Does the introduction of stock index futures effectively reduce stock market volatility? Is the 'futures effect' immediate? Evidence from the Italian stock exchange using GARCH

2002 ◽  
Vol 12 (3) ◽  
pp. 183-192 ◽  
Author(s):  
Pierluigi Bologna ◽  
Laura Cavallo
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Market Volatility ◽  
Stock Index ◽  
Stock Index Futures ◽  
Stock Market Volatility ◽  
Index Futures

scholarly journals Stock Market Volatility, Recurrence Interval and Memory Effect

2017 ◽  
Vol 3 (3) ◽  
pp. 14
Author(s):  
Zhang Chi
Keyword(s):  
Stock Market ◽  
Extreme Events ◽  
Risk Estimation ◽  
Loss Probability ◽  
Recurrence Interval ◽  
Market Volatility ◽  
Stock Index ◽  
Extreme Value Statistics ◽  
Stock Index Futures ◽  
Stock Market Volatility

The stock market volatility in 2015 has caused serious damage to investors and the market has also suffered severely and immensely. During the last year, large volatility and extreme events were increasingly frequent than before, which made great theoretical and practical significance to gain a deep understanding of extreme value statistics of the volatility. Extreme events also bring huge risk to financial market, therefore the risk prevention, estimation and prediction are of necessity.The research uses 1-min high-frequency datasets of Shanghai 50 Stock Index Futuresin 2015. The data comes from Tongdaxin Database. The paper is the first one to study Shanghai 50 Stock Index Futures and a relationship between recurrence interval and risk estimation has been constructed. We find the recurrence interval of stock volatility can be fitted with stretched exponential function and the recurrence interval decreases when the threshold decreases. Then we demonstrate the existence of short-term and long-term correlations in recurrence intervals. We further construct a hazard function and define a loss probability to evaluate risk and find a crossover point in the loss probability plot. The study would enable one to improve risk estimation andthere are some shortcomings and need to be perfect in the future.

scholarly journals Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach

Risks ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 89
Author(s):  
Muhammad Sheraz ◽  
Imran Nasir
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Approximate Entropy ◽  
Market Volatility ◽  
Comparative Approach ◽  
Stock Market Volatility ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Garch Modeling

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.

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