Presence Of Structural Breaks Research Articles

A Regime Switching Skew-Normal Model of Contagion in Major African Stock Markets

This study was carried out to examine the contagion and structural break for some selected African stock markets namely: Nigeria, Ghana, South Africa (SA), Tunisia, and the US stock market. To achieve this, two periods were considered, that is the crisis period, 31st December, 2013 to 31st December, 2017 and the non-crisis period, 1st January, 2018 to 31st December, 2019. The choice of these periods was to reflect the structural break caused by economic recession in Nigeria during the crisis period. The expectation is that there is capital flight from Nigerian equities to safety in the selected emerging economies in Africa and in the US economy. Following the work of Chan et al (2018), the study used Regime Switching Skew-Normal (RSSN, henceforth) model which is capable of measuring contagion and structural breaks between markets and across crisis and non-crisis periods. The RSSN model was estimated using Bayesian method. The study finds the existence of moderate contagion between Nigeria and SA and the absence of contagion with the rest of the economies, suggesting there is capital flight from equities in Nigeria to SA for safety during the 2016 economic recession. Also, the study confirms the presence of structural break between crisis and non-crisis periods as the probability of average return declines during crisis period. The absence of contagion among African stock markets suggests there is no significant economic cooperation and cross-border portfolio investment flow among the countries. This development further underpins the imperative of the full implementation of African Continental Free Trade Area (AfCFTA), which is to encourage economic activities and investment flow on the continent.

Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability

In their out-of-sample predictions of stock returns in the presence of structural breaks, Lettau and Van Nieuwerburgh (2008) implicitly assume that economic agents’ perception of the regime-specific mean for the dividend-price ratio is time-invariant within a regime. In this paper, we challenge this assumption and employ least squares learning with constant gain (or constant-gain learning) in estimating economic agents’ time-varying perception for the mean of dividend-price ratio. We obtain better out-of-sample predictions of stock returns than in Lettau and Van Nieuwerburgh (2008) for both the U.S. and Japanese stock markets. Our empirical results suggest that economic agents’ learning plays an important role in the dynamics of stock returns.

Asymmetric volatility spillovers between world oil prices and stock markets of the G7 countries in the presence of structural breaks

AbstractThe purpose of this paper is to examine the volatility transmission between oil prices and the seven major stock markets (G7) using symmetric and asymmetric bivariate GARCH models incorporating structural breaks. Since these countries play a crucial role in the international economy, it is important for financial market participants to understand the volatility transmission process for designing optimal portfolio allocations. We examine weekly data over the period 1998–2017 and endogenously detect structural breaks in variance using an iterated algorithm in order to estimate the volatility dynamics with greater accuracy. The models were estimated using the maximum likelihood method, and the BFGS algorithm was employed to obtain the variance–covariance estimation and the corresponding standard errors. Overall, we find volatility spillovers between markets together with the fact that the oil market has a leading role. More importantly, we show that neglecting structural changes in variance and/or asymmetries may lead to biased volatility spillover effects. Furthermore, the results highlight that models ignoring structural breaks are significantly overestimating the effect of their own past volatility on the current volatility. In addition, we find that disregarding asymmetries could also lead to erroneous estimates of these effects. Finally, we compute dynamic risk‐minimizing hedge ratios and optimal portfolio weights, however, no significant discrepancies are found among the models considered.

Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks

This paper is concerned with the problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows and exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection, which is complicated by time variations in the effects of signal variables. In this study we investigate whether or not we should use weighted observations at the variable selection stage in the presence of structural breaks, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches we focus on the recently developed One Covariate at a time Multiple Testing (OCMT) method. This procedure allows a natural distinction between the selection and forecasting stages. We establish three main theorems on selection, estimation post selection and in-sample .t. These theorems provide justification for using the full (not down-weighted) sample at the selection stage of OCMT and down-weighting of observations only at the forecasting stage (if needed). The benefits of the proposed method are illustrated by empirical applications to forecasting output growths and stock market returns.

