An Empirical Study on Volatility Spillover Effect among KOSPI200, Futures, ETFs Markets

2014 ◽  
Vol 22 (4) ◽  
pp. 675-697
Author(s):  
Seok-Kyu Kang ◽  
Youngtae Byun ◽  
Jonghae Park
Keyword(s):  
Data Analysis ◽  
High Frequency ◽  
Spillover Effect ◽  
High Frequency Data ◽  
Volatility Spillover ◽  
Frequency Data ◽  
Data Set ◽  
Underlying Asset ◽  
Daily Data ◽  
Volatility Spillover Effect

In this study we compared the effectiveness of different ETFs. For this purpose, we analyzed the volatility spillover effect (process) among KOSPI200, KOSPI200 futures and KOSPI200 ETFs such as KODEX200, KOSEF200, KINDEX200, TIGER200 using multi-variate GARCH model. The sample was generated from high frequency data set over the period from 05/24/2009 to 12/29/2011 (669 days). The volatility spillover effect was examined at 1, 5, 10, 30 minute' intervals for each market and the main results are as follows; First, KODEX200 has the highest correlations with KOSPI200 and KOSPI200 futures in four ETFs. Second, all ETFs have a cointegrated relationship with its underlying asset KOSPI200 as KOSPI200 and KOSPI200 futures do. Third, in the daily data the volatility spillover among ETFs, KOPSI200 and KOSPI200 futures was investigated in part but it was not consistent. The fourth, according to the result derived from high-frequency data analysis the volatility spillover effect from KODEX200 to KOSPI200 (KOSPI200 futures) is bigger than that from KOSPI200 (KOSPI200 futures) to KODEX200 while other ETFs are not. The overall results indicate that KODEX200 which is the biggest ETF in volume performs very important roles in finding the price of underlying asset and further researches can be expected.

Test for volatility spillover effects in Japan’s oil futures markets by a realized variance approach

2019 ◽  
Vol 36 (2) ◽  
pp. 224-239 ◽  
Author(s):  
Tadahiro Nakajima
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
High Frequency ◽  
High Frequency Data ◽  
Volatility Spillover ◽  
Frequency Data ◽  
Oil Futures ◽  
Content Type ◽  
Daily Data ◽  
Egarch Model

Purpose The purpose of this paper is twofold. First, the paper examines the risk transmission between crude oil and petroleum product prices of Japan’s oil futures market. Second, it compares the performance of two tests for Granger causality using realized variance (RV) and the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. Design/methodology/approach The author measures the daily RV of crude oil, kerosene and gasoline futures listed on the Tokyo Commodity Exchange using high-frequency data, and he examines the Granger causality in variance between these variables using the vector autoregression model. Further, the author estimates the EGARCH model based on daily data and test for Granger causality in variance between commodity futures using Hong’s (2001) approach. Findings The results of the RV approach reveal that the hypothesis on the existence of a mutual volatility spillover between crude oil and petroleum product markets is accepted. However, the results of the conventional approach indicate that all the hypotheses on Granger causalities in variance are rejected. The methodology based on intraday high-frequency data exhibits higher power than the conventional approach based on daily data. Originality/value This is the first paper to investigate Japan’s oil market using RV. The authors conclude that the approach based on RV is universally adoptable when testing for Granger causality in variance.

Stock market predictability

2014 ◽  
Vol 31 (4) ◽  
pp. 354-370 ◽  
Author(s):  
Silvio John Camilleri ◽  
Christopher J. Green
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Serial Correlation ◽  
Design Methodology ◽  
High Frequency Data ◽  
Frequency Data ◽  
Content Type ◽  
Lag Effects ◽  
Daily Data ◽  
Indian Stock Market

Purpose – The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices. Design/methodology/approach – The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency. Findings – The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market. Research limitations/implications – The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity. Originality/value – The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

scholarly journals ESTIMATING THE FRACTAL DIMENSION OF THE S&P 500 INDEX USING WAVELET ANALYSIS

2004 ◽  
Vol 07 (05) ◽  
pp. 615-643 ◽  
Author(s):  
ERHAN BAYRAKTAR ◽  
H. VINCENT POOR ◽  
K. RONNIE SIRCAR
Keyword(s):  
Fractal Dimension ◽  
Computational Complexity ◽  
Wavelet Analysis ◽  
High Frequency ◽  
High Frequency Data ◽  
Hurst Parameter ◽  
Frequency Data ◽  
Data Set ◽  
Time Period ◽  
Data Segment

S&P 500 index data sampled at one-minute intervals over the course of 11.5 years (January 1989–May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. An asymptotically unbiased and efficient estimator using the log-scale spectrum is employed. The estimator is asymptotically Gaussian and the variance of the estimate that is obtained from a data segment of N points is of order [Formula: see text]. Wavelet analysis is tailor-made for the high frequency data set, since it has low computational complexity due to the pyramidal algorithm for computing the detail coefficients. This estimator is robust to additive non-stationarities, and here it is shown to exhibit some degree of robustness to multiplicative non-stationarities, such as seasonalities and volatility persistence, as well. This analysis suggests that the market became more efficient in the period 1997–2000.

scholarly journals Robust Inference in Models Identified via Heteroskedasticity

2020 ◽  
pp. 1-45
Author(s):  
Daniel J. Lewis
Keyword(s):  
High Frequency ◽  
Simultaneous Equations ◽  
Robust Inference ◽  
High Frequency Data ◽  
Frequency Data ◽  
Monetary Policy Shocks ◽  
Weak Identification ◽  
Daily Data ◽  
Policy Shocks ◽  
Intraday Data

Identification via heteroskedasticity exploits variance changes between regimes to identify parameters in simultaneous equations. Weak identification occurs when shock variances change very little or multiple variances change close-toproportionally, making standard inference unreliable. I propose an F-test for weak identification in a common simple version of the model. More generally, I establish conditions for validity of non-conservative robust inference on subsets of the parameters, which can be used to test for weak identification. I study monetary policy shocks identified using heteroskedasticity in high frequency data. I detect weak identification, invalidating standard inference, in daily data, while intraday data provides strong identification.

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