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The authors analyse the nature of nonlinear long-run causal dynamics between VIX futures and exchange-traded products (ETPs).

Nonlinear long-run causal relations between daily price movements in ETPs and futures are established through a Markov switching vector error correction model (MS-VECM).

The authors observe time variation in causality with the volatility of volatility. In particular, demand pressures for VIX ETNs and futures can change in different regimes. The authors observe two regimes where regime 1 is classified as low-mean low-volatility, while regime 2 is classified as high-mean high-volatility. The convergence to the long-run equilibrium in the low-mean low-volatility regime is faster than the high-mean high-volatility regime. The nature of the time varying lead lag relations demonstrates the opportunities for arbitrage.

The linear causal relations between VXX and VIX futures are well established, with leads and lags generally found to be short-lived with arbitrage relations holding. The authors go further to capture the time-varying causal relationships through a Markovian process. The authors establish the nonlinear causal relations between inverse and leveraged products where causal relations are not yet documented.

Recent surges in inflation have posed significant challenges for Türkiye, with the annualinflation rate culminating at 83.45% by the close of 2022. The purpose of the study is to take a closer look at the details behind the rising inflation trend in Türkiye.

Due to the time-varying nature of the relationship of the variables, dynamic conditional correlation-generalized autoregressive conditionally heteroscedastic (DCC-GARCH) models and the Markov switching model are used as analytical tools. Leveraging the DCC methodology proposed by Tse and Tsui (2002), this study examined time-varying correlations, while the effect of the weighted sum of past correlations was captured using the DCC-GARCH approach introduced by Engle (2002).

The findings from the DCC models highlight that the exchange rate plays the most pivotal role in influencing inflation, closely followed by the money supply. In addition, the Markov switching analysis, rooted in the Phillips curve concept, identified two statistically significant regimes. The results emphasize that components of the money supply and the exchange rate stand out as primary drivers of Türkiye’s heightened inflation rates. To promote sustainable development in Turkey, the Central Bank should focus on inflation targeting, managing the money supply to align with GDP growth and adopting adaptive inflation responses.

To the best of the authors’ knowledge, this paper is the first attempt to use a combination of the DCC and Markov switching models to examine Turkish inflation from December 2005 to October 2022, according to a thorough review of previous research. Such an innovative method provides a new perspective on inflationary patterns throughout this time. In addition, this study departs from traditional approaches by including money supply measures in the analysis.

The primary purpose of this study is to unveil the relationship between oil prices and exchange rates, with a specific focus on five major oil-importing countries. By examining this relationship, the research aims to provide valuable insights for policymakers, investors and stakeholders operating in the global economic landscape.

The study employs a methodological approach to ensure robust and reliable findings. First, we assess the stationarity of the time series data to establish a solid analytical foundation. Subsequently, we construct GARCH(1,1) models to capture the persistence of the volatilities inherent in the data. Building upon this, we propose the novel application of the Markov-switching R-vine copula approach, which enables us to capture structural changes and measure the dependencies between oil prices and exchange rates.

Our findings uncover significant negative relationships between oil prices and exchange rates across the examined economies while revealing varying degrees of interdependency among these variables. Notably, we elucidate distinct tail dependence structures, encompassing both symmetric and asymmetric aspects, which hold profound implications for risk assessment and portfolio management strategies. Furthermore, this study confirms the presence of regime-switching dynamics, elucidating how the co-movement patterns between oil prices and exchange rates evolve across different states or regimes, reflecting the dynamic nature of these interconnected markets.

The originality and value of this study lie in its comprehensive approach to understanding the relationship between oil prices and exchange rates. By accounting for structural changes and regime-switching behaviors, the research provides a nuanced understanding of the complex dynamics at play. The novel application of the Markov-switching R-vine copula approach contributes to the methodological advancement in this field of study. Furthermore, the insights derived from this research offer practical implications for policymakers, investors and stakeholders navigating the complexities of the global economic landscape, enabling them to make informed decisions and develop effective strategies to mitigate risks and capitalize on opportunities.

The purpose of this study is to achieve a comprehensive understanding of how the intricate interconnections between oil price fluctuations, supply chain disruptions and shifting demand patterns collectively shape inflation dynamics within the Chinese economy, especially during critical periods such as the Covid-19 pandemic and geopolitical events like the Russia–Ukraine conflict. The importance of assessing the impact of oil price volatility on China’s inflation becomes particularly pronounced amidst these challenging circumstances.

This study uses the Markov Regime-Switching generalized autoregressive conditional heteroskedasticity (MRS-GARCH) family of models under student’s t-distributions to measure the uncertainty of oil prices and the inflation rate during the period spanning from 1994 to 2023 in China.

