Structural Breaks Research Articles

A MS SHARV-MIDAS model: a new regime-switching model for volatility forecasting

ABSTRACT The recent SHARV and SHARV-MIDAS models incorporate current returns information for volatility forecasting. However, these models fail to capture the intricate transition process of volatility states due to structural breaks caused by extreme events in financial markets. Therefore, we introduce a Markov regime-switching into the SHARV-MIDAS model and develop the MS SHARV-MIDAS model that comprehensively accounts for structural breaks, long memory, and current return information in volatility forecasting. Furthermore, we examine the effectiveness of this model through empirical studies and robustness tests, which demonstrate its superiority over benchmark models regarding in-sample fitting and out-of-sample volatility forecasting.

Fintech industry risk: does investor attention matter?

ABSTRACT The Fintech industry risk challenges the stability of China’s financial system. This paper makes the first attempt to capture the dynamics of the Fintech industry risk, represented by Fintech stock volatility, from the investor attention perspective. The novelty of this paper is that we fully consider the regime changes in the Fintech industry risk by adopting the AR-MS-GARCH-MIDAS with the Markov regime-switching process incorporated in the short-run volatility component, long-run volatility component, or both. We find that (1) Fintech industry risk is subject to structural breaks, (2) higher investor attention tends to alleviate the Fintech stock volatility, while the negative impact strengthens when the industry is in volatile condition, (3) investor attention contributes to improving the accuracy of the Fintech stock volatility forecasting, (4) considering the structural breaks of investor attention is important in volatility forecasting. We enrich the literature on the Fintech industry risk regulation, stock volatility forecasting, and the attention allocation theory.

Analysing the Nexus: Stock Indices and Cryptocurrencies During the Conflict Between Russia and Ukraine

Background: The global economy faced increased instability due to the simultaneous occurrence of two major events, the global pandemic in 2020 and the Russian-Ukrainian conflict in 2022, which impacted the financial markets. Purpose: This paper aimed to evaluate the comovements between the USA (S&P 500), Germany (DAX 30), France (CAC40), Japan (Nikkei 225), Canada (TSX), Russia (MOEX) and Ukraine (PFTS) stock markets and the cryptocurrencies Bitcoin (BTC), Ethereum (ETH), Litcoin (LTC) Dash (DASH/USD), Ripple (XRP) DigiByte (DGB) and Nem (XEM), from February 24, 2022, to April 12, 2023 Methods: The approach to our research question will involve using the causality econometric model, Granger SVAR (Vector Autoregressive). Results: The results showed that stock indices and digital currencies show sharp structural breaks, and not all markets influence cryptocurrencies. The MOEX stock market affects the price formation of BTC, ETH, DGB, XEM, and XRP, while the DAX 30 stock index impacts ETH, LTC, DASH, DGB, and XEM. The Ukraine market (PFTS) influences ETH, but the other stock markets do not influence any of the cryptocurrencies analysed. Conclusion: Investors, policymakers, and other participants operating in the digital currency markets can find valuable information in the study's conclusions when seeking to rebalance their portfolios.

Economic policy uncertainty and equity index in sub-Saharan African (SSA) countries: accounting for multiple structural breaks in a panel framework

Economic policy uncertainty and equity index in sub-Saharan African (SSA) countries: accounting for multiple structural breaks in a panel framework

Simmering tensions on the Russia–Ukraine border and natural gas futures prices: identifying the impact using new hybrid GARCH

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

Estimating price elasticity of demand for mineral commodities used in Lithium-ion batteries in the face of surging demand

The accelerating adoption of clean energy technologies is driving demand for certain mineral commodities like lithium, essential for electric vehicle batteries. Understanding the influence of the energy transition on each market requires examining their supply and demand price elasticities. However, there have only been a limited number of studies that have estimated mineral demand price elasticities in emerging technologies. We empirically estimate the price elasticity demand (PEDs) for mineral commodities used in lithium-ion battery cathodes and anodes—cobalt, graphite, lithium, manganese, and nickel. We also test whether the price elasticities of these mineral commodities have changed due to any structural changes in recent years, given the recent expansion in the sales of electric vehicles. Using Two Stage Least Squared (2SLS) econometric techniques, we find that demand has become less elastic for lithium (-0.11), cobalt (-0.45), nickel (-0.09), and manganese (-0.04) after structural breaks in 2020, 2013, 2009, and 2014, respectively. In contrast, demand for natural amorphous graphite (-0.36), and natural flake graphite (-0.58) remains relatively stable over the time period assessed.