Asymmetric volatility transmission in Japanese stock market in the presence of structural breaks

This research shows that accounting for structural breaks within the asymmetric GJR-GARCH model reduces volatility persistence in stock and oil returns. More importantly, we ascertain that good news has less impact on current volatility if sudden changes are accounted for, while bad news has more impact in all cases with an exception of Nikkei225. Our findings are in line with previous studies and highlight that it is plausible to include structural breaks in asymmetric GARCH models to model volatility dynamics more precisely. The results of the univariate analysis provide evidence of bi-directional volatility spillover effects between the Japanese stock market and the rest of the markets, independently of the model choice. Thus, neglecting structural breaks in variance may lead to different results but not to the extent of distortion of the direction of volatility transmission effects. The latter finding does not hold true under the multivariate context highlighting the power difference between the CCF approach and the BEKK-GARCH models. Finally, we ascertain that data frequency may also affect the direction of the volatility spillovers among the markets.

The causality between house prices and stock prices: evidence from seven European countries

PurposeThis paper aims to examine whether there exists a long-run causal relationship between the prices of households’ two major assets: stocks and houses over the period 1975Q1–2017Q1 for seven major European countries.Design/methodology/approachThe paper uses the bootstrap panel Granger causality approach to determine the causal structure, focusing on cross-sectional dependence, slope heterogeneity and structural breaks.FindingsThe findings show that, in most cases, there is a unidirectional causality running from stock price to house price but the converse is not true. This confirms a strong wealth effect in housing markets. The findings are important for not only households but also policymakers concerned with financial stability and housing prices.Originality/valueFirst, the methodology used here devotes full attention to dynamic co-movement between housing and stock markets. Second, this study uses a rather long quarterly data, which implies that the findings could be robust. Third, the study uses real personal disposable income as a control variable to remove the effects of economic growth. Fourth, most of the previous studies do not consider the presence of structural breaks and this makes the result of causality invalid and biased. Fifth, most of the previous studies on housing and stock markets concentrated on the US and non-European countries such as China, Korea and Singapore.

Public Expenditure Shocks and Human Capital Development in the Presence of Structural Breaks: Evidence from Nigeria

The study examines the impact of public expenditure shocks on human capital development in in the Presence of Structural Breaks: Evidence from Nigeria. The nature of this study necessitates the use of a time–series research design and an extensive reliance on secondary data sourced from the Central Bank of Nigeria (CBN) statistical bulletins, for the period 1981-2018. The method of data analysis utilized a battery of econometric applications often used in most contemporary economic time-series studies. First, the descriptive and correlation analysis is presented. After that the Augmented Dickey Fuller (ADF) unit root test and Zivot and Andrews Unit root for structural breaks is applied and this is followed by the co-integration test both with and without structural breaks. The Wald test for significance of the structural breaks was also employed and then the Variance Autoregressive (VAR) technique is then utilized. Finally, the impulse-response functions is conducted The study findings reveal that response of HCAP to innovations in HEXP is shown to exhibit oscillations over the period horizon with initial declines showing in the 1st quarter after the shock. An immediate rebound in HCAP response is observed beginning from the 2nd quarter after the shock and this positive rebound is sustained up to 10th quarter after the shock. However, one standard deviation shocks to EDEXP is seen to have immediate positive effects on HCAP lasting till 2-3 quarters after the shock and then followed by a decline sustained at a little below the baseline and declining further in the 7th quarter but again we see a slight rebound which reverts. On the overall, the shocks shows high potentials for asymptotic convergence and stability in HCAP. However, the response of HCAP to investment shocks is the most destabilizing. Furthermore, we observe that the impulse response functions are robust to structural breaks. The evidence of structural breaks reveals few instances of variations with the main analysis without breaks especially in relation the public expenditure. The result shows that, with the inclusion of breaks, the sustained positive effect in HCAP of a one standard deviation shock in HEXP is still observed and also sustained up to 10th quarter after the shock. Again just like in the case of no structural breaks, we also find that the shocks in EDEXP shows high potentials for asymptotic convergence and stability in the response of HCAP but in the case of investment, we find some differences in the response of HCAP comparing with and without structural breaks. The study recommends that need for government to ensure efficiency in public expenditures.

