Rolling Window Regression Research Articles

Analysing the response of CO2 emissions to business cycle in a developing economy: evidence for South Africa post-apartheid era

Introduction: This research addresses the response of CO2 emissions to economic fluctuations in South Africa post-Apartheid, covering the period 1990–2018. While previous studies focused on developed countries, limited attention has been given to sub-Saharan developing nations. The study challenges the assumption of constant emissions elasticity in current forecasts.Methods: The study employs a two-step strategy. Firstly, the rolling window regression with Hodrick-Prescott filtering was used to investigate whether the CO2 emissions elasticity varies over time. Secondly, a Markov-switching approach was used to examine the regime-switching behavior in GDP.Results and Discussion: Results suggest that CO2 emisssions elasticity varies over time. This was confirmed through alternative filtering techniques (Christiano-Fitzgerald, Baxter King, and the Butterworth filter). Markov-switching analysis revealed a regime-switching behavior in GDP, indicating negative CO2 emissions elasticity during recessions and positive elasticity during expansions. These findings persist even when accounting for monetary policy shocks and productivity shocks in the Environmental Dynamic Stochastic General Equilibrium (E-DSGE) model. Noteworthy is that South Africa is among the top 20 greenhouse gas emitters globally.Conclusion and recommendations: The study recommends tailored carbon-pricing policies that are conscious to the countercyclical nature of business cycles. Pricing emissions higher during economic upswings aligns with periods of growth. Additionally, the government is advised to invest in research and development for energy conservation, efficiency, and renewable technologies to counterbalance emissions growth. Implementing emission caps and tax incentives can further enforce pollution abatement measures. Policymakers should consider these asymmetrical responses when addressing global warming challenges in South Africa.

Herding Behavior in The Asean Stock Market During The Covid-19 Pandemic

This study aims to detect indications of herding behavior that occurred in the ASEAN capital market throughout the COVID-19 pandemic. The research spanned from December 31, 2019, to June 30, 2021, encompassing the duration of the COVID-19 pandemic. This research used panel regression and rolling window regression methods on the dependent variable of Cross-Sectional Absolute Deviation (CSAD) to detect indications of herding behavior. This research also employed daily stock return data for all companies on several stock exchanges in five ASEAN countries. The panel regression model was then utilized in the analysis. The panel regression results demonstrated the absence of herding behavior on the stock exchanges in ASEAN. This was evident as the Rm2 coefficient consistently revealed a significant positive sign throughout the entire observation period. However, upon rechecking through rolling window regression, it was uncovered that, at some times, herding did occur on the ASEAN stock exchange during the COVID-19 pandemic. Furthermore, the government’s policy response by limiting short selling could reduce herding on the stock market. Simultaneously, the government’s policy response to the COVID-19 epidemic, proxied by the Stringency Index, apparently heightened investor anxiety and promoted herding behavior. Lastly, the heightened levels of anxiety and volatility experienced during the pandemic (variable IVI) led to a rise in herd behavior among the stock markets of ASEAN countries.

Does High-Speed Railway Promote the Quality of Urbanization? From a Dynamic Network Perspective

Applying the improved social-network analysis method and the idea of rolling-window regression, this paper explores the impact of an HSR network on the quality of urbanization and its dynamics. Based on a sample of 273 cities in China over the period 2009–2019, we find that the high-speed railway network has an increasingly positive effect on the quality of urbanization, which proves the existence of a network effect. The empirical results further show that this effect is closely related to the coverage rate of the high-speed rail network. In addition, heterogeneity analysis reveals that urban agglomeration cities are the main beneficiaries. Academically, our study provides a plausible explanation and evidence from network size differences for the two conflicting views of the HSR effect. Practically, we also propose some important policy implications for countries in different high-speed-rail-network construction stages.

The dynamics of crude oil future prices on China's energy markets: Quantile‐on‐quantile and casualty‐in‐quantiles approaches

AbstractThis study employs the quantile‐on‐quantile method, casualty‐in‐quantiles method, and rolling window regression to investigate the impact of international crude oil future prices on the stock prices of both traditional and new energy sectors in China. The empirical results reveal that the effect of oil future prices on the energy stock market in China varies across quantiles and is easily affected by extreme events. Specifically, the impact of oil future prices on the new energy stock market is significant and volatile, while it is less volatile and displays a negative correlation with the traditional energy market. Furthermore, a concentrated positive correlation is observed in the middle and low quantile stages of the energy stock market. A significant Granger causality exists between oil future prices and the energy stock market in different quantiles. Those findings can provide useful guidance for policymakers, investors, and consumers.

Expect the unexpected: Did the equity markets anticipate the Russo-Ukrainian war?

