Multiple Structural Breaks Research Articles

Volatility spillover between stock market and currency market of Pakistan in the presence of structural breaks

This study examines the volatility transmission between the currency market and the stock market of Pakistan in the presence of structural breaks. For this purpose, daily data from the stock market and currency market is analyzed. An empirical investigation is conducted using the bivariate EGARCH model and the test for multiple structural breaks developed by Bai and Perron (2003). This study analyses these markets with the inclusion of sudden changes. Results of the EGARCH model suggest that volatility spillover is positive and bidirectional between these markets of Pakistan.

The influence of renewable energy use, human capital, and trade on environmental quality in South Africa: multiple structural breaks cointegration approach.

Recent economic and environmental literature suggests that the current state of energy use in South Africa amidst rapid growing population is unsustainable. Researchers in this area mostly focus on the effect of fossil energy use on carbon (CO2) emission, which represents only an aspect of environmental quality. In contrast, the current study evaluates the influence of renewable energy use, human capital, and trade on ecological footprint--a more comprehensive measure of environmental quality. To this end, the study employs multiple structural breaks cointegration tests (Maki cointegration tests), dynamic unrestricted error correction model through Autoregressive Distributed Lag (ARDL) model, and VECM Granger causality tests. The results of the Maki cointegration tests reveal the existence of a cointegration between the variables in all the models with evidence of multiple structural breaks. Further, the ARDL results divulge that an increase in renewable energy use, human capital, and trade improves environmental quality through a decrease in ecological footprint, while an increase in income stimulates ecological footprint. Moreover, causal relationship is found, running from all the variables to renewable energy and trade flow in the long run, while in the short run, economic growth causes ecological footprint. Trade is found to Granger-cause human capital, while human capital causes renewable energy. Additionally, human capital, renewable energy, and economic growth are predictors of trade. The study therefore recommends South African policymakers to consider the importance of renewable energy, human capital development, and trade as a policy option to reduce ecological footprint and improve environmental quality.

International Stock Return Predictability and Asset Pricing Models

We propose a new methodology for predicting international stock returns and evaluating international asset pricing models. Our Bayesian framework performs probabilistic selection of predictors and factors that can shift at multiple unknown structural break dates. The approach generates significantly more accurate forecasts of international stock returns than a range of popular models that are economically meaningful for a risk-averse mean-variance investor. Allowing for regime-specific variable selection reduces considerably the international diversification of an unhedged U.S. investor's portfolio. Our framework improves the ability of international asset pricing models to explain the cross-section of expected returns.

Group orthogonal greedy algorithm for change-point estimation of multivariate time series

This paper proposes a three-step method for detecting multiple structural breaks for piecewise stationary vector autoregressive processes. The number of structural breaks can be large and unknown with the locations of the breaks being different among different components. The proposed method is established via a link between a structural break problem and a high-dimensional regression problem. By means of this connection, a group orthogonal greedy algorithm, originated from the high-dimensional variable selection context, is developed for efficiently screening out potential break-points in the first step. A high-dimensional information criterion is proposed for consistent structural breaks estimation in the second step. In the third step, the information criterion further determines the specific components in which structural breaks occur. Monte Carlo experiments are conducted to demonstrate the finite sample performance, and applications to stock data are provided to illustrate the proposed method.

Bayesian estimation for threshold autoregressive model with multiple structural breaks

This paper provides a Bayesian setup for multiple regimes threshold autoregressive model with possible break points. A full conditional posterior distribution is obtained for all model parameters with considering suitable prior information. Threshold and break point variables do not attain standard form distributions. To compute posterior distributions, we apply the Gibbs sampler with the Metropolis-Hastings algorithm. A variety of loss functions are considered for optimizing the risk associated with each parameter. For empirical evidence, simulation study and real data illustration are carried out.