Структурные сдвиги и тестирование на единичный корень (Structural Breaks and Unit Root Testing

Russian Abstract: В данном обзоре рассматриваются методы тестирования наличия единичного корня во временном ряде при наличии структурных сдвигов. Отдельно обсуждаются методы оценивания дат структурных сдвигов при неопределенности относительно того, является ли временной ряд стационарным или нет. Обзор покрывает большое количество недавно разработанных методов тестирования, робастных к различным типам неопределенности данных, включая начальные значения и изменяющуюся во времени волатильность. Обзор предоставляет читателю возможность практического применения современных методов тестирования в совокупности с пониманием системы развития этих методов. English Abstract: This review discusses methods of testing for a unit root in a time series in the presence of structural breaks. Separately the methods for break date estimation under uncertainty over the order of integration are considered. The review covers a large number of recently developed testing methods that are robust to various types of data uncertainty, including initial conditions and time-varying volatility. The review provides the reader the possibility of practical application of modern testing methods together with an understanding of the development of these methods.

An evaluation of some methods used for determination of homogenous structural break point in mean of panel data

In this study, performances of correct break point estimation of Simple Mean Shift Model Method, Fluctuation Test, Wald Statistic Test and Kim Test methods used to investigate presence of structural break and determine the date of break in a panel data consisting of N time series, each of T length, belonging to N cross-section have been investigated. In this context 108 Monte Carlo simulations with each 3000 repeats have been carried out for 3, 3, 4 and 3 levels of factors, respectively number of cross-section units, length of series, size of break and proportion of break, to evaluate the performance of these tests used for determination of structural break in panel data. According to the Monte Carlo simulations it is concluded that Simple Mean Shift Model approach has better performance of break point estimation than other methods. Moreover, while Wald Test puts forth its best performance in the case where the breaks in series are at the half of the series, Fluctuation and Kim Tests showed their best performances in the case that the breaks are at the third quarter of series. Generally, correct break point estimation performances of tests decrease as the number of cross-section or length of series increases, even if it is limited. The changes at the levels of the proportion of break factor also lead to high accuracy estimation performance of different methods. Moreover, increases at the size of break usually decreases rates of correct estimation of methods and they approach to zero while means of the series changed 40% and over after break.

Structural Breaks and Volatility Spillover in South Asian Economies

Purpose - This study examines the volatility spillovers in the presence of structural breaks with specific reference to South Asian Capital markets. The global financial crisis of 2007-2009 has compelled policymakers to realize that financial instability has the potential to threaten economic stability and growth; therefore, managing the financial crisis is inevitable. To manage the impact of financial crises, understanding the dynamics of volatility spillover across various markets is imperative. This study has investigated the possible emergence of structural breaks in risk patterns after global financial crises in south Asian markets.
 Methodology - Using the data from July 2002 to June 2016, employing the Exponential GARCH methodology.
 Findings - This study finds a significant volatility spillover after the financial crisis of 2007-09. Therefore, the existence of a structural break in the risk pattern of south Asian capital markets cannot be fully rejected.
 Policy Implications - This conclusion is of prime importance to policymakers in devising policy guidelines concerning financial crises.

Does trade openness affects global carbon dioxide emissions

Purpose The purpose of this paper is to analyze the effect of economic growth, international trade and energy consumption on the global carbon dioxide (CO2) emissions, in the case of top CO2 emitters, namely, USA, Japan, Canada, Iran, Saudi Arabia, UK, Australia, Italy, France and Spain using the annual data from 1971 to 2013. Design/methodology/approach For this purpose, the time series, data technique is applied. Unit root test with structural break and the bounds testing approach for cointegration in the presence of structural break is tested. Finally, a vector error correction model for the Granger causality test is applied to detect the direction of causality. The authors have used the techniques that will help in examining the structural break in the time series data. Findings The results reveal that their exists a long-run relationship between CO2 emissions and its determinants in the USA, Canada, Iran, Saudi Arabia, the UK, Australia, Italy, France and Spain, energy consumption is the main determinant of carbon dioxide (CO2) emissions in the long run and for direction of causality, the authors found bidirectional causality in the long run between energy consumption and CO2 emissions in the USA, Canada, Iran, Saudi Arabia and the UK, and Granger causality running in opposite direction in the case of Australia from CO2 emissions to energy consumption was analyzed. In terms of growth-trade-pollution nexus (USA, Canada, Iran and France) hold one-way causality running from economic growth and trade openness to CO2 emissions (IV) the environmental Kuznets curve hypothesis is validated only for the USA. Robust policy implications can be derived from this study. First, without harming the economy, these countries can reduce the use of energy consumption for lower pollution. Second, the amount of trade should be decreased to lower the emissions because the authors find that an increase in trade does Granger cause to CO2 emissions in the long run. Originality/value There has been no study that investigated the relationship between CO2 emissions, real income, consumption of energy and international trade in the environmental Kuznets relation for the top CO2 emitter’s countries over the period of 1971–2013. The authors did a comparative study of the empirical finding among these nations.

