Search results

This research applies vector autoregression to estimate a system composed of market share and relative advertising expenditures of the seven major competitors in the U. S. replacement passenger tire market between 1972 and 1983. The results of the study suggest that a company's market share in this market cannot be predicted from its relative advertising expenditures.

The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a).

Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, variance decomposition analyses and bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model under constant conditional correlation framework, in general. Markov regime switching and efficient causality tests proposed by Hill (2007) are also used.

Overall, there are both short-run and long-run dynamic interactions observed between the US and Indian financial stress indices. For rest of the markets, only short-run interactions are found to be in existence. The time-varying co-movement coefficients report financial contagion impact of the US financial crisis on Russian and Indian financial systems only. Contrary to this, Brazilian and Chinese financial systems are largely exhibiting interdependence with the US financial system. Efficient causality tests report indirect impact of the Russian financial system on Brazilian via auxiliary Indian financial system.

The present study is the first of its kind capturing linkages among the US-BRIC financial stress indices by using diverse econometric models. The results support different market participants and policymakers in understanding effectiveness and implementation of economic policies while considering their cross-market interactions as well.

Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX).

The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed.

The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not.

BTC’s diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold.

Considering the unique data of the gold investor sentiment index in Thailand, the purpose of this paper is to investigate the bivariate dynamic relationship between the gold investor sentiment index and stock market return, as well as that between the gold investor sentiment index and stock market volatility, using the panel vector autoregression (PVAR) methodology. The author presents and discusses the findings both for the full sample and at the industry level. The results support prior literature that stocks in different industries do not react similarly to investor sentiment.

The PVAR methodology with the GMM estimation is found to be superior to other static panel methodologies due to considering both unobservable time-invariant and time-variant factors, as well as being suitable for relatively short time periods. The panel data approach improves the statistical power of the tests and ensures more reliable results.

In general, a negative and unidirectional association from gold investor sentiment to stock returns is observed. However, the gold sentiment-stock realized volatility relationship is negative and bidirectional, and there exists a greater impact of a stock’s realized volatility on gold investor sentiment. Importantly, evidence at the industry level is stronger than that at the aggregate level in both return and volatility cases, confirming the role of gold investor sentiment in the Thai stock market. The capital flow effect and the contagion effect explain the gold sentiment-stock return relationship and the gold sentiment-stock volatility relationship, respectively.

The gold price sentiment index can be used as a factor for stock return predictability and stock realized volatility predictability in the Thai equity market.

Practitioners and traders can employ the gold price sentiment index to make a profit in the stock market in Thailand.

This is the first paper to use panel data to investigate the relationships between the gold investor sentiment and stock returns and between the gold investor sentiment and stocks’ realized volatility, respectively.

Climate change not only causes serious economic losses but also influences financial stability. The related research is still at the initial stage. This paper aims to examine and explore the impact of climate change on financial stability in China.

This paper first uses vector autoregression model to study the impact of climate change to financial stability and applies NARDL model to assess the nonlinear asymmetric effect of climate change on China’s financial stability using monthly data from 2002 to 2018.

The results show that both positive and negative climate shocks do harm to financial stability. In the short term, the effect of positive climate shocks on financial stability is greater than the negative climate shocks in the current period, but less in the lag period. In the long term, negative climate shocks bring larger adjustments to financial stability relative to positive climate shocks. Moreover, compared with the short-term effect, climate change is more destructive to financial stability in the long run.

The paper provides a quantitative reference for assessing the nexus between climate change and financial stability from a nonlinear and asymmetric perspective, which is beneficial for understanding climate-related financial risks.

This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.

This study uses several empirical analyses such as vector autoregression (VAR), vector error correction model (VECM), ARMA model and Granger causality to analyse predictive maintenance by using big data analytics concept.

The results indicate that there are strong correlations among these variables, which indicate reciprocal predictive maintenance of maintenance management job function. The findings also showed that there are significant needs of application of big data analytics for maintenance management in Putrajaya, Malaysia, to ensure the efficient maintenance of government buildings.

The conducted case study has demonstrated the empirical perspective which streamlines with the big data analytics' concept in maintenance, especially for analytics' support with appropriate empirical methodology

Recent turbulence in global financial markets implies that emerging economies are likely to soon enter a new era with greater pressure for currency depreciation and capital outflows. This will likely bring challenges, including macroeconomic instability and inflationary pressures due to potential rapid depreciation. In this context, certain key questions about emerging economies have become focal points of discussion in political and academic spheres: what are the effects of exchange rate depreciation on economic activity? Does exchange rate depreciation create inflationary pressure? Finding answers to these questions is critical for policymakers and financial market participants. As such, the purpose of this paper is to shed light on these questions and thus provides guidance on mitigating the negative impacts of shocks in four fast-growing emerging economies.

