Test of Multiple Breaks in Long Memory Process: An Unknown Mean Breaks Case

2013 ◽  
Vol 391 ◽  
pp. 410-412
Author(s):  
Lu Deng
Keyword(s):  
Test Performance ◽  
Long Memory ◽  
Fractional Integration ◽  
Break Point ◽  
Memory Process ◽  
Finite Sample ◽  
Long Memory Process ◽  
Sample Property ◽  
Mean Variance ◽  
F Statistics

This paper extends Bai and Perron (1998) multi-unknown breaks method to long memory process, analyzes the finite sample property of the method (mean, variance, the test performance of Sup-F statistics) and the relation with the fractional integration parameter, size and location of break point. One and two unknown mean break and various long memory situations are considered. It found that expect for the condition that fractional integrated parameter d approaches to 0.5, BP method is fairly exact to estimate the break point, the bias is relatively small and the power and size of the Sup-F test is acceptable. Especially when d is negative, this method shows outstanding statistical property, small bias and standard deviation, and perfect test power and size.

scholarly journals Cointegrated Dynamics for a Generalized Long Memory Process: Application to Interest Rates

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Manabu Asai ◽  
Shelton Peiris ◽  
Michael McAleer ◽  
David E. Allen
Keyword(s):  
Interest Rates ◽  
Long Memory ◽  
Moving Average ◽  
Unit Root Test ◽  
Unit Roots ◽  
Memory Process ◽  
Finite Sample ◽  
Interest Rate Policy ◽  
Long Memory Process ◽  
The Usa

AbstractRecent developments in econometric methods enable estimation and testing of general long memory processes, which include the general Gegenbauer process. This paper considers the error correction model for a vector general long memory process, which encompasses the vector autoregressive fractionally integrated moving average and general Gegenbauer processes. We modify the tests for unit roots and cointegration, based on the concept of heterogeneous autoregression. The Monte Carlo simulations show that the finite sample properties of the modified tests for unit roots are satisfactory, while the conventional tests suffer from size distortion. The experiments also indicate that the modified tests for cointegration improve the problem of finding too many cointegration relationships which arises for fractionally integrated series. Empirical results for interest rates series for the USA and Australia indicate that: (1) the modified unit root test detected unit roots for all series; (2) after differencing, all series favour the general Gegenbauer (GG) process; (3) the modified test for cointegration found only two cointegrating vectors; and (4) the zero interest rate policy in the USA had no effect on the cointegrating vectors for the two countries.

A Random walk down the Dhaka Stock Exchange lane

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Md. Kamrul Bari ◽  
Dr. Melita Mehjabeen ◽  
Dr. A. K. Enamul Haque
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Market Efficiency ◽  
Long Memory ◽  
Stock Exchange ◽  
Reliable Estimate ◽  
Memory Process ◽  
Long Memory Process ◽  
Study Results ◽  
Adf Test

Market efficiency has always been a matter of keen interest to the researchers of finance. Since the advancement of this concept, researchers are consistently investigating the market efficiency of different financial markets. Bangladesh, being one of the emerging economies, has also attracted the attention of many researchers. The researchers have investigated the realities regarding the market efficiency of both the stock exchanges of the country. Most of their investigations reveal that the Dhaka Stock Exchange (DSE) and the Chittagong Stock Exchange (CSE) are inefficient. This research, however, did not stop at revisiting market efficiency alone. Whether the return series follows a long-memory process, has also been tested. Besides, non-parametric tests have also been conducted to confirm the results of the parametric tests and vice versa. It generated a more reliable estimate of market efficiency for the period under study. Results of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model confirm that the return series does not follow a long memory process, and any shock in the system will eventually vanish. The findings of other tests (the run test, the Augmented Dickey-Fuller (ADF) test, the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, and the Kolmogorov-Smirnov (K-S) test) suggest that the return series of the DSE are time-series stationary, non-normal, and do not follow a random walk. Given these results, we must echo the prior researchers to conclude that the stock market of Bangladesh is not efficient for the period of 2015 to 2020. These findings add new knowledge to the existing knowledge pool about market efficiency and long memory of the stock market of Bangladesh.