Revisiting the interdependences across global base metal futures markets: Evidence during the main waves of the COVID-19 pandemic

This paper examines the correlation and connectedness across global base metal futures markets in the periods before and during the COVID-19 pandemic. We find that the impacts of the pandemic shock on the dynamic correlations between global commodity exchanges vary across futures markets, with significant structural breaks being detected in the underlying dynamic correlations at different stages of the pandemic. We document that stronger price signals were generated in the aggregate markets due to the pandemic shock, and the roles of copper and zinc futures as the pricing center in the global base metal markets increased during the pandemic period. Moreover, our findings show that the leading position of the London Metal Exchange in the global base metal futures pricing declined, while the pricing authority of the Shanghai Futures Exchange improved significantly during the pandemic time. Leveraging post-pandemic data, we identify interdependence structures amid some crucial geopolitical conflicts.

Do socially responsible indices outperform conventional indices? Evidence from before and after the onset of Covid‐19

AbstractAccording to traditional portfolio theories constraints, restrictions, and screens applied in portfolio selection reduces the diversification opportunities which can impact financial performance. Is this true in the case of socially responsible investment (SRI)? To answer this, present study analyzes the performance of Socially Responsible (SR) indices in comparison to conventional indices in an emerging economy. The uniqueness of the study is that it analyses the performance of Shariah, ESG, and thematic indices in a single study. Further, the study measures the impact of Covid‐19 on them. Comparative performance evaluation was conducted by using absolute return analysis and risk‐adjusted measures namely, Sharpe ratio, Treynor ratio, tracking error, information ratio, capital asset pricing model (CAPM), Fama–French three‐factor, and Carhart's four‐factor models. The Structural break was identified, hence analysis was conducted for the total period (January 2017–March 2023) and two sub‐periods, that is, pre and post‐Covid‐19 period. No significant difference was found between the returns of SR indices and conventional indices as against the benchmark index on the basis of absolute return analysis. Sharpe ratio and Treynor ratio both were having negative values for all the SR and conventional indices. Tracking error for all the SR and Conventional indices were very low. The CAPM and both multi‐factor models univocally pointed toward the underperformance of all the SR (except S&P BSE 100 ESG index which had equal performance) and both conventional indices against the benchmark index. Noteworthy point is that only Shariah indices gave the highest returns during post‐Covid period. This research will help in deepening the SRI in the capital market. Companies should increase their ESG scores and make efforts to be listed on the SR indices. Policymakers should announce some kind of rebates, or recognition for star‐performing companies in the field of sustainability to encourage other companies to adopt SR practices in their business operations. The novelty of the current study is that it adds to the socially responsible literature by analyzing the performance of Shariah, ESG, and Thematic indices and conventional indices in a single study in the fastest‐growing economy of India and analyses the impact of Covid‐19 on this performance.

Exploring inflation dynamics in Canada: A threshold vector autoregressive approach

Given the pivotal role of inflation expectations in contemporary monetary policy, we posit that if monetary policy has effectively influenced inflation expectations, thereby altering the trajectory of total inflation, a structural break in the path of total inflation should be observable. Conversely, if inflation expectations have remained stable and monetary policy has had limited impact, a stable vector autoregressive (VAR) model should adequately describe the path of total inflation. To address these hypotheses, a non-linear specification of a threshold vector autoregressive (TVAR) model is employed, offering a comprehensive analytical framework for the examination of these dynamics.

What a Puzzle! Unravelling Why UK Phillips Curves were Unstable

AbstractThe UK relationship between nominal wage inflation and the unemployment rate is unstable. Over sub‐periods of the last 160 years of turbulent data, Phillips curve slopes range from strongly negative, slightly negative, flat, slightly positive and strongly positive. Our constant‐parameter congruent model of real wages explains these instabilities, yet also implies a constant negative relationship between nominal wage inflation and the unemployment rate when corrected by its regressors. Disentangling these effects reveals that structural breaks in the real‐wage model's variables do not explain the instabilities, which instead occur during sub‐periods when some of its explanatory variables are insignificant.