In 2022, Russian military operations began in Ukraine, which surprised some, while others might have expected it. We investigate whether the expectations began to be priced in the equity markets before the operations. To estimate the start of information incorporation, we use the adjusted returns of 62 country-level stock market data assuming informed traders using a non-linear model and rolling-window regressions. We find that the equity markets started to price the conflict at least 50 days before the invasion, and approximately one-third of the information was incorporated on the event day.

Is timing everything? Assessing the evidence on whether energy/electricity demand elasticities are time-varying

In this review we assess both the reasons/hypotheses for why GDP and price elasticities of energy/electricity demand might change over time, and the evidence from papers that have addressed that issue. We consider papers that have used parametric methods (e.g., endogenous or exogenous breaks, rolling window regressions) and semi- or non-parametric approaches (e.g., structural time series models, local linear estimators). The most convincing reasons for GDP and price elasticities varying over time were: (i) results from reactions to the energy price shocks of the 1970s and early-1980s (that would have affected OECD countries directly and non-OECD countries, possibly, via leapfrogging); (ii) saturation of the demand for energy services (typically at high income levels); and (iii) country-specific events such as policy changes and/or economic crises/recoveries/shocks/transitions. Despite those reasons, the weight of evidence points toward income/GDP and price elasticities of energy/electricity demand that are fairly stable over time. In addition to changes caused by reactions to the energy crises and (other) country specific events, the main exception to effectively constant elasticities is the GDP elasticity of economy-wide electricity demand in OECD countries. This elasticity may have declined substantially over time, perhaps, because the share of energy services provided by electricity has saturated. Lastly, it is recommended that modelers consider a combination of (parametric and nonparametric) approaches and search for consensus among the results.

KETAKUTAN, PERAN INVESTOR INSTITUSI DAN PERILAKU HERDING DALAM FASE PANDEMI COVID 19

This study aims to examine the effect between fear of death caused by the covid 19 virus and institutional ownership on herding behavior in companies listed on the LQ45 index (JSII) in the period from 2019 to 2021. The fear variable with the GFI index is a variable that has never been used before by previous studies to be a variable that can estimate whether fear can affect herding behavior. The sample of this study is companies listed on the LQ45 Index on the IDX from 2019 to 2021. The analysis method used is multiple linear regression assisted by Eviews in data processing. The results showed that the fear associated with the global mortality index was able to influence the occurrence of herding behavior on the LQ45 index. Meanwhile, the role of institutional ownership in companies on the IDX is unable to influence the occurrence of herding behavior. This study also proved that at the end of the year and the beginning of 2020 (honeymoon phase) using rolling window regression. It is proved that there has been a herding behavior caused by the fear of investors.

Simultaneous Inference on the Korean Won-US Dollar Forward Premium Anomaly

The forward premium anomaly, which refers to the empirical failure of the uncovered interest parity (UIP), has been primarily examined by the forward premium regression of [Fama, E. (1984). Forward and spot exchange rates. Journal of Monetary Economics, 14(3), 319–338. https://doi.org/10.1016/0304-3932(84)90046-1]. Some studies apply the rolling-window regression to capture the time-varying coefficient on the forward premium, with difficulty in statistically testing the deviation of the coefficient from the UIP over time. We follow [Baillie, R., & Kim, K. (2015). Was it risk? Or was it fundamentals? Explaining excess currency returns with kernel smoothed regressions. Journal of Empirical Finance, 34, 99–111] to apply the simultaneous inference procedure to the Korean Won-US Dollar spot and forward exchange rates by estimating the time-varying coefficient from the kernel-smoothed local-linear regression and constructing the uniform confidence band to test the local deviation. We find that, while the UIP is not rejected from the baseline regression, the simultaneous inference shows that the deviation from the UIP is mainly observed during the early 2000s and 2010s. Time-variation of the forward premium coefficient tends to be significantly affected by economic uncertainties such as the interest rate, inflation, and stock return volatilities in Korea and US.

Herding Behavior in the Market for Green Cryptocurrencies: Evidence from CSSD and CSAD Approaches

Green cryptocurrencies have been recently created to reduce energy consumption and environmental pollution by adopting alternative mining practices. This paper examines for the first time the market of green cryptocurrencies for indication of herding behavior in the period of January 2017–June 2022. By using two measures that capture the proximity of asset returns from the market consensus, we conclude that herding behavior among investors in green cryptocurrencies was absent in the whole sample. However, the results of a subsample analysis and rolling window regression show that herding dynamics varied significantly throughout the sample period. The recent COVID-19 pandemic amplified the observed levels of herding behavior, suggesting that opportunities for diversification for investors operating in this market may have become more limited lately. For this reason, financial regulators should focus on the market of green cryptocurrencies if they want to promote the market’s efficiency necessary to attract additional investors.

