices, traditional methods, such as data mining, statistical approaches, and non-deep neural networks models are not suited for prediction and generalized forecasting stock prices. While autoregressive fractional integrated moving average (ARFIMA) model provides a flexible tool for classes of long-memory models. The advancement of machine learningbased deep non-linear modelling confirms that the hybrid model efficiently extracts profound features and model non-linear functions. LSTM networks are a special kind of recurrent neural network (RNN) that map sequences of input observations to output observations with capabilities of long-term dependencies. A novel ARFIMA-LSTM hybrid recurrent network is presented in which ARFIMA model-based filters having the linear tendencies better than ARIMA model in the data and passes the residual to the LSTM model that captures nonlinearity in the residual values with the help of exogenous dependent variables. The model not only minimizes the volatility problem but also overcome the over fitting problem of neural networks. The model is evaluated using PSX company data of the stock market based on RMSE, MSE and MAPE along with a comparison of ARIMA, LSTM model and generalized regression radial basis neural network (GRNN) ensemble method independently. The forecasting performance indicates the effectiveness of the proposed AFRIMA-LSTM hybrid model to improve around 80% accuracy on RMSE as compared to traditional forecasting counterparts.

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“…Considerable evidence has been found based on the analysis of long memory and volatility of stock returns [42] - [45]. Bentes et al [37] looked at long memory and volatility clustering for the S & P500, NASDAQ100, and Stoxx 50 indexes to compare the US and European markets and realize that they might better understand the degree of volatility clustering and long-memory effects by evaluating the entropy values for various evenly spaced sub-periods, which is something that will be explored further.…”

Section: Long Memory In Volatilitymentioning

confidence: 99%

Modeling Asymmetric Effects and Long Memory in Conditional Volatility of Dhaka Stock Exchange: New Evidence from Family of FIGARCH Models

Haque¹,

Farzana²

2021

ujaf

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This paper investigates modeling the conditional volatility of the Bangladesh equity market, namely the Dhaka Stock Exchange benchmark index (DSEX) and the Shariah Index (DSES), to explore the presence of leverage effects and long memory behavior covering the period from July 01, 2004 to December 31, 2020. We employ a family of Fractionally Integrated GARCH models FIFARCH BBM, FIGARCH CHUNG, FIEGARCH, FIAPARCH BBM, FIAPARCH CHUNG, and HYGARCH to capture both asymmetric effects and long memory behavior in conditional variance, a unique study in volatility literature. We detect strong evidence in favor of asymmetric effects and long memory behavior in the conditional volatility of DSEX and DSES returns, which repudiates the weak-form efficient market hypothesis. The study reveals FIEGARCH and FIAPRACH CHUNG outperform the other fractionally integrated GARCH specifications in modeling conditional volatility of equity returns. The paper further examines the diagnostic test of misspecification of the conditional variance equation based on the news impact curve, and results ensure that all models are fairly specified. This study also looks into the risk-return tradeoff in time-varying volatility and finds no evidence of the positive relationship between equity returns and volatility dynamics. The findings have pragmatic implications for retail and wholesale investors and other market players to initiate the investments and hedging strategies before investing in an emerging equity market like Bangladesh.

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Investors' trading behaviour and stock market volatility during crisis periods: A dual long‐memory model for the Korean Stock Exchange

Cited by 18 publications

References 92 publications

Foreign Ownership and Capital Structure in Times of Crises: Case of South Korea

Foreign Ownership and Capital Structure in Times of Crises: Case of South Korea

Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting

Modeling Asymmetric Effects and Long Memory in Conditional Volatility of Dhaka Stock Exchange: New Evidence from Family of FIGARCH Models

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