The results indicate that the MRS-GJR-GARCH-in-mean (MRS-GARCH-M) models, when used under student’s t-distributions, exhibit superior performance in modeling the volatility of both oil prices and the inflation rate. This finding underscores the effectiveness of these models in capturing the intricacies of volatility dynamics in the context of oil prices and inflation. The study has identified compelling evidence of regime-switching behavior within the oil price market. Subsequently, the author conducted an analysis by extracting the forecastable component, which represents the expected variation, from the best-fitted models. This allowed us to isolate the time series of oil price uncertainty, representing the unforecastable component. With this unforecastable component in hand, the author proceeded to estimate the impact of oil price fluctuations on the inflation rate. To accomplish this, the author used an autoregressive distributed lag model, which enables us to explore the dynamic relationships and lags between these crucial economic variables. The study further reveals that fluctuations in oil prices exert a noteworthy and discernible influence on the inflation rate, with distinct patterns observed across different economic regimes. The findings indicate a consistent positive impact of oil prices on inflation rate uncertainty, particularly within export-oriented and import-oriented industries, under both of these economic regimes.

This study offers original value by analyzing the impact of crude oil price volatility on inflation in China. It provides unique insights into the relationship between energy market fluctuations and macroeconomic stability in one of the world’s largest economies. By focusing on crude oil – a critical but often overlooked component – this research enhances understanding of how energy price dynamics influence inflationary trends. The findings can inform policymakers and stakeholders about the significance of energy market stability for maintaining economic stability and guiding inflation control measures in China.

Start-ups are successful in receiving valuation in billions of US dollars prior to initial public offering (IPO). However, to sustain higher valuation, the stocks need to perform consistently after the IPO. Short-run stock performance of India-based start-ups during the first year of IPO listing from March 2021 to March 2022 is analysed.

The paper deals with the new generation start-ups' stock performance in emerging market in terms of total and abnormal return generated in comparison to the market (NIFTY-200). Further, the volatility of returns during bear and bull regimes is analysed through a family of Markov-switching GARCH models using both normal and skewed distributions.

The results suggest that start-up stocks are more volatile during bear regime than in the bull run in market-based economies where price limit policy does not apply. Besides, the cumulative abnormal return over the market return was lower for majority of start-up IPO stocks.

Though negative returns of the start-up stocks during the first year of IPO need not be surprising, higher volatility during bear regime is a matter of concern as it could severely impact retail investors and founders. The results hold implication for IPO regulation in emerging markets and for retail investors desirous of investing in start-up stocks.

Volatility of return is examined using a state-space model during the first year of the start-up IPO listing. The study contributes to the emerging market IPO literature by examining IPO performance in market-based economy. Previous IPO performance studies in emerging markets are predominantly based on ecosystems where start-ups are subjected to price limit policy, and it does not reflect the true nature of IPO performance across emerging markets.

The purpose of this study is to determine whether South African real estate investment trusts (REITs) with significant foreign real estate holdings produce better market performance metrics when compared to REITs with larger domestic holdings. The paper also provides a comprehensive overview of the market performance of South African REITs in the decade following the inception of the REIT regime in 2013.

The authors employ the capital asset pricing model (CAPM), using different estimation techniques to determine the stability of the estimated parameters over time. In addition to the CAPM framework, several basic and advanced portfolio performance metrics are computed to assess the performance of the various REIT portfolios.

The results show that REITs with significant offshore allocations produce superior market returns than their counterparts. Across most of the risk measures analysed, the foreign-biased REIT portfolios were found to be riskier. On the whole, foreign-biased REITs performed better on a risk-adjusted basis. The results were consistent across the different sample periods and the performance metrics analysed.

The results suggest that the decision to diversify internationally has implications for the pricing of REITs on stock markets. The differences in the performance metrics for the foreign- and home-biased REIT portfolios also imply an opportunity for investors to further diversify REIT portfolios by holding a mix of home-biased and foreign-biased REITs.

This paper is one of the few to consider the implications of international diversification on stock market performance rather than on more fundamental measures of REIT performance such as the net present value. This study also provides an emerging market (African) perspective to the literature.

The article aims to establish whether the degree of aversion to inflation and the responsiveness to deviations from potential output have changed over time.

This paper assesses time variation in monetary policy rules by applying a time-varying parameter generalised methods of moments (TVP-GMM) framework.

Using monthly data until December 2022 for five inflation targeting countries (the UK, Canada, Australia, New Zealand, Sweden) and five countries with alternative monetary regimes (the US, Japan, Denmark, the Euro Area, Switzerland), we find that monetary policy has become more averse to inflation and more responsive to the output gap in both sets of countries over time. In particular, there has been a clear shift in inflation targeting countries towards a more hawkish stance on inflation since the adoption of this regime and a greater response to both inflation and the output gap in most countries after the global financial crisis, which indicates a stronger reliance on monetary rules to stabilise the economy in recent years. It also appears that inflation targeting countries pay greater attention to the exchange rate pass-through channel when setting interest rates. Finally, monetary surprises do not seem to be an important determinant of the evolution over time of the Taylor rule parameters, which suggests a high degree of monetary policy transparency in the countries under examination.