Evaluation of Test Statistics for Detection of Outliers and Shifts

Existence of outliers and structural breaks having mutually unknown nature, in time series data, offer challenges to data analysts in model identification, estimation and validation. Detection of these outliers has been an important area of research in time series since long. To analyze the impact of these structural breaks and outliers on model identification, estimation and their inferential analysis, we use two data generating processes: MA(1) and ARMA(1,1). The performance of the test statistics for detecting additive outlier(AO), innovative outlier(IO), level shift(LS) and transient change(TC) is investigated using simulation strategy through power of a test, empirical level of significance, empirical critical values, misspecification frequencies and sampling distribution of estimators for the two models. The empirical critical values are found higher than the theoretical cut-off points, empirical power of the test statistics is not satisfactory for small sample size, large cut-off points and large model coefficient. We have explored confusion between LS, AO, TC and IO at different critical values(c) by varying sample size. We have also collected empirical evidence from time series data for Pakistan using 3-stage iterative procedure to detect multiple outliers and structural breaks. We find that neglecting shocks lead to wrong identification, biased estimation and excess kurtosis.
 JEL Classification Codes: C15, C18, C63, C32, C87, C51, C52, C82
 AMS Classification Codes: 62, 65, 91, DI, 62-08, 62J20, 00A72, 91-08, 91-10, 91-11 62P20, 91B82, 91B84, 62M07, 62M09, 62M10, 62M15, 62M20

LPG consumption and environmental Kuznets curve hypothesis in South Asia: a time-series ARDL analysis with multiple structural breaks.

This paper aims to scrutinize the validity of the greenhouse emissions-induced environmental Kuznets curve (EKC) hypothesis, controlling for liquefied petroleum gas (LPG) consumption, FDI inflows, and trade openness, in the context of six South Asian economies. Besides, the impacts of LPG use on both aggregate and disaggregated emissions of greenhouse gases are also evaluated. Using annual data from 1980 to 2016, the elasticity estimates from the autoregressive distributed lag (ARDL) regression analysis confirms the authenticity of the EKC hypothesis for Bangladesh, India, Sri Lanka, and Bhutan. In the cases of Pakistan and Nepal, economic growth, in the long-run, is evidenced to monotonically increase and decrease the greenhouse emissions, respectively. However, LPG consumption is found to homogenously reduce all types of greenhouse emissions in each of the selectedSouth Asian nations. Moreover, in majority of the cases, statistical evidence of joint favorable impacts of economic growth and LPG consumption on the environmentare ascertained. Furthermore, the Hacker and Hatemi-J bootstrapped causality analysis finds causal relationships between economic growth, greenhouse emissions, and LPG consumption. However, the causality estimates are found to be heterogeneous across the different South Asian nationsconsidered in the analysis. The results, in a nutshell, denote that economic growth is both the cause and the solution to the greenhouse emission problems faced by the South Asian economies. Moreover, the results also assert that LPG can be a transitional fuel to reduce these emissions before the South Asian nations are ready to undergo transition from non-renewable to renewable energy consumption.Hence, the findings impose key fuel-diversification policy implicationsfor the South Asian governments.

ANALYSIS OF THE RELATIONSHIP BETWEEN ENERGY PRICE CHANGES AND STOCK MARKET INDICES IN DEVELOPED COUNTRIES

Oil is an important energy source and basic raw material in the manufacturing process. Therefore, the economies of every country in the world are directly or indirectly dependent on oil. The chain effect of the addiction in question also affects the financial markets. In the study, long-term relationship between energy prices (oil, natural gas and electricity prices) and stock market index for developed countries was analyzed with Multiple Structural Break Panel Cointegration Test. The causality relationship between the variables was examined with Dumitrescu-Hurlin Panel Causality Test. According to the findings of the analysis, it was determined that the series move together in the long term. However, while there was causality between oil prices and natural gas prices in the short term, it was revealed that there was no causality between electricity prices and stock market indices.

Random order autoregressive time series model with structural break

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

Unit root tests in the presence of structural breaks: Evidence from African stock markets

This paper examines whether stock prices for fourteen African countries are affected by transitory or permanent shocks. This study answers whether Africa stock market indices are mean-reverting or random-walk in the presence of multiple structural breaks. To investigate African equity price behavior, we considered one and two endogenously determined structural break tests of Zivot and Andrews (1992) and Lumsdaine and Papell (1997), respectively. Findings/Originality: Our results show that almost all African equity price indices follow the random walk processes except for Senegal and Botswana, which exhibit mean-reversion properties in its equity prices. It implies that investors in African stock markets cannot rely on past information and behavior to predict stock market movements or develop their trading strategies. The result also confirms that the Augmented Dickey-Fuller (ADF) unit root test is not applicable in the presence of structural breaks in African stock markets.