Robust and automatic data cleansing method for short-term load forecasting of distribution feeders

Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting.

The stability of money demand in India in the post reform period: an empirical analysis

This paper provides an empirical analysis of the stability of demand for money in India using ARDL cointegration framework. The study of stability of money demand plays an important role in deciding the appropriate instruments of monetary policy. The present study examines the demand for money by using annual data over the period 1991-2015. The ARDL model bound test procedure has confirmed that a stable, long relationship exists between M1 and its determinants such as income, interest rates, exchange rates and inflation. Through results we conclude that income elasticity coefficient is positive and significant while the coefficient of inflation and interest rate is negative. Based on the Bahmoni-Oskooee and Pourhedrian (1990) argument, exchange rates negatively affect the demand for money. This study also examines the cointegration in the presence of structural break by using Gregory-Hansen single structural break approach. The study does not find any cointegration in presence of single structural break. Our result also reveals that M1 is stable between the sample period when we incorporated the CUSUM and CUSUMSQ tests.

The stability of money demand in India in the post reform period: an empirical analysis

This paper provides an empirical analysis of the stability of demand for money in India using ARDL cointegration framework. The study of stability of money demand plays an important role in deciding the appropriate instruments of monetary policy. The present study examines the demand for money by using annual data over the period 1991-2015. The ARDL model bound test procedure has confirmed that a stable, long relationship exists between M1 and its determinants such as income, interest rates, exchange rates and inflation. Through results we conclude that income elasticity coefficient is positive and significant while the coefficient of inflation and interest rate is negative. Based on the Bahmoni-Oskooee and Pourhedrian (1990) argument, exchange rates negatively affect the demand for money. This study also examines the cointegration in the presence of structural break by using Gregory-Hansen single structural break approach. The study does not find any cointegration in presence of single structural break. Our result also reveals that M1 is stable between the sample period when we incorporated the CUSUM and CUSUMSQ tests.

Revisiting the Feldstein-Horioka puzzle for Turkey

ABSTRACT The domestic saving and investment correlation as posited by Feldstein and Horioka is revisited for Turkey and tested over the whole period (1950–2017) and the two subperiods (1950–1989 and 1990–2017). The time-series properties of the data and the presence of structural breaks are properly addressed by the bounds testing procedure. Although, the investment and savings are positively correlated during the period of restricted capital mobility (1950–1989) and negatively correlated during the period of perfect capital mobility (1990–2017) according to the joint F-test on the significance of the coefficients, the long-run elasticity of investment with respect to savings ratio is significant for the whole period and for the first sub-period. The results confirm the Feldstein and Horioka hypothesis in a closed economy. However, the high, negative and insignificant long-run elasticity and non-constant coefficients in the second sub-period necessitate a full-investment model.

Структурные сдвиги и тестирование на единичный корень

his review discusses methods of testing for a unit root in a time series in the presence of structural breaks. Separately the methods for break date estimation under uncertainty over the order of integration are considered. The review covers a large number of recently developed testing methods that are robust to various types of data uncertainty, including initial conditions and time-varying volatility. The survey introduces the reader to practical application of modern testing methods together with an understanding of the development of these methods.

