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 S&P 500 Index Price Spillovers around the COVID-19 Market Meltdown

2021 ◽  
Vol 14 (7) ◽  
pp. 330
Author(s):  
Camillo Lento ◽  
Nikola Gradojevic
Keyword(s):  
Exchange Rate ◽  
Financial Markets ◽  
Frequency Domain ◽  
High Frequency ◽  
Spillover Effects ◽  
High Frequency Data ◽  
Frequency Data ◽  
Data Set ◽  
Time Period ◽  
Price Movements

This paper explores price spillover effects around the COVID-19 pandemic market meltdown between the S&P 500 index, five other financial markets, and the VIX. Frequency domain causalities are estimated for the January–May 2020 time period on a high-frequency data set at five-minute intervals. The results reveal that price movements in the S&P 500 generally caused price movements in other financial markets before the market meltdown; however, a large number of bi-directional causalities emerged during the market meltdown. During the market recovery, S&P 500 price movements were more likely to be caused by other financial markets’ price movements. The VIX, exchange rate, and gold returns had the most prominent influence on the S&P 500 returns in the market recovery.

FRACTAL STRUCTURE OF FINANCIAL HIGH FREQUENCY DATA

Fractals ◽  
2002 ◽  
Vol 10 (01) ◽  
pp. 13-18 ◽  
Author(s):  
YOSHIAKI KUMAGAI
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Transaction Costs ◽  
Time Scale ◽  
High Frequency ◽  
Fractal Structure ◽  
Extreme Values ◽  
High Frequency Data ◽  
Frequency Data ◽  
Time Intervals

We propose a new method to describe scaling behavior of time series. We introduce an extension of extreme values. Using these extreme values determined by a scale, we define some functions. Moreover, using these functions, we can measure a kind of fractal dimension — fold dimension. In financial high frequency data, observations can occur at varying time intervals. Using these functions, we can analyze non-equidistant data without interpolation or evenly sampling. Further, the problem of choosing the appropriate time scale is avoided. Lastly, these functions are related to a viewpoint of investor whose transaction costs coincide with the spread.

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.

scholarly journals Efficiency of S&P CNX Nifty Index Option of the National Stock Exchange (NSE), India, using Box Spread Arbitrage Strategy

2014 ◽  
Vol 15 (3) ◽  
pp. 269 ◽  
Author(s):  
G. P. Girish ◽  
Nikhil Rastogi
Keyword(s):  
High Frequency ◽  
Stock Exchange ◽  
Estimation Error ◽  
High Frequency Data ◽  
Frequency Data ◽  
Index Options ◽  
Time Period ◽  
Arbitrage Strategy ◽  
Computational Simplicity ◽  
Index Option

Box spread is a trading strategy in which one simultaneously buys and sells options having the same underlying asset and time to expiration, but different exercise prices. This study examined the efficiency of European style S&P CNX Nifty Index options of National Stock Exchange, (NSE) India by making use of high-frequency data on put and call options written on Nifty (Time-stamped transactions data) for the time period between 1st January 2002 and 31st December 2005 using box-spread arbitrage strategy. The advantages of box-spreads include reduced joint hypothesis problem since there is no consideration of pricing model or market equilibrium, no consideration of inter-market non-synchronicity since trading box spreads involve only one market, computational simplicity with less chances of mis-specification error, estimation error and the fact that buying and selling box spreads more or less replicates risk-free lending and borrowing. One thousand three hundreds and fifty eight exercisable box-spreads were found for the time period considered of which 78 Box spreads were found to be profitable after incorporating transaction costs (32 profitable box spreads were identified for the year 2002, 19 in 2003, 14 in 2004 and 13 in 2005) The results of our study suggest that internal option market efficiency has improved over the years for S&P CNX Nifty Index options of NSE India.     

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data
Sign in / Sign up

Export Citation Format

Share Document