The Environmental Kuznets curve hypothesis for deforestation in Bangladesh: An ARDL analysis with multiple structural breaks

This paper makes a novel attempt to test the validity of the environmental Kuznets curve hypothesis in the context of Bangladesh using deforestation propensities as indicators of environmental adversities and controlling for energy consumption, agricultural land coverage and population growth rate. Using annual frequency data from 1972 to 2018, the short- and long-run elasticity estimates from the autoregressive distributed lag-error correction modeling approach provide statistical support to the nonlinear inverted-U-shaped association between economic growth and deforestation practices in Bangladesh. Thus, the results validate the deforestation-induced environmental Kuznets curve hypothesis in the context of Bangladesh. The elasticity estimates also reveal that energy consumption promotes deforestation activities in the long run but not in the short run. Besides, higher population growth rate and agricultural land expansion are found to account for greater deforestation propensities both in the short and long runs. Furthermore, the vector error correction model and the Hacker and Hatemi-J Granger causality exercises reveal the causal impacts of economic growth on deforestation propensities, both in the short and the long runs. Therefore, the overall results, in a nutshell, indicate a trade-off between economic and environmental welfares during the initial phases of economic growth in Bangladesh which can be anticipated to fade away in the long run. The overall results impose critically important policy implications regarding effective means to reduce deforestation practices in Bangladesh.

The Greater Himalayan Thrust Belt: Insight Into the Assembly of the Exhumed Himalayan Metamorphic Core, Modi Khola Valley, Central Nepal

AbstractStrike‐parallel tectonometamorphic discontinuities within the Himalayan metamorphic core are typically interpreted to reflect thrust‐sense movement. However, there is disagreement on the nature and sense of movement across one such structure in central Nepal. Using an integrated approach, this study characterizes multiple structural breaks in the Modi Khola region. Thermobarometric calculations combined with petrochronological investigation show that rocks across the Sinuwa thrust record similar histories with prograde garnet growth ca. 35 Ma and peak pressures of ~11.0 kbar, anatexis at ca. 28 Ma, and followed by cooling and exhumation between ca. 24 and 15 Ma. Rocks below the structurally lower Bhanuwa fault record similar garnet growth at ca. 35 Ma and pressures of ~11.5 kbar but experienced melting and retrogression after ca. 21 Ma. Finally, rocks in the footwall of the Main Central thrust, the structurally lowest break investigated, record prograde metamorphism ca. 17–13 Ma with peak pressures of ~7.0 kbar. This down‐structural migration of prograde metamorphism, anatexis, and subsequent cooling and exhumation of the footwall is consistent with models of progressive underplating and in‐sequence thrusting. When paired with published cooling ages across the Bhanuwa fault, results from this study are consistent with normal‐sense reactivation of the structure during middle‐late Miocene time. This new data set shows that the final assembly of the Himalayan metamorphic core is a result of progressive deformation and juxtaposition of multiple thrust sheets through a combination of in‐sequence thrusting, out‐of‐sequence thrusting, and normal faulting. We refer to this as the Greater Himalayan thrust belt.

The Influence of FDI on Domestic Innovation: An Investigation Using Structural Breaks

This paper investigates the influence of foreign direct investment on innovation by em-ploying the panel cointegration method incorporating multiple structural breaks and a dynamic common correlated estimation for 34 countries over the period 1991-2016. Our findings indicate that the series of innovation and foreign direct investment are stationary after considering the potential structural breaks and that the external shock is mainly from country-specific shocks. Our results also suggest that a long-run co-integrated relationship exists with one break between foreign direct investment and innovation. Furthermore, the estimation based on the dynamic common correlated estimation shows that foreign direct investment has a long-run significant positive influence on technological innovation. Our findings shed light on the importance of accounting for structural breaks when discussing the relationship between foreign direct investment and technological innovation.

Persistence in per capita energy consumption: A fractional integration approach with a Fourier function

Abstract In this study, we investigate the degree of persistence for aggregate energy consumption per capita for 113 countries over the period 1960–2014 through a recently introduced fractional unit root test with a Fourier function to allow multiple smooth structural breaks. We also employ the ADF unit root test, the efficient Wald test for fractional unit-roots, and the Fourier ADF test to make a comparison and to enrich the study. The empirical results considering the fractional Fourier unit root test indicate two crucial points. First, shocks have only temporary effects on energy consumption per capita, and it will vanish slowly as a result of the long memory characteristic. Therefore even transitory shocks have persistence effects, which require permanent policies due to their nature. This inference is different from what would be erroneously suggested by the dichotomous approach for stationarity and nonstationarity that temporary shocks will have transitory effects. Second, the integration degree of energy consumption per capita may change due to the existence of a structural break; therefore, policymakers must be aware of the varying integration degree to design and implement the best policies.

Multiple Structural Breaks in Profit Share Rates of Participation Banks in Turkey: Are They Caused by the Recent Global Crises?