What Is the Temporal Path of the GDP Elasticity of Energy Consumption in OECD Countries? An Assessment of Previous Findings and New Evidence

This paper answers the question: what is the path of the GDP elasticity of economy-wide energy consumption for OECD countries over the period 1960–2019? To do so, this study first considers the arguments as to why this elasticity might change over time, and then reviews the previous evidence on whether this elasticity has changed over time. Lastly, the paper compiles and uses a new dataset to analyze whether the GDP elasticity of energy demand in OECD countries (i) has changed between the periods before and after the major energy crises (e.g., 1974–1985); and (ii) has been stable since 1986. Elasticity stability is analyzed via rolling window regressions using dynamic mean group cross-correlated errors. We argue that (i) the GDP elasticity for economy-wide energy consumption was around unity for OECD countries prior to the first energy crisis; and (ii) the reactions to the extreme oil price experiences that occurred over 1974–1985 led to a substantially lower GDP elasticity for economy-wide energy consumption of around 0.6 that has been stable at that level since the end of the second energy crisis (circa 1986). This demonstration of the path of the GDP elasticity is in contrast to some recent work that has suggested the GDP elasticity of energy has not changed (or changed very little) since the 1970s or even since the 1960s. Furthermore, this evidence that reactions to those extreme oil price experiences led to a step-function-like lowering of the GDP elasticity runs counter to other arguments that dematerialization, inverted-U-based development paths, or Kyoto Protocol ratification are responsible for continued declines in the GDP elasticity.

Foreign capital inflow and housing prices in a developing country: an advanced econometric enquiry

PurposeThe study is intended to find out the relationship between housing prices and the inflow of foreign capital in Pakistan. There is a shortage of housing units due to rising population and rural–urban migration since its inception; on the other hand, there is also a lack of housing finances. The urban sprawl has created the demand for housing units, but the supply of housing has not been increased up to the required level, the major reason is a deficiency of housing finances.Design/methodology/approachThe analysis was carried out from 1973 to 2018, on an annual, quarterly and monthly basis; the structural changes are captured by the Zivot–Andrews unit root test. Gregory–Hansen test is used for cointegration, the combined cointegration also validates the results. In addition, the rolling window is used to capture timely changes between data sets. Finally, wavelet analysis is used to prove volatility.FindingsThe rising prices of housing in the country is alarming; Pakistan is a developing country, and it is facing many problems along with a housing shortage. The domestic sources of housing finances are inadequate, so foreign funds are welcomed. The rolling window regression proves that domestic factors along with the foreign capital inflow affect housing prices positively, and the wavelet analysis finds out that foreign direct investment is more volatile than workers’ remittances in financing the housing market.Originality/valueThis is a pioneering study to find out the impact of foreign capital inflows on the housing prices in the economy of Pakistan. The inadequacy of housing finances from domestic sources attracted foreign funds financing this sector. This study has used new techniques like rolling window and wavelet transformation, such techniques have not been used before.

Bitcoin Under Inflation-Hedging Scrutiny - The Extended Fisher Hypothesis in Crypto

Improved access to the scarce, digital resource justifies analyzing Bitcoin from a macroeconomic perspective. Using ten years of historical monthly returns, this study examines Bitcoin’s hedging capabilities with respect to inflation measures following the Fisher hypothesis. Short-term interest rates are used to split the ex post observed inflation into an ex ante expected and unexpected part by applying autoregressive distributed lag models. Having 40 countries under scrutiny, the results show that Bitcoin has partial hedging capabilites against expected inflation in specific countries. In a cross-sectional view on expected inflation, rolling window regressions reveal a diminishing number of significant inflation betas over time.

The Dynamic Progression of the Redenomination and Sovereign Risk in the Price Discovery Process of Italian Banks’ CDSs

The recent global financial crisis and the subsequent sovereign debt crisis of the Eurozone peripheral countries have generated historic levels of volatility and instability in the financial markets. In particular, during the sovereign debt crisis market operators have begun to focus on the so-called “redenomination risk”, that is the hypothesis of exit from the EMU (Euro Monetary Union) by one or more countries and the consequent redenomination of their debt in the past national currency. This type of risk constitutes a form of additional credit risk premium due to expected systemic failure of the Eurozone. The effects of the economic-financial crisis, the weak economic growth and the political instability that have characterized especially the Italian system in recent years provide the ideal starting point to analyze the evolution of the redenomination risk in the pricing process of the Italian banks’ CDSs (Credit Default Swaps). The contribution of this work is to evaluate the dynamic evolution of sovereign and redenomination risk in the price discovery process of the Italian banks’ CDS spreads (or premia) by using rolling window regressions. Results show that redenomination risk explains a great part of the variance in the CDS spreads during periods of financial distress. The sovereign risk component explains a large part of the variance for almost the entire considered period. JEL Classification: G01, G12, G14, G20. Keywords: CDS spreads, Sovereign risk, Redenomination risk, Rolling window regressions, ISDA basis.