It provides new evidence on changes over time in monetary policy rules.

Earlier studies used conventional time-series models to forecast the impact of the Covid-19 pandemic on stock market performance. This study aims to provide a more flexible model that offers more robust estimation features, such as incorporating additional information (prior) about the model parameters, capturing the evolving behavior of the parameters over time and being able to include several covariates using a spike and slab prior, within the context of the Covid-19 shock and its effect on stock market performance.

Empirically, this paper compares autoregressive integrated moving average (ARIMA) models and the proposed Bayesian structural time-series (BSTS) models regarding their forecasting accuracy for airline and petroleum stocks in the five countries most affected by the Coronavirus, namely, Brazil, France, India, Russia and the USA. In addition, the authors estimate the difference between the pre- and post-intervention periods of the observed series of stock prices and a simulated time-series that would have occurred without the extreme event of Covid-19, using intervention analysis under the best-performing models.

The forecasting results, based on the trend, seasonality and regression components, demonstrate that BSTS models respond faster to the diverse needs of time-series analysis in unprecedented and crisis conditions compared to ARIMA models. Therefore, the authors use intervention analysis under BSTS models to examine the impact of Covid-19 intervention on stock market performance. The authors find that the Covid-19 shock had an adverse effect on the stock markets of the selected countries. The impact was more pronounced in the Brazilian market, where the average weekly prices of airline and petroleum stocks plummeted by 76% and 29%, respectively.

To the best of the authors’ knowledge, no prior study has carried out intervention analysis under BSTS models to forecast the impact of Covid-19 intervention on stock market returns. This study attempts to fill this methodological gap in the literature.

This study addresses the empirical results of the spillover effect with export as the primary economic activity that enhances local businesses' total factor productivity (TFP). A learning mechanism is expected to be generated and used as the basis for the policy implication.

This study adopted the Cobb–Douglas function and multiple estimation approaches, including the generalized method of moments, the Olley–Pakes and the Levinsohn–Petrin estimation techniques. The findings were estimated based on the panel data of a Vietnamese local businesses survey conducted by the General Statistics Office of Vietnam (GSO) from 2010 to 2019.

The results showed that the highest TFP belongs to the businesses in the Southeast region, the Mekong Delta region, the mining industry and the foreign-invested enterprises. The lowest impacted TFP are businesses in the Northwest region and agricultural, forestry and fishery sectors. In addition, the estimated results also show that the positive spillover effect on TFP is shown through forward and backward linkage. The negative spillover effect is expressed through the backward and horizontal channels.

This study offers original empirical evidence on the learning mechanisms via which exports contribute to productivity improvement in a developing Asian economy, so making a valuable contribution to the existing academic literature in this domain. The findings of this research make a valuable contribution to the advancement of understanding on the many ways via which spillover effects manifest such as horizontal, forward, backward and supplied-backward linkage.

The study's findings indicate that it is advisable for governments to give priority to the development and improvement of forward and supply chain linkages between exporters and local suppliers. This approach is recommended in order to optimize the advantages derived from export spillovers. At the organizational level, it is imperative for enterprises to strengthen their technological and managerial skills in order to efficiently incorporate knowledge spillovers that originate from overseas partners and trade counterparts.

This study sheds new evidence on the export spillover effect on productivity in emerging economies, with Vietnam as the case study. The paper contributes to the research's originality by adopting novel methodological aspects to estimate local businesses' impact on total factor productivity.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0373

This paper aims to examine the extreme return spillover between crude oil and ESG stocks for 10 developed and 11 emerging economies from 4 January 2016 to 3 October 2024.

The paper extends the generalized VAR methodology proposed by Diebold and Yilmaz (2012) (DY12) to quantify the dynamics of spillovers across ESG indices and crude oil. The authors use the quantile connectedness approach by Ando et al. (2022) to explore the quantile connectedness with various quantiles (q), such as bearish, normal and bullish market conditions.

The critical findings of the paper are as follows: firstly, the study reports extreme spillover at the tails, especially during COVID-19, resulting in asymmetry in tail dependency within the network. Secondly, asymmetry in the tail dependence is maximum during COVID-19. Thirdly, crude oil acts as a major recipient, but the degree of receiving return shocks from ESG market innovations intensifies during extreme market conditions. Lastly, the network analysis depicts the complex market dynamics during the bearish phase mainly for the emerging markets.

Unlike the previous studies which uses the vector autoregression (VAR) models, cointegration methods, wavelet analysis, cross-correlation techniques, copula approaches and GARCH models which fails to capture the dynamics of return spillovers under extreme market conditions and derived from forecast-error variance decomposition to account for tail-specific dynamics, this study offers a more comprehensive understanding of tail dependence and asymmetry in spillover effects using the median-based quantile VAR (QVAR) approach between crude oil and ESG indices, and tested across 10 developed and 11 emerging markets.