TESTING FOR A CHANGE IN CORRELATION AT AN UNKNOWN POINT IN TIME USING AN EXTENDED FUNCTIONAL DELTA METHOD

2011 ◽  
Vol 28 (3) ◽  
pp. 570-589 ◽  
Author(s):  
Dominik Wied ◽  
Walter Krämer ◽  
Herold Dehling
Keyword(s):  
Monte Carlo ◽  
Stock Returns ◽  
Structural Changes ◽  
Null Distribution ◽  
Delta Method ◽  
Local Power ◽  
Finite Sample ◽  
Empirical Correlations ◽  
Functional Delta Method ◽  
Unknown Point

We propose a new test against a change in correlation at an unknown point in time based on cumulated sums of empirical correlations. The test does not require that inputs are independent and identically distributed under the null. We derive its limiting null distribution using a new functional delta method argument, provide a formula for its local power for particular types of structural changes, give some Monte Carlo evidence on its finite-sample behavior, and apply it to recent stock returns.

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.

scholarly journals Testing for Multiple Structural Changes with Non-Homogeneous Regressors

2015 ◽  
Vol 7 (1) ◽  
pp. 1-35 ◽  
Author(s):  
Eiji Kurozumi
Keyword(s):  
Monte Carlo ◽  
Structural Changes ◽  
Linear Trend ◽  
Critical Values ◽  
Test Statistics ◽  
Finite Sample ◽  
Multiple Structural Changes ◽  
Finite Sample Properties ◽  
Polynomial Trends ◽  
Weighted Type

AbstractThis paper investigates tests for multiple structural changes with non-homogeneous regressors, such as polynomial trends. We consider exponential-type, supremum-type and average-type tests as well as the corresponding weighted-type tests suggested in the literature. We show that the limiting distributions depend on regressors in general, and we need to tabulate critical values depending on them. Then, we focus on the linear trend case and obtain the critical values of the test statistics. The Monte Carlo simulations are conducted to investigate the finite sample properties of the tests proposed in the paper, and it is found that the specification of the number of breaks is an important factor for the finite sample performance of the tests. Since it is often the case that we cannot prespecify the number of breaks under the alternative but can suppose only the maximum number of breaks, the weighted-type tests are useful in practice.

A NONPARAMETRIC TEST OF SIGNIFICANT VARIABLES IN GRADIENTS

2020 ◽  
pp. 1-45
Author(s):  
Feng Yao ◽  
Taining Wang
Keyword(s):  
Null Distribution ◽  
Monte Carlo Study ◽  
Nonparametric Test ◽  
Hedonic Price ◽  
Finite Sample ◽  
Test Statistic ◽  
Bootstrap Test ◽  
Local Polynomial Estimation ◽  
Polynomial Estimation ◽  
Mean Function

We propose a nonparametric test of significant variables in the partial derivative of a regression mean function. The derivative is estimated by local polynomial estimation and the test statistic is constructed through a variation-based measure of the derivative in the direction of variables of interest. We establish the asymptotic null distribution of the test statistic and demonstrate that it is consistent. Motivated by the null distribution, we propose a wild bootstrap test, and show that it exhibits the same null distribution, whether the null is valid or not. We perform a Monte Carlo study to demonstrate its encouraging finite sample performance. An empirical application is conducted showing how the test can be applied to infer certain aspects of regression structures in a hedonic price model.

scholarly journals Monitoring Persistence Change in Heavy-Tailed Observations

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 936
Author(s):  
Dan Wang
Keyword(s):  
Kernel Method ◽  
Alternative Hypothesis ◽  
Null Distribution ◽  
Real Data ◽  
Ratio Test ◽  
Finite Sample ◽  
Test Statistic ◽  
Bootstrap Approximation ◽  
Heavy Tailed ◽  
Better Than

