An Extended Class of Unit Root Tests for Relative Stock Prices

2014 ◽  
Author(s):  
Shlomo Zilca
Keyword(s):  
Unit Root ◽  
Stock Prices ◽  
Unit Root Tests ◽  
Extended Class

The random walk in the stock prices of 18 OECD countries

2016 ◽  
Vol 43 (4) ◽  
pp. 598-608
Author(s):  
Hassan Shirvani ◽  
Natalya V. Delcoure
Keyword(s):  
Unit Root ◽  
Stock Prices ◽  
Unit Roots ◽  
Oecd Countries ◽  
Unit Root Tests ◽  
Panel Unit Root Tests ◽  
Cross Sectional ◽  
Panel Unit Root ◽  
Content Type ◽  
Long Run

Purpose The purpose of this paper is to examine the presence of unit roots in the stock prices of 16 OECD countries. Design/methodology/approach Heterogeneous panel unit root tests developed by Im et al. (1997/2003) and Pesaran (2007). Findings Under the assumption of cross-sectional independence across the panel, the authors find no evidence of unit roots, thus failing to reject mean reversion in the stock prices for all the countries in the sample. However, under the assumption of cross-sectional dependence, an assumption borne out by the diagnostic test results, the authors find support for the presence of unit roots in the stock prices. Practical implications Thus, the use of more robust panel unit root tests seems to raise questions about the long-run predictability of the stock market, at least in the context of the OECD countries. Originality/value Thus, it seems that in the long run, an investment policy of buy and hold has still much to offer.

scholarly journals Random Walk in Emerging Asian Stock Markets

2016 ◽  
Vol 9 (1) ◽  
pp. 20
Author(s):  
Muneer Shaik ◽  
S. Maheswaran
Keyword(s):  
Random Walk ◽  
Unit Root ◽  
Stock Prices ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Unit Root Tests ◽  
Optimal Test ◽  
Market Prices ◽  
Crisis Period ◽  
Random Walk Hypothesis

<p>The random walk hypothesis is an important area of research in finance and many tools have been proposed to investigate the behaviour of the fluctuations in stock prices. However, a detail study on emerging Asian stock markets which employ the various unit root tests has not been done. In this paper, we employ six different unit root tests such as the Augmented Dickey and Fuller test (1979), Phillips and Perron test (1988), Kwiatkowski-Phillips-Schmidt-Shin test(1992), Dickey-Fuller GLS (ERS) test (1996), Elliot-Rothenberg-Stock Point-Optimal test (1996) and Ng and Perron (2001) unit root tests on 10 emerging Asian stock markets to detect for the presence of a random walk in stock prices. We have conducted the unit root tests during different sub-sample time periods of global financial crisis to check for robustness. To be specific, we have found that during the overall sample period (2001-2015) 8 out of 10 Asian stock markets and during the pre-crisis period (2001-2007) all the 10 Asian stock market prices do follow random walk according to the unit root tests under consideration. However, during the crisis &amp; post-crisis period (2008-2015) we have found only 5 out of 10 Asian markets follow the random walk movement based on unit root tests.</p>

Mean reversion in stock prices: new evidence from panel unit root tests

2007 ◽  
Vol 24 (3) ◽  
pp. 233-244 ◽  
Author(s):  
Paresh Kumar Narayan ◽  
Seema Narayan
Keyword(s):  
Unit Root ◽  
Stock Prices ◽  
Structural Breaks ◽  
Mean Reversion ◽  
Efficient Market Hypothesis ◽  
Unit Root Tests ◽  
Panel Unit Root Tests ◽  
Test Statistic ◽  
Panel Unit Root ◽  
Content Type

PurposeThere are several studies that investigate evidence for mean reversion in stock prices. However, there is no consensus as to whether stock prices are mean reverting or random walk (unit root) processes. The goal of this paper is to re‐examine mean reversion in stock prices.Design/methodology/approachThe authors use five different panel unit root tests, namely the Im, Pesaran and Shin t‐bar test statistic, the Levin and Lin test, the Im, Lee, and Tieslau Lagrangian multiplier test statistic, the seemingly unrelated regression test, and the multivariate augmented Dickey Fuller test advocated by Taylor and Sarno.FindingsThe main finding is that there is no mean reversion of stock prices, consistent with the efficient market hypothesis.Research limitations/implicationsOne issue not considered by this study is the role of structural breaks. It may be the case that the efficient market hypothesis is contingent on structural breaks in stock prices. Future studies should model structural breaks.Practical implicationsThe findings have implications for econometric modelling, in particular forecasting.Originality/valueThis paper adds to the scarce literature on the mean reverting property of stock prices based on panel data; thus, it should be useful for researchers.

Are stock prices in the US nonstationary? Evidence from contemporary unit root tests

2011 ◽  
Vol 21 (22) ◽  
pp. 1703-1709 ◽  
Author(s):  
Vasudeva Murthy ◽  
Kenneth Washer ◽  
John Wingender
Keyword(s):  
Unit Root ◽  
Stock Prices ◽  
Unit Root Tests ◽  
The Us

Are stock prices stationary? Some new evidence from a panel data approach

2014 ◽  
Vol 31 (4) ◽  
pp. 387-405
Author(s):  
Xin Shen ◽  
Mark J. Holmes
Keyword(s):  
Panel Data ◽  
Unit Root ◽  
Stock Prices ◽  
Stock Price ◽  
Panel Data Analysis ◽  
Seemingly Unrelated Regression ◽  
Unit Root Tests ◽  
Cross Sectional ◽  
Content Type ◽  
Non Linear

Purpose – This paper investigates whether mean reversion holds for a panel of 16 OECD stock price indices for the period 1970 to 2011. Design/methodology/approach – We employ seemingly unrelated regression (SUR)-based linear and non-linear unit root tests which are not only able to exploit the power of panel data analysis but also account for cross sectional dependencies as well as identify which panel members are stationary. Findings – In contrast to a literature that offers mixed findings on stationarity, it was found that most of our sample is characterized as mean- or trend-reverting with approximated half-lives in the region of three to five years. Originality/value – In contrast to other panel unit root tests of stock prices, the authors identify which individual panel members are stationary and non-stationary using a SURADF test. A further novelty of our approach is that we also develop a SUR-based panel KSS test that allows us to explore the possibility that stock prices exhibit non-linear stationarity.

Do Asian Stock Markets Follow a Random Walk? Evidence from LM Unit Root Tests with One and Two Structural Breaks

2007 ◽  
Vol 10 (01) ◽  
pp. 15-31 ◽  
Author(s):  
Hooi Hooi Lean ◽  
Russell Smyth
Keyword(s):  
Random Walk ◽  
Lagrange Multiplier ◽  
Unit Root ◽  
Stock Prices ◽  
Structural Breaks ◽  
Structural Break ◽  
Unit Root Test ◽  
Unit Root Tests ◽  
Asian Countries ◽  
Random Walk Hypothesis

This paper applies univariate and panel Lagrange Multiplier (LM) unit root tests with one and two structural breaks to examine the random walk hypothesis for stock prices in eight Asian countries. The results from the univariate LM unit root tests and panel LM unit root test with one structural break suggest that stock prices in each country is characterized by a random walk, but the findings from the panel LM unit root test with two structural breaks suggest that stock prices in the eight countries are mean reverting.

Survey on structural breaks and unit root tests

2020 ◽  
Vol 58 ◽  
pp. 96-141
Author(s):  
A. Skrobotov ◽  
◽  
Keyword(s):  
Unit Root ◽  
Structural Breaks ◽  
Unit Root Tests

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.

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