Variance-Ratio Tests and High-Frequency Data: A Study of Liquidity and Mean Reversion in the Indian Equity Markets

2003 ◽  
Author(s):  
Tirthankar C. Patnaik ◽  
Susan Thomas
Keyword(s):  
High Frequency ◽  
Equity Markets ◽  
Mean Reversion ◽  
High Frequency Data ◽  
Variance Ratio ◽  
Frequency Data

scholarly journals UNIT ROOT TEST WITH HIGH-FREQUENCY DATA

2021 ◽  
pp. 1-59
Author(s):  
Sébastien Laurent ◽  
Shuping Shi
Keyword(s):  
Random Walk ◽  
Unit Root ◽  
High Frequency ◽  
Asset Prices ◽  
Composite Index ◽  
Mean Reversion ◽  
Unit Root Tests ◽  
High Frequency Data ◽  
Frequency Data ◽  
Intraday Data

Deviations of asset prices from the random walk dynamic imply the predictability of asset returns and thus have important implications for portfolio construction and risk management. This paper proposes a real-time monitoring device for such deviations using intraday high-frequency data. The proposed procedures are based on unit root tests with in-fill asymptotics but extended to take the empirical features of high-frequency financial data (particularly jumps) into consideration. We derive the limiting distributions of the tests under both the null hypothesis of a random walk with jumps and the alternative of mean reversion/explosiveness with jumps. The limiting results show that ignoring the presence of jumps could potentially lead to severe size distortions of both the standard left-sided (against mean reversion) and right-sided (against explosiveness) unit root tests. The simulation results reveal satisfactory performance of the proposed tests even with data from a relatively short time span. As an illustration, we apply the procedure to the Nasdaq composite index at the 10-minute frequency over two periods: around the peak of the dot-com bubble and during the 2015–2106 stock market sell-off. We find strong evidence of explosiveness in asset prices in late 1999 and mean reversion in late 2015. We also show that accounting for jumps when testing the random walk hypothesis on intraday data is empirically relevant and that ignoring jumps can lead to different conclusions.

scholarly journals Study on Stock Index Futures’ Mean Reversion Effect and Arbitrage in China Based on High-Frequency Data

iBusiness ◽  
2012 ◽  
Vol 04 (01) ◽  
pp. 78-83 ◽  
Author(s):  
Wei Zhuo ◽  
Xiujuan Zhao ◽  
Zhou Zhou ◽  
Shouyang Wang
Keyword(s):  
High Frequency ◽  
Mean Reversion ◽  
Stock Index ◽  
High Frequency Data ◽  
Stock Index Futures ◽  
Frequency Data ◽  
Index Futures

scholarly journals Retornos Anormais no Ibovespa Utilizando Modelos para Dados de Alta Frequência

2012 ◽  
Vol 10 (2) ◽  
pp. 243
Author(s):  
Nelson Ferreira Fonseca ◽  
Wagner Moura Lamounier ◽  
Aureliano Angel Bressan
Keyword(s):  
High Frequency ◽  
Historical Data ◽  
Abnormal Returns ◽  
Equity Markets ◽  
Trading Strategies ◽  
High Frequency Data ◽  
Var Model ◽  
Frequency Data ◽  
Forecasting Models ◽  
Better Than

This article aims to identify profitable trading strategies based on the effects of leads and lags between the spot and futures equity markets in Brazil, using high frequency data. To achieve this objective and based on historical data of the Bovespa and the Bovespa Future indexes, four forecasting models have been built: ARIMA, ARFIMA, VAR, and VECM. The trading strategies tested were: net trading strategy, buy and hold strategy, and filter strategy – better than average predicted return. The period of analysis of this paper extends from August 1, 2006 to October 16, 2009. In this work, it was possible to obtain abnormal returns using trading strategies with the VAR model on the effects of leads and lags between the Bovespa index and Bovespa Future index.

Testing Martingale Hypothesis Using Variance Ratio Tests: Evidence from High-frequency Data of NCDEX Soya Bean Futures

2019 ◽  
Vol 20 (6) ◽  
pp. 1407-1422
Author(s):  
Sibanjan Mishra
Keyword(s):  
Market Efficiency ◽  
High Frequency ◽  
Weak Form ◽  
Soya Bean ◽  
High Frequency Data ◽  
Variance Ratio ◽  
Frequency Data ◽  
Futures Trading ◽  
Processing Information ◽  
Martingale Hypothesis

The aim of the article is to examine the martingale hypothesis of market efficiency on high-frequency data of the soya bean futures traded in National Commodity and Derivatives Exchange (NCDEX) of India using multiple variance ratio (VR) tests from February 2015 to August 2015. The study employs high-frequency future prices of 5, 10, 15, 30 and 60 min time intervals mainly to decipher the efficiency of processing information by soya bean traders during intraday sessions of futures trading. The results of VR tests confirm that except prices of 5 and 10 min intervals which displays weak form of market efficiency, all other samples follow martingale hypothesis. The findings suggest that as information gets absorbed promptly in the intraday NCDEX soya bean futures prices, there exists fairly less opportunities to explore any trading strategy for profitable outcomes in the soya bean futures market in 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
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