In this paper, a ratio test based on bootstrap approximation is proposed to detect the persistence change in heavy-tailed observations. This paper focuses on the symmetry testing problems of I(1)-to-I(0) and I(0)-to-I(1). On the basis of residual CUSUM, the test statistic is constructed in a ratio form. I prove the null distribution of the test statistic. The consistency under alternative hypothesis is also discussed. However, the null distribution of the test statistic contains an unknown tail index. To address this challenge, I present a bootstrap approximation method for determining the rejection region of this test. Simulation studies of artificial data are conducted to assess the finite sample performance, which shows that our method is better than the kernel method in all listed cases. The analysis of real data also demonstrates the excellent performance of this method.

scholarly journals Clarifying the Response of Gold Return to Financial Indicators: An Empirical Comparative Analysis Using Ordinary Least Squares, Robust and Quantile Regressions

2019 ◽  
Vol 12 (1) ◽  
pp. 33 ◽  
Author(s):  
Takashi Miyazaki
Keyword(s):  
Stock Returns ◽  
Structural Changes ◽  
Global Financial Crisis ◽  
Ordinary Least Squares ◽  
Stock Market Volatility ◽  
Financial Indicators ◽  
Sample Period ◽  
Flight To Quality ◽  
Crude Oil Prices ◽  
The Global Financial Crisis

In this study, I apply a quantile regression model to investigate how gold returns respond to changes in various financial indicators. The model quantifies the asymmetric response of gold return in the tails of the distribution based on weekly data over the past 30 years. I conducted a statistical test that allows for multiple structural changes and find that the relationship between gold return and some key financial indicators changed three times throughout the sample period. According to my empirical analysis of the whole sample period, I find that: (1) the gold return rises significantly if stock returns fall sharply; (2) it rises as the stock market volatility increases; (3) it also rises when general financial market conditions tighten; (4) gold and crude oil prices generally move toward the same direction; and (5) gold and the US dollar have an almost constant negative correlation. Looking at each sample period, (1) and (2) are remarkable in the period covering the global financial crisis (GFC), suggesting that investors divested from stocks as a risky asset. On the other hand, (3) is a phenomenon observed during the sample period after the GFC, suggesting that it reflects investors’ behavior of flight to quality.

Finite Sample Distributions of t and F Statistics in an AR(1) Model with Anexogenous Variable

1987 ◽  
Vol 3 (3) ◽  
pp. 387-408 ◽  
Author(s):  
J.C. Nankervis ◽  
N.E. Savin
Keyword(s):  
Monte Carlo ◽  
Time Trend ◽  
Stable Process ◽  
Asymptotic Approximations ◽  
Test Statistics ◽  
Finite Sample ◽  
Sampling Behavior ◽  
Exogenous Process ◽  
Exogenous Time Series ◽  
F Statistics

The distributions of the test statistics are investigated in the context of an AR(1) model where the root is unity or near unity and where the exogenous process is a stable process, a random walk or a time trend. The finite sample distributions are estimated by Monte Carlo methods assuming normal disturbances. The sensitivity of the distributions to both the values of the parameters of the AR(1) model and the process generating the exogenous time series is examined. The Monte Carlo results motivate several theorems which describe the exact sampling behavior of the test statistics. The analytical and empirical results present a mixed picture with respect to the accuracy of the relevant asymptotic approximations.

scholarly journals Changes in the Portfolio Management and Construction under the Pandemic Era

2021 ◽  
Vol 275 ◽  
pp. 01005
Author(s):  
Ruipeng Tan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stock Returns ◽  
Financial Market ◽  
Portfolio Management ◽  
Portfolio Construction ◽  
Sample Data ◽  
Before And After ◽  
Reduce Risk ◽  
The Impact

This paper focuses on comparing portfolio management and construction before and after the coronavirus. First, this paper presents the importance of building up portfolios for investors to diversify their risks. Theories on portfolio management are discussed in this section to show how they have been developed to help on investing and reduce risk. Then, the paper moves on to show the impact of the pandemic on the financial market and portfolio management. Sample data on tech stock returns are collected to perform a Monte Carlo simulation on portfolio construction to find out the efficient portfolio before and after the COVID-19 outbreak. The efficient portfolio is build based on the Markowitz theory to find the combination. Comparisons between these portfolio constructions are made to find out the changes in portfolio management and construction under the pandemic era. In conclusion, this paper presents how pandemic has changed and impacted the investments and lists recommendations on future portfolio management and construction.

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