scholarly journals A Study on the Volatility of the Bangladesh Stock Market — Based on GARCH Type Models

2017 ◽  
Vol 5 (3) ◽  
pp. 193-215 ◽  
Author(s):  
Bhowmik Roni ◽  
Chao Wu ◽  
Roy Kumar Jewel ◽  
Shouyang Wang
Keyword(s):  
High Risk ◽  
Stock Market ◽  
Risk Premium ◽  
Leverage Effect ◽  
Conditional Heteroskedasticity ◽  
Model Accuracy ◽  
Risk And Return ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Changes Over Time ◽  
Over Time

Abstract The generalized autoregressive conditional heteroskedasticity (GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant “low-yield associated with high-risk” phenomenon is detected in the crisis period and the “leverage effect” occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity (TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.

scholarly journals Spillover effects in the financial year cycle for Indian markets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Parul Bhatia
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Spillover Effects ◽  
Ordinary Least Squares ◽  
Leverage Effect ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Year Effect ◽  
Autoregressive Conditional Heteroskedasticity

PurposeThe stock market anomalies have been studied across the globe with intermingled results for individual markets. The present study has investigated the financial year effect for Indian stock markets by testing month-of-the-year-effect anomalies.Design/methodology/approachThe oldest stock exchange's index returns (Bombay Stock Exchange [BSE]) have been tested using ordinary least squares (OLS) and autoregressive conditional heteroskedasticity in mean (ARCH-M) models with Student's t and Student's t-fixed distributions for the period between 1991 and 2019. The Glosten, Jagannathan and Runkle-generalised autoregressive conditional heteroskedasticity (GJR-GARCH) model has been further used to find out existence of the leverage effect in returns.FindingsThe findings indicated no evidence for anomalies in the Indian stock market which may be used by investors for making unusual returns. However, the volatility in returns has shown weak but significant results due to the financial year impact. The leverage effect has not been found in the financial year cycle change over. The Indian market may be said to be moving towards a state of efficiency, leaving no scope for investors to gauge bizarre profits.Research limitations/implicationsThe study has incorporated the Indian context for testing anomalies during the start and end of the financial year cycle. The model may be extended further to developed and developing nations’ markets for testing efficiency in their stock markets during the same cycle.Originality/valueThe paper may be the first of its kind to test for the financial year effect on standalone basis for Indian markets. The paper also adds to the existing literature on testing events’ effect.

Clinical and dermatoscopic analysis of nevi’s changes over time in patients with a high risk of skin melanoma

2021 ◽  
Vol 20 (1) ◽  
pp. 72
Author(s):  
I.I. Vakhitova ◽  
A.V. Michenko ◽  
K.S. Titov ◽  
D.A. Synkova ◽  
N.N. Potekaev ◽  
...  
Keyword(s):  
High Risk ◽  
Skin Melanoma ◽  
Changes Over Time ◽  
Over Time

Volatility in the Indian Stock Market: Evidence from S&P CNX 500

2020 ◽  
Vol 17 (4) ◽  
pp. 1826-1830
Author(s):  
V. Shanthaamani ◽  
V. B. Usha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Conditional Variance ◽  
Leverage Effect ◽  
Volatility Clustering ◽  
Expected Stock Returns ◽  
High Volatility ◽  
Indian Stock Market ◽  
Daily Returns

This paper uses the Generalized Autoregressive Conditional Heteroskedastic models to estimate volatility (conditional variance) in the daily returns of the S&P CNX 500 index over the period from April 2007 to March 2018. The models include both symmetric and asymmetric models that capture the most common stylized facts about index returns such as volatility clustering and leverage effect. The empirical results show that the conditional variance process is highly persistent and provide evidence on the existence of risk premium for the S&P CNX 500 index return series which support the positive correlation hypothesis between volatility and the expected stock returns. Our findings also show that the asymmetric models provide better fit than the symmetric models, which confirms the presence of leverage effect. These results, in general, explain that high volatility of index return series is present in Indian stock market over the sample period.

scholarly journals Realized Measures to Explain Volatility Changes over Time

2020 ◽  
Vol 13 (6) ◽  
pp. 125
Author(s):  
Christos Floros ◽  
Konstantinos Gkillas ◽  
Christoforos Konstantatos ◽  
Athanasios Tsagkanos
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Leverage Effect ◽  
Frequency Data ◽  
Specific Group ◽  
Volatility Feedback ◽  
Changes Over Time ◽  
The Relationship ◽  
Over Time

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling.

Modeling volatility on the Karachi Stock Exchange, Pakistan

2016 ◽  
Vol 10 (3) ◽  
pp. 253-275 ◽  
Author(s):  
Shahan Akhtar ◽  
Naimat U. Khan
Keyword(s):  
Financial Crises ◽  
Stock Exchange ◽  
Small Sample ◽  
Leverage Effect ◽  
Data Series ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Modeling Volatility ◽  
The Impact