Asymmetric Exchange Rate Effects on Trade Flows in India

This paper examines the role of exchange rate changes on India’s trade. The drivers of exports and imports (income, exchange rate including sectoral differences, and exchange rate variability) are estimated for the short and long run including a structural break. Using annual data from 1994 to 2022, the results of dynamic fixed effects estimation show that both exports and imports are income-elastic in the short and long run, but income elasticity is far stronger for exports. Moreover, exports are responsive to the real effective exchange rate in the short run but not in the long run, and the reverse is true for imports. Furthermore, exchange rates have asymmetric effects for high-volume and primary sectors for exports and imports. The combined impacts show the ineffectiveness of using currency depreciation to address trade imbalances.

CLIMATE RISKS AND PREDICTABILITY OF COMMODITY RETURNS AND VOLATILITY: EVIDENCE FROM OVER 750 YEARS OF DATA

We analyze whether metrics of climate risks, as captured primarily by changes in temperature anomaly and its stochastic volatility (SV), can predict returns and volatility of 25 commodities, covering the overall historical period of 1258 to 2021. To this end, we apply a higher-order nonparametric causality-in-quantiles test to not only uncover potential in-sample predictability in the entire conditional distribution of commodity returns and volatility but also to account for nonlinearity and structural breaks which exist between commodity returns and the metrics of climate risks. We find that, unlike in the misspecified linear Granger causality tests, climate risks do predict commodity returns and volatility, though the impact on the latter is stronger, in terms of the coverage of the conditional distribution. Insights from our findings can benefit academics, investors, and policymakers in their decision-making.

Analyzing Nexus between Crude Oil, Gold, Dollar and Equity Markets with Structural Break: ARDL Evidence from India

This paper explores the price relations between the Crude oil, Gold, US dollar, and equities during several economic episodes. The long- and short-term causal relationships between asset markets are examined in this study. The research uses a ‘Vector Error Correction Model (VECM)’ and an ‘Autoregressive Distributed Lag (ARDL)’ bounds test to examine monthly data from April 1999 to March 2022. The results demonstrate the weak connections between asset markets throughout the different sample periods. Furthermore, the integration of most markets is uneven and changes over time. The currency and equity markets are adjusting to the long-run equilibrium only at a slow pace. We suggest that systematic risk factors must be taken into account while jointly modeling market linkages. This study improves on previous research in the subject by demonstrating the time-varying effects of asset price links on portfolio optimizations in different economic episodes.

MSE superiority of the unrestricted Stein-rule estimator in a regression model with a possible structural break

This paper investigates the estimation of a linear regression model with a possible structural break at a known point. We analytically derive the exact formulae of the MSE for the restricted SR and PSR estimators, which shrinks the OLS estimator toward the restriction of no structural break. We compare the MSE performance of restricted/unrestricted SR, PSR, and least squared estimators. We analytically show that the unrestricted SR estimator can have a smaller MSE than the restricted SR estimator even when the restriction is correct. Further, our numerical results show that the unrestricted PSR estimator has the best MSE performance over a wide region of parameter space. These results indicate that the use of the unrestricted PSR estimator is recommended even when a structural break may not exist, although the unrestricted PSR estimator does not take the possibility of no structural break into consideration.

Factor Selection and Structural Breaks

We develop a new approach to select risk factors in an asset pricing model that allows the set to change at multiple unknown break dates. Using the six factors displayed in Table 1 since 1963, we document a marked shift towards parsimonious models in the last two decades. Prior to 2005, five or six factors are selected, but just two are selected thereafter. This finding offers a simple implication for the factor zoo literature: ignoring breaks detects additional factors that are no longer relevant. Moreover, all omitted factors are priced by the selected factors in every regime. Finally, the selected factors outperform popular factor models as an investment strategy.