The impact of energy intensity, urbanisation, industrialisation, and income on CO<SUB align=right>2 emissions in South Africa: an ARDL bounds testing approach

We examine the dynamic short and long-run relationship between urbanisation, industrialisation, energy intensity, per capita GDP, and CO2 emissions in South Africa from 1970 to 2016. The Autoregressive Distributed Bounds Testing Approach (ARDL) is employed to calculate short-run and long-run relationships in the presence of structural breaks and the vector error correction model is applied to determine the direction of causality. The bounds tests suggest that the five variables are bound together in the long run when CO2 emission is the dependent variable and that urbanisation has the largest impact on CO2 emissions. Granger causality tests show a strong bidirectional causality among all the variables, with the exception of GDP and energy intensity where there is a strong unidirectional relationship running from energy intensity to GDP per capita. Our findings suggest that policymakers must develop comprehensive policies for mitigating CO2 emissions, particularly those that focus on managing rapid urbanisation.

The Impact of Energy Intensity, urbanization, Industrialization, and Income on CO2 Emissions in South Africa: An ARDL Bounds Testing Approach

We examine the dynamic short and long-run relationship between urbanisation, industrialisation, energy intensity, per capita GDP, and CO2 emissions in South Africa from 1970 to 2016. The Autoregressive Distributed Bounds Testing Approach (ARDL) is employed to calculate short-run and long-run relationships in the presence of structural breaks and the vector error correction model is applied to determine the direction of causality. The bounds tests suggest that the five variables are bound together in the long run when CO2 emission is the dependent variable and that urbanisation has the largest impact on CO2 emissions. Granger causality tests show a strong bidirectional causality among all the variables, with the exception of GDP and energy intensity where there is a strong unidirectional relationship running from energy intensity to GDP per capita. Our findings suggest that policymakers must develop comprehensive policies for mitigating CO2 emissions, particularly those that focus on managing rapid urbanisation.

Do technology and structural changes favour environment in Malaysia: an ARDL-based evidence for environmental Kuznets curve

The current study is an effort to investigate the relationship between technological innovation, structural changes, and carbon dioxide emission (CO2) along with the energy use and economic growth in the context of environmental Kuznets curve (EKC) over a period from 1985 to 2016. The study adopted the Zivot Andrews unit root test to check for the level of stationarity of the study variables. Based on the stationarity level, the study adopted autoregressive distributed lag (ARDL) approach to cointegration to investigate the presence of long- and short-run associations, simultaneously. To confirm the outcome of the ARDL bounds test in the presence of structural breaks, the study further applied the Gregory Hansen test of cointegration. The results reveal that industry value added in Malaysia has no significant impact on CO2 emissions. The results further posit that improvement in technology can lead to a cleaner environment. Regardless of improvements in technology and insignificant pressure of industrial activities towards environment, the results are not in the favour of the EKC hypothesis neither under the conventional turning point formula nor under the new turning point formula. The results reveal that long-run CO2 emission is bidirectionally related to energy use, structural changes, and technological innovation. The short-run results indicate bidirectional causalities between energy consumption, structural change, and CO2 emissions. The study suggests that the government should overhaul the structure of the energy in Malaysia to overcome both the losses in the production process and to reduce carbon emissions by inspiring the induction of environment-friendly and efficient technologies in the production process and a shift towards more services sector economy. The outcomes of the study are according to the theory of ecological modernization.

Resource curse hypothesis and role of oil prices in USA

This paper employs an augmented production function to examine resource curse hypothesis by incorporating oil prices as an additional determinant of economic growth. In doing so, the bounds testing approach to cointegration is applied in the presence of structural breaks in the series. The directional of causal association between the variables is examined by applying the VECM Granger causality approach. The empirical results show the existence of long run relationship between the variables. Moreover, natural resource abundance is negatively linked with economic growth confirms the validation of resource curse hypothesis. The nonlinear relationship between natural resource abundance and economic growth is inverted U-shaped. Oil prices add in economic growth. Capitalization increases economic growth. Labor boosts economic growth. The causality analysis reveals the unidirectional causal relationship running from natural resource abundance to economic growth. The feedback effect exists between oil prices and economic growth. Capitalization causes economic growth and in return, economic growth causes capitalization in Granger sense.