Análisis sobre la evolución del COVID-19 en Colombia: ¿se alcanzará el pico de contagio?

Se realiza un análisis de la evolución de los casos diarios de contagios y el número de muertes para Colombia y algunas ciudades con base en información oficial reportada por el Ministerio de Salud, con el fin de dar respuesta a la siguiente pregunta: ¿dada la trayectoria actual del nuevo coronavirus, es posible estimar el futuro pico de contagios? A partir de la construcción de una estructura de datos de panel, se estiman modelos con tendencia cuadrática temporal para el logaritmo del número de contagios y muertes por COVID-19. La estimación de la dinámica de los contagios y de las muertes de manera recursiva a partir del método de regresiones rolling window muestra un buen ajuste, permitiendo predecir cuándo se alcanzará el pico de la epidemia en función de los nuevos casos reportados oficialmente. Se estiman algunos factores asociados a la evolución de la pandemia, especialmente el efecto de la velocidad de contagio (tasa de reproducción efectiva, Ro) sobre la trayectoria futura de los contagios diarios. Además, se intenta estimar el impacto de las medidas de aislamiento preventivo adoptadas por las autoridades nacionales y regionales. Los resultados muestran que el pico de la pandemia en Colombia se alcanza aproximadamente a finales de agosto, período que coincide con estudios epidemiológicos más elaborados. El número promedio de nuevos casos diarios en el pico alcanzaría más de 10.000 contagios, mientras que el máximo número de fallecidos en el pico sería de 44.471. La estimación del impacto de las medidas de confinamiento muestra que se logra aplazar el pico y reducir el número de contagios y muertes.

Forecasting Islamic equity indices alpha

PurposeThis paper aims to investigate whether Islamic and conventional equity indices offer some alpha. These indices are expected to offer no alpha being value-weighted, passive and unmanaged.Design/methodology/approachThis paper used monthly data from 1996 to 2016 of four Dow Jones (DJ) and one financial times stock exchange (FTSE) Islamic equity indices and five conventional Morgan Stanley Capital International (MSCI) equity indices. This study used a simple ordinary least square (OLS) rolling window regressions to generate the alphas and risk loadings when adjusting for prominent pricing factor models.FindingsThe findings from OLS regressions suggest that DJ Islamic indices of Japan, Europe and World generate significant alphas, whereas, MSCI conventional indices of Asia/Pacific, USA and World generate significant alpha when risk-adjusted for pricing factor models. However, in 36-month rolling window regressions, all Islamic indices generate significant alpha and factor loading. The magnitude of alpha and factor loading changes over time.Research limitations/implicationsThe finding shows that the Shari’ah-compliant investment fund’s alpha must be adjusted with the respective benchmark index alpha to measure the fund manager’s skill performance quantitatively.Originality/valueTo the best of the author’s knowledge, this is the first study that investigates and compares the Islamic, as well as conventional indices for abnormal returns, which are adjusted for both Fama–French five and q-theory-based four assets pricing risk factors and as a benchmark for Shari’ah-compliant fund’s performance.

Knowledge recombination along the technology life cycle

This study sheds light on how recombination of different kinds of knowledge changes along the technology life cycle. From a theoretical point of view, the cyclical technology life cycle model is extended to account for the influence of recombination of different kinds of knowledge in the different life cycle phases. This model is empirically tested for the technological life cycle of wind power and photovoltaics in Germany for the period from 1970 until 2006. Patent forward citations are considered as recombinatorial success and inventors’ patenting experience proxy different kinds of knowledge. Negative binomial regressions as well as rolling-window regressions are used to estimate the relevance of different kinds of knowledge along the technology life cycle. Results reveal that different kinds of knowledge matter along the technology life cycle. In the era of ferment, knowledge from domains external to the technology is relevant, but for the dominant design and the era of incremental change, new and specialized knowledge is most important. However, there are technological differences and deviations from the model. Rolling-window regressions reveal nuanced changes in the life cycle phases. The results have several policy and management implications, especially for the timing of whom to fund or hire for inventive activity.