Impact of Portfolio Performance on International Portfolio Diversification

Investment opportunity is what we all are looking for every now and then, avenues that can generate maximum return with minimum of risk. International Portfolio diversification may not fulfill aspiration of investors with globalization spreading its wings in each sphere of economic activity. The study has been conducted from the perspective of Indian Investor taking into account 22 foreign markets (11 emerging and 11 developed). The countries have been selected from two points of view; firstly on the basis of market capitalization and secondly on the basis of availability of data. Emerging markets chosen for the study contribute more than 80% in Emerging market Index which is sufficient to generalize the results to the whole. Geographical regions have also been taken into consideration in order to benefit from regional diversification; these are Asia-pacific with 13 markets, 8 markets from Europe, 3 from Latin America and USA. Firstly, portfolios with different investment objective would be chosen on the basis of sampled countries. Then testing co-integration study would help in elimination of those countries generating co-integrating vector. Hence, Indian investor would be benefited by the study to find out whether globally diversified portfolio is better or not as compared to domestic portfolio. International portfolio diversification can be suggested for Indian investor in the wake of its performance during structural breaks.

Investigating the crowding effect of FDI on domestic investments: Evidence from Bangladesh

This study empirically investigates the crowding effect of Foreign Direct Investment (FDI) on domestic investments in Bangladesh, utilizing annual time series data from 1972 to 2022. Initially, unit root tests are conducted with and without considering structural breaks in the dataset. This study employs the Johansen test of cointegration to investigate the enduring association between the variables and utilizes the Vector Error Correction Model (VECM) to accommodate this relationship over the long term. Following the estimation of the VECM, formulas about the magnitude of the crowding effect (CE) are applied to examine the impact of FDI on domestic investment in Bangladesh. Results indicate that FDI positively influences domestic investments in both the short and long run.

Structural Breaks and Co-Movements of Bitcoin and Ethereum: Evidence from the COVID-19 Pandemic Period

Abstract This study examined the structural breakdowns and co-movements of Bitcoin (BTC) and Ethereum (ETH) cryptocurrencies from the onset of the COVID-19 pandemic. The Bai-Perron test was used to determine the change in the mean and variance of the two principal actors regarding market capitalization in the cryptocurrency market. Wavelet coherence analysis was also used to detect the co-movements between BTC and ETH. As a result of the study, several similar breaks were seen in each BTC and ETH series. Only one break could be directly associated with the pandemic process. This means that the pandemic is internalized and normalized in the process. The wavelet coherence results indicate a strong positive dependency (dark warm colours) between BTC and ETH and in phase (in the same direction) in the short and long bandgaps.

Sex differences in 3- to 5-year-old children's motor competence: A pooled cross-sectional analysis of 6241 children.

There is some, albeit inconsistent, evidence supporting sex differences in preschoolers' motor competence (MC), with these observations not uniform when analyzed by age, and cultural groups. Thus, this study examined sex differences across ages in 3- to 5-year-old children's MC. A cross-country pooled sample of 6241 children aged 3-5 years (49.6% girls) was assessed for MC using the Test of Gross Motor Development-2nd/3rd edition, and children were categorized into groups of age in months. Multiple linear regression models and predictive margins were calculated to explore how sex and age in months affect scores of MC (i.e., locomotor and ball skills), with adjustments for country and BMI. The Chow's Test was used to test for the presence of a structural break in the data. Significant differences in favor of girls were seen at 57-59 and 66-68 months of age for locomotor skills; boys performed better in ball skills in all age periods, except for 42-44 and 45-47 months of age. The higher marginal effects were observed for the period between 45-47 and 48-50 months for locomotor skills (F = 30.21; and F = 25.90 for girls and boys, respectively), and ball skills (F = 19.01; and F = 42.11 for girls and boys, respectively). A significantly positive break point was seen at 45-47 months, highlighting the age interval where children's MC drastically improved. The identification of this breakpoint provides an evidence-based metric for when we might expect MC to rapidly increase, and an indicator of early delay when change does not occur at that age.

The calibration of initial shocks in bank stress test scenarios: An outlier detection based approach

The shock scenarios used in the practice of stress testing are often based on ad hoc hypotheses concerning the evolution of risk factors. Consequently, they have been criticized for reflecting mild and temporary shocks maintained over very short periods. The aim of this article is to propose methodological improvements to the design and calibration of initial shocks based on the detection of outliers and structural breaks in (macro)economic and financial data. Our results of the real-world implementation of outlier detection algorithms show that the dynamics of shocks, the length of the stress horizons, and their magnitude are sensitive to the type and nature of the considered risk factor. The inferred stress horizon is longer (one to two years) for macroeconomic variables like GDP, real estate or oil prices, than for interest rate variables and the slope of the yield curve (six months).