Purpose The current paper aims to fill a gap in the literature by analyzing the nature of volatility on the Karachi Stock Exchange (KSE) 100 index of the KSE, and develop an understanding as to which model is most suitable for measuring volatility among those used. The study contributes significantly to the literature as, compared with the limited previous studies of Pakistan undertaken in the past, it covers three types of data (i.e. daily, weekly and monthly) for the whole period from the introduction of the KSE 100 index on November 2, 1991 to December 31, 2013. In addition, to analyze the impact of global financial crises upon volatility, the data have been divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods. Design/methodology/approach This study has used an advanced set of volatility models such as autoregressive conditional heteroskedasticity [ARCH (1)], generalized autoregressive conditional heteroskedasticity [GARCH (1, 1)], GARCH in mean [GARCH-M (1, 1)], exponential GARCH [E-GARCH (1, 1)], threshold GARCH [T-GARCH (1, 1)], power GARCH [P-GARCH (1, 1)] and also a simple exponentially weighted moving average (EWMA) model. Findings The results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity and volatility clustering. However, the heteroskedasticity is absent only in the monthly returns making only the EWMA model usable to measure the volatility level in the monthly series. The P-GARCH (1, 1) model proved to be a better model for modeling volatility in the case of daily returns, while the GARCH (1, 1) model proved to be the most appropriate for weekly data based on the Schwarz information criterion (SIC) and log likelihood (LL) functionality. The study shows high persistence of volatility, a mean reverting process and an absence of a risk premium in the KSE market with an insignificant leverage effect only in the case of weekly returns. However, a significant leverage effect is reported regarding the daily series of the KSE 100 index. In addition, to analyze the impact of global financial crises upon volatility, the findings show that the subperiods demonstrated a slightly low volatility and the global economic crisis did not cause a rise in volatility levels. Originality/value Previously, the literature about volatility modeling in Pakistan’s markets has been limited to a few models of relatively small sample size. The current thesis has attempted to overcome these limitations and used diverse models for three types of data series (daily, weekly and monthly). In addition, the Pakistani economy has been beset by turmoil throughout its history, experiencing a range of shocks from the mild to the extreme. This paper has measured the impact of those shocks upon the volatility levels of the KSE.

scholarly journals The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and Its Extended Forms

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Liu ◽  
Zuoquan Zhang ◽  
Qin Zhao
Keyword(s):  
High Risk ◽  
Stock Market ◽  
Arma Model ◽  
Securities Market ◽  
High Yield ◽  
Leverage Effect ◽  
Stock Market Volatility ◽  
Extension Model ◽  
Modeling Analysis ◽  
Partial Autocorrelation Function

The proposed ARCH and its extension model have brought a powerful tool for the study of stock market volatility as well as verify that a “high risk brings high-yield” and the “leverage effect” of stock market. This paper gives modeling analysis by using the ARCH group models; in the last ten years Shanghai's index returns, concluded that there are significant “high-yield associated with high-risk” phenomenon and the “leverage effect” in the domestic securities market. The previous studies in fitting return series of ARMA models, mostly with low accuracy have a very subjective “observation autocorrelation and partial autocorrelation function method,” and even directly use “random walk” model. That will inevitably have some impact on the accuracy of the model. While this paper adopts the Pandit-Wu formulaic modeling method, the ARMA model is built on a strong theoretical foundation.

Forecasting developed and BRICS stock markets with cryptocurrencies and gold: generalized orthogonal generalized autoregressive conditional heteroskedasticity and generalized autoregressive score analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmed Jeribi ◽  
Achraf Ghorbel
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Value At Risk ◽  
Developed Countries ◽  
The Other ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Developed Stock Markets ◽  
Generalized Orthogonal

PurposeThe purpose of this paper is threefold. First, it models and forecasts the risk of the five leading cryptocurrencies, stock market indices (developed and BRICS) and gold returns. Second, it conducts different backtesting procedures forecasts. Third, it focuses on the hedging potential of cryptocurrencies and gold.Design/methodology/approachThe authors used the generalized autoregressive score (GAS) models to model and forecast the risk of cryptocurrencies, stock market indices and gold returns. They conduct different backtesting procedures of the 1% and 5%-value-at-risk (VaR) forecasts. They also use the generalized orthogonal generalized autoregressive conditional heteroskedasticity (GO-GARCH) model to explore the hedging potential of cryptocurrencies by estimating the dynamic conditional correlation between cryptocurrencies and gold, on the one hand, and stock markets on the other hand.FindingsWhen conducting different backtesting procedures of VaR, our finding suggests that Bitcoin has the highest VaR among cryptocurrencies and Gold and the BRICS indices returns have lower VaR compared to the developed countries. Finally, we provide evidence that the risks among developed stock markets can be hedged by Bitcoin and Gold. Bitcoin can be considered as the new Gold for these economies. Unlike Bitcoin, Gold can be considered as a hedge for Chinese and Indian investors. However, Gold and Bitcoin can be considered as diversifier assets for the other BRICS economies while Dash and Monero are diversifier assets for developed stock markets.Originality/valueThe first paper's empirical contribution lies in analyzing optimal forecast models for cryptocurrencies (other than Bitcoin) returns and risk. The second contribution consists of studying the hedging potential of five leading cryptocurrencies. To the best of our knowledge, no previous studies have investigated the role of cryptocurrencies for BRICS investors.

Detection of serum N-glycans based on changes over time for earlier detection of pancreatic cancer in high-risk individuals

Pancreatology ◽  
2021 ◽  
Vol 21 ◽  
pp. S12
Author(s):  
I. Levink ◽  
D. Klatte ◽  
R. Hanna-Sawires ◽  
I. Ibrahim ◽  
Y. van der Burgt ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
High Risk ◽  
Changes Over Time ◽  
Over Time
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