Measuring Trump: The Volfefe Index and its impact on European financial markets

In this study, we examine the predictive power of the recently constructed Volfefe Index, the quantification of the tweeting activity of U.S. president Donald J. Trump, on the dynamics of European stock markets. After controlling for a set of macroeconomic and financial factors, we show that the Trump Tweet factor contributes to the prediction of European stock market returns. The results obtained from a rolling-window regression model indicate that the relationship between the Volfefe Index and European stock market returns is heterogeneous and time-varying. These dynamics coincide surprisingly well with a series of presidential tweets, identifying the directional effect of the Trump Twitter factor.

Dynamic Optimality of Airline Fuel Cost Hedging

Hedging creates value only when the policy is near optimal but can be harmful otherwise. This paper takes the US airline industry as an example and derives the optimal fuel cost hedging ratio as a function of firm-specific revenue and cost sensitivities, as well as the relative composition of demand and supply shocks in the oil price movement. We construct a market hedging demand index based on rolling-window regression of crude futures returns on equity index returns and use the index to capture the time-variation in the fuel cost hedging demand for a typical airline. By regressing the logarithm of Tobin's Q against the hedging ratio under different market conditions, we show that fuel cost hedging increases firm value only when the market hedging demand is high. More important, we use the time-series correlation between an airline's hedging ratio and the market hedging demand to measure the dynamic optimality of the airline's fuel hedging practice. Out of the 33 US airlines in our sample over a 25-year period, one third do not hedge at all, while the hedging ratios for more than another one third move in the opposite direction of the market hedging demand. Only less than one third of airlines show positive dynamic optimality for their hedging practice. The cross-sectional diversity of the optimality estimates highlights the inherent difficulty of implementing an optimal policy. Still, we find strong value in staying dynamically optimal in an airline's fuel cost hedging practice: The dynamic optimality of each airline's hedging practice strongly and positively predicts its valuation as measured by the logarithm of its Tobin's Q. The value increase comes from both reduced variation in its return on asset and increased average return. Airlines with negative hedging dynamic optimality not only are ineffective in reducing their return on asset variation, but also incur extra costs from setting up and maintaining a costly hedging program and from entering and exiting hedging positions. Such extra costs lower the airlines' average return and hurt their valuation. As a result, their average Tobin's Q is even lower than the average for airlines that do not hedge at all.

Time-varying international market power for the Chinese iron ore markets

This study analyses the dynamics of iron ore's international market power on exports to China by Australia, Brazil, India and South Africa. It uses the residual demand elasticity (RDE) model and rolling window regression model and applies the monthly data for the international iron ore market over the period from January 2006 to December 2016. The results indicate that (1) Australia and Brazil exercise market power in the Chinese iron ore market, but India and South Africa have no market power in the iron ore market; (2) market power has a time-varying nature, particularly in Australia and Brazil; and (3) changes in the pricing regime in the international iron ore market have a profound impact on market power. Furthermore, to maintain the stability of the Chinese iron ore market, exporters are suggested to enhance their competitiveness and gain greater market power on the basis of maintaining market share, and China should optimize its trade structure.

Rolling window regression of δ13C and δ18O values in carbonate sediments: Implications for source and diagenesis

AbstractDiagenetic alteration can produce, modify or erase significant biogeochemical information recorded in carbon and oxygen (δ13C and δ18O) values of marine carbonates throughout geological time. Understanding the type and extent of alteration experienced by a carbonate deposit can improve sedimentological and geochemical interpretations of events in Earth history. In this study, we present a new application of a statistical approach to aid in the identification and interpretation of sedimentological surfaces in a shallow marine carbonate sequence using a rolling window regression (RWR) analysis. RWR analysis evaluates the degree of covariation between two records and how it changes through time. Geological application of this statistical technique permits a new perspective on the fine scale variability in carbon and oxygen isotope records and the processes that generate them and provides a complementary tool for sedimentological interpretations. In this study, we apply RWR to δ13C and δ18O values from the Clino drilled into the western margin of Great Bahama Bank, a core that has been extensively altered by diagenetic processes, within the vadose, phreatic and marine burial zones. This core penetrates ~676 m of shallow marine to deeper slope deposits and contains a variety of both sedimentological and diagenetic events, including facies transitions, subaerial exposure surfaces, marine hardgrounds, firmgrounds and periods of reduced sedimentation rate. Using more than 1,200 analyses of paired δ13C and δ18O values, we have applied a RWR analysis to evaluate how the correlation between these two proxies changes at scales of 10, 30 and 100 m. The results of this study highlight the dynamic evolution of correlations between δ13C and δ18O values within diagenetic zones and in association with diagenetic surfaces and provide context for interpreting covariance in δ13C and δ18O values from the geological record.