On the properties of the periodogram of a stationary long-memory process over different epochs with applications

2010 ◽  
Vol 31 (1) ◽  
pp. 20-36 ◽  
Author(s):  
Valdério A. Reisen ◽  
Eric Moulines ◽  
Philippe Soulier ◽  
Glaura C. Franco
Keyword(s):  
Long Memory ◽  
Memory Process ◽  
Long Memory Process

scholarly journals TESTING THE ORDER OF FRACTIONAL INTEGRATION OF A TIME SERIES IN THE POSSIBLE PRESENCE OF A TREND BREAK AT AN UNKNOWN POINT

2018 ◽  
Vol 35 (6) ◽  
pp. 1201-1233 ◽  
Author(s):  
Fabrizio Iacone ◽  
Stephen J. Leybourne ◽  
A.M. Robert Taylor
Keyword(s):  
Time Series ◽  
Power Function ◽  
Long Memory ◽  
Fractional Integration ◽  
Monte Carlo Study ◽  
Local Power ◽  
Finite Sample ◽  
Lm Test ◽  
Unknown Point ◽  
Memory Parameter

We develop a test, based on the Lagrange multiplier [LM] testing principle, for the value of the long memory parameter of a univariate time series that is composed of a fractionally integrated shock around a potentially broken deterministic trend. Our proposed test is constructed from data which are de-trended allowing for a trend break whose (unknown) location is estimated by a standard residual sum of squares estimator applied either to the levels or first differences of the data, depending on the value specified for the long memory parameter under the null hypothesis. We demonstrate that the resulting LM-type statistic has a standard limiting null chi-squared distribution with one degree of freedom, and attains the same asymptotic local power function as an infeasible LM test based on the true shocks. Our proposed test therefore attains the same asymptotic local optimality properties as an oracle LM test in both the trend break and no trend break environments. Moreover, this asymptotic local power function does not alter between the break and no break cases and so there is no loss in asymptotic local power from allowing for a trend break at an unknown point in the sample, even in the case where no break is present. We also report the results from a Monte Carlo study into the finite-sample behaviour of our proposed test.

Real-Time Monitoring Test for Realized Volatility

2013 ◽  
Vol 5 (1) ◽  
pp. 1-24
Author(s):  
Cindy Shin-Huei Wang ◽  
Cheng Hsiao
Keyword(s):  
Real Time ◽  
Long Memory ◽  
Structural Breaks ◽  
Brownian Bridge ◽  
Memory Process ◽  
Time Pattern ◽  
Finite Samples ◽  
Long Memory Process ◽  
Data Generating Process ◽  
Monitoring Test

AbstractThis paper proposes a monitoring cumulative sum of squares (CUSQ)-type test for structural breaks in real time via an autoregressive (AR) approximation framework where data generating process (DGP) is a long memory process. The limiting distribution of the monitoring test follows a Brownian bridge and is free of long memory parameters under the null hypothesis of no break. The test is easy to implement and avoids the issue of spurious breaks found for some retrospective tests for long memory process. Neither does it need to use the bootstrap procedure to find the critical values. Monte Carlo simulations appear to confirm that there exists negligible size distortion and satisfactory power performances in finite samples. The procedure is then applied to monitor the real-time pattern of realized volatilities of dollar–Deutschmark and dollar–Japanese Yen.

scholarly journals Sieve bootstrap monitoring persistence change in long memory process

2016 ◽  
Vol 9 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Zhanshou Chen ◽  
Zheng Tian ◽  
Yuhong Xing
Keyword(s):  
Long Memory ◽  
Memory Process ◽  
Sieve Bootstrap ◽  
Long Memory Process
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