Multiple Structural Breaks in Profit Share Rates of Participation Banks in Turkey: Are They Caused by the Recent Global Crises?

Determinants of gold price movements: An empirical investigation in the presence of multiple structural breaks

This study investigates the short- and long-run determinants of gold price movements in financial markets by taking into account multiple structural breakpoints using an ARDL-based error correction approach. The study used daily time series data from December 19, 2018 to May 15, 2020. The key variables used include international stocks and bond funds that are frequently traded on stock exchanges around the world. The results, based on the fourth breakpoint regime, reveal a significant positive relationship between gold price movements and LSE, Nikkei stocks, T.Rowe global multi-sector bond funds, and CBOE volatility index; and a significant negative association with Gmo emerging country debt and Pimco emerging markets local currency bond funds both in the short- and long-run. Other stocks, like NASDAQ, DJI, S&P500, only revealed negative short-run relationships; except for NYSE that was found to have a positive short-run association with gold price movements. Conversely, Goldman Sachs bonds revealed a significant positive long-run relationship with gold price movements. These results have significant policy implications for gold producers and investors, as both stocks and bonds are an important source of information in the determination of gold price movements both in the short- and long-run.

A monitoring procedure for detecting structural breaks in factor copula models

Abstract We propose a new monitoring procedure based on moving sums (MOSUM) for detecting single or multiple structural breaks in factor copula models. The test compares parameter estimates from a rolling window to those from a historical data set and analyzes the behavior under the null hypothesis of no parameter change. The case of multiple breaks is also treated. In the model, the joint copula is given by the copula of random variables which arise from a factor model. This is particularly useful for analyzing high dimensional data. Parameters are estimated with the simulated method of moments (SMM). We analyze the behavior of the monitoring procedure in Monte Carlo simulations and a real data application. We consider an online procedure for predicting the day-ahead Value-at-risk based on the suggested monitoring procedure.

Multiple breaks detection in financial interval-valued time series

Multiple structural breaks detection for Interval-Valued Time Series (IVTS) is undoubtedly relevant under practical perspectives and challenging under the point of view of the analysis of expert systems. In this respect, financial time series usually show high variability and outliers; moreover, they often exhibit the property of being of high frequency nature; thus, it is naturally advisable to consider them as IVTS type for a given time unit. Despite this relevance, scarce effort has been spent by scholars to apply the methodological advancements in breaks detection for IVTS to the crucial environment of financial time series. This paper contributes to fill this gap. It employs the Atheoretical Regression Trees framework – a very recent tool that is able to automatically locale multiple breaks occurring to unknown dates – to stock prices. Such a procedure is able to estimate in an efficient way the structural breaks of the considered series; at the same time, it keeps into account the main characteristics of the intervals describing the IVTS. For our purposes, we adopt a theoretical proposal of reading daily stock prices as intervals whose bounds are defined through the closing prices. Empirical experiments on the American International Group – whose daily prices have experienced structural breaks in the past – validate the theoretical model and show the usefulness of the proposed procedure.

On the performance of the variance ratio unit root tests with flexible Fourier form

This article introduces a new unit root test that combines the variance ratio framework with the Flexible Fourier Form under the generalized least squares detrending mechanism. The advantage of the proposed method against its alternatives can be listed as: (1) it suggests a non-parametric procedure that does not require any parametric or semi-parametric model to remove serial correlation in the innovation process; (2) it can reasonably adapt itself to deal with the multiple structural breaks with various functional specifications. In the simulation exercises, we show that the proposed method exhibits satisfactory performance in the size and size-adjusted power analysis.

Exchange rate pass‐through to restaurant and hotel prices in the United States: The role of energy prices and tourism development

This study incorporates energy prices and tourism development into the standard purchasing power parity doctrine to investigate exchange rate pass‐through to restaurant and hotel prices in the United States over the period 2001:Q4 to 2017:Q4. We apply the multiple structural breaks cointegration test and flexible autoregressive distributed lag (ARDL) method. Our empirical results provide evidence that exchange rate appreciation reduces restaurant and hotel prices while an increase in energy prices and tourism development causes the prices of restaurant and hotel to rise. However, the pass‐through of exchange rate, energy prices, and tourism development is incomplete in both the long‐ and short‐run with the long‐run pass‐through having a stronger effect. Furthermore, the causality results show that restaurant and hotel prices have predictive power for exchange rate. Causality between restaurant and hotel prices and tourism development has a feedback effect. We also find that exchange rate Granger causes not only energy prices but tourism development while tourism development causes energy prices. The policy implications for these results are carefully outlined in the study.