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 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.

scholarly journals Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dawit Yeshiwas ◽  
Yebelay Berelie
Keyword(s):  
Crude Oil ◽  
High Frequency ◽  
Realized Volatility ◽  
Multivariate Garch ◽  
High Frequency Data ◽  
Frequency Data ◽  
Asset Return ◽  
Coffee Arabica ◽  
Forecasting Performance ◽  
Intraday Data

Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers. This paper estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of Coffee Arabica and compares the forecasting performance of these models based on high-frequency intraday data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility and employ high-frequency data whenever feasible.

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 Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2773
Author(s):  
Paravee Maneejuk ◽  
Nootchanat Pirabun ◽  
Suphawit Singjai ◽  
Woraphon Yamaka
Keyword(s):  
High Frequency ◽  
Foreign Exchange Market ◽  
Markov Switching ◽  
High Frequency Data ◽  
Garch Models ◽  
Frequency Data ◽  
Hedge Ratio ◽  
Hedging Strategies ◽  
Hedging Effectiveness ◽  
Intraday Data

Previous studies aimed at determining hedging strategies commonly used daily closing spot and futures prices for the analysis and strategy building. However, the daily closing price might not be the appropriate for price in some or all trading days. This is because the intraday data at various minute intervals, in our view, are likely to better reflect the information about the concrete behavior of the market returns and reactions of the market participants. Therefore, in this study, we propose using high-frequency data along with daily data in an attempt to determine hedging strategies, using five major international currencies against the American dollar. Specifically, in our study we used the 5-min, 30-min, 60-min, and daily closing prices of the USD/CAD (Canadian Dollar), USD/CNY (Chinese Yuan), USD/EUR (Euro), USD/GBP (British Pound), and USD/JPY (Japanese Yen) pairs over the 2018–2019 period. Using data at 5-min, 30-min, and 60-min intervals or high-frequency data, however, means the use of a relatively large number of observations for information extractions in general and econometric model estimations, making data processing and analysis a rather time-consuming and complicated task. To deal with such drawbacks, this study collected the high-frequency data in the form of a histogram and selected the representative daily price, which does not have to be the daily closing value. Then, these histogram-valued data are used for investigating the linear and nonlinear relationships and the volatility of the interested variables by various single- and two-regime bivariate GARCH models. Our results indicate that the Markov Switching Dynamic Copula-Generalized autoregressive conditional heteroskedasticity (GARCH) model performs the best with the lowest BIC and gives the highest overall value of hedging effectiveness (HE) compared with the other models considered in the present endeavor. Consequently, we can conclude that the foreign exchange market for both spot and futures trading has a nonlinear structure. Furthermore, based on the HE results, the best derivatives instrument is CAD using one-day frequency data, while GBP using 30-min frequency data is the best considering the highest hedge ratio. We note that the derivative with the highest hedging effectiveness might not be the one with the highest hedge ratio.

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