scholarly journals Volatility dynamics of the Tunisian stock market before and during the COVID ‐19 outbreak: Evidence from the GARCH family models

2021 ◽  
Author(s):  
Mohamed Fakhfekh ◽  
Ahmed Jeribi ◽  
Marwa Ben Salem
Keyword(s):  
Stock Market ◽  
Family Models

scholarly journals Equity market volatility using garch models- evidence from Pakistan stock exchange (kse-100 index)

2016 ◽  
Vol 4 (2) ◽  
pp. 96 ◽  
Author(s):  
Muhammad Asif ◽  
Abdul Aziz
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Weak Form ◽  
Daily Basis ◽  
Market Volatility ◽  
Behavior Changes ◽  
Stock Market Returns ◽  
Market Index ◽  
Available Information ◽  
Family Models

Purpose – The purpose of this paper is to investigate the cluster volatility of return distribution in the Pakistan Stock exchange (PSX) formerly named Karachi stock exchange (KSE-100 Index). GARCH model for characterizing financial market volatility is discussed.Design/methodology/approach –This study used daily time series of the market index PSX (KSE-100) data over the period from January 1st, 2008 to December 31st, 2015, 1983 observations have been collected from KSE website.ARCH family models have been used, such as GARCH, EGARCH, PGARCH and TARCH models, to estimate cluster volatility. SIC, AIC, and Log likelihood have been used to select the appropriate model.Findings – GARCH 1,1 model is found the most appropriate model among ARCH family models. The outcome of this study indicates that the Pakistan Stock Exchange is weak-form efficient and explains cluster volatility and leptokurtic distribution.Research limitations/implications – Re-composing of Karachi stock exchange 100 index.Practical implications – Stock market returns' behavior changes according to daily basis available information, which is helpful for the investors to maximize their portfolio's return and managing the risk.Originality/value – Karachi stock market (KSE-100 Index) volatility from 2011 to 2015.

A Study on the Empirical Performance of the Volatility Estimation Models

2015 ◽  
Vol 23 (1) ◽  
pp. 73-97
Author(s):  
Jeehye Kim ◽  
Kook-Hyun Chang
Keyword(s):  
Stock Market ◽  
Jump Diffusion ◽  
Realized Volatility ◽  
Estimation Model ◽  
Jump Risk ◽  
Volatility Estimation ◽  
Korean Stock Market ◽  
Empirical Performance ◽  
Estimation Models ◽  
Family Models

In this paper, we examine which volatility estimation model best explains KOSPI200-realized volatility in the Korean stock market, which has both heteroscedasticity and jump risk. The sample covers from July 1, 2010 to July 31, 2014, which is a low-volatility period in Korean stock market by which time the effects of the global crisis had almost vanished. We use the intra-day return of KOSPI200, which has been measured by 5-minute intervals. This study finds GARCH-family models are efficient estimators compared to historical volatility and EWMA. Also, among the GARCH-family models, Jump-Diffusion GARCH has shown comparatively good results. Especially this study finds that VKOSPI200 is the most efficient model with the largest adj. R2 and the smallest evaluation statistics during the sample period. Meanwhile, it seems to be necessary to consider jump risk when we estimate volatility in Korean stock market.

Predicting Stock Market Performance

2020 ◽  
pp. 1-10
Author(s):  
Thomas Plieger ◽  
Thomas Grünhage ◽  
Éilish Duke ◽  
Martin Reuter
Keyword(s):  
Stock Market ◽  
Risk Taking ◽  
Life Stress ◽  
Price Volatility ◽  
Individual Characteristics ◽  
Self Report ◽  
Financial Behavior ◽  
Market Simulation ◽  
Stock Market Performance ◽  
Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

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