The authors use a vector autoregression model with sign restrictions to examine the dynamic effects of exchange rate movements on fundamental macroeconomic indicators for four fast-growing countries, namely, Brazil, Turkey, Russia, and South Africa. Following Berument et al. (2012a), Ncube and Ndou (2013), Bjørnland and Halvorsen (2013), and An et al. (2014), the authors adopt the sign restriction methodology to identify exchange rate shocks alongside other macroeconomic shocks (monetary policy and productivity shocks) leading to exchange rate fluctuations.

The results show that exchange rate depreciation typically generates a deep recession and high inflation while improving the trade balance in the four emerging economies. This indicates that depreciation has strong “stagflationary” effects, which are transmitted to the macroeconomy primarily via supply-side channels, especially through the cost of import. Furthermore, the authors find that monetary policy reacts immediately to a domestic currency depreciation in all four emerging countries.

The results imply that these countries’ monetary policies are not and cannot be neutral to exchange rate shocks. However, in these import-dependent countries, monetary tightening (i.e. rate hikes in response to an exchange rate shock) plays a limited role in mitigating the negative effects of depreciation on inflation and economic activity due to the presence of a dominant supply-side channel. In this framework, policymakers should pay greater attention to structural reforms that aim to reduce import dependency. These reforms may increase the effectiveness of domestic monetary policy in mitigating the negative effects of external shocks.

This paper provides a useful perspective for policymakers designing economic interventions to mitigate the adverse effects of exchange rate depreciation and to those who borrow or lend in domestic or international financial markets.

Deposit insurance is a key element in modern banking, as it guarantees the financial safety of deposits at depository financial institutions. It is necessary to have at least a dual fair premium rate system based on creditworthiness of financial institutions, as considering singular premium system for all banks will have moral hazard. This paper aims to develop theoretical and empirical model for calculating dual fair premium rates.

The definition of a fair premium rate in this paper is a rate that covers the operational expenditures of the deposit insuring organization, provides it with sufficient funds to enable it to pay a certain percentage share of deposit amounts to depositors in case of bank default and provides it with sufficient funds as precautionary reserves. To identify and classify healthier and more stable banks, the authors use credit rating methods that use two major dimensional reduction techniques. For forecasting nonperforming loans (NPLs), the authors develop a model that can capture both macro shocks and idiosyncratic shocks to financial institutions in a vector error correction model.

The response of NPLs/loans to macro shocks and idiosyncratic innovations shows that using a model with macro variables only is insufficient, as it is possible that under favorable economic conditions, some banks show negative performance due to bank level reasons such as mismanagement or vice versa. The final results show that deposit insurance premium rate needs to be vary based on banks’ creditworthiness.

The results provide interesting insight for financial authorities to set fair deposit insurance premium rate. A high premium rate reduces the capital adequacy of individual financial institutions, which endangers the stability of the financial system; a low premium rate will reduce the security of the financial system.

This study aims to uncover the main predictors of financial distress in the Gulf Cooperation Council (GCC) countries using a wide range of global factors and asset classes.

This study uses novel approaches that take into account extreme events as well as the nonlinear behavior of time series over various time intervals (i.e. short, medium and long term) and during boom and bust episodes. This study primarily uses the conditional value at risk (CoVaR), the quantile multivariate causality test and the partial wavelet coherence method. The data collection period ranges from March 2014 to September 2022.

US T-bills and gold are the primary factors that can increase financial stability in the GCC region, according to VaRs and CoVaRs. More proof of the predictive value of the oil, gold and wheat markets, as well as geopolitical tensions, uncertainty over US policy and volatility in the oil and US equities markets, is provided by the multivariate causality test. When low extreme quantiles or cross extreme quantiles are taken into account, these results are substantial and sturdy. Lastly, after adjusting for the effect of crude oil prices, this study’s wavelet coherence results indicate diminished long-run connections between the GCC stock market and the chosen global determinants.

Despite the implications of the author’s research for decision makers, there are some limitations mainly related to the selection of Morgan Stanley Capital International (MSCI) GCC ex-Saudi Arabia. Considering the economic importance of the Kingdom of Saudi Arabia (KSA) in the region, the author believes that it would be better to include this country in the data to obtain more robust results. In addition, there is evidence in the literature of the existence of heterogeneous responses to global shocks; some markets are more vulnerable than others. This is another limitation of this study, as this study considers the GCC as a bloc rather than each country individually. These limitations could open up further research opportunities.

These findings are important for investors seeking to manage their portfolios under extreme market conditions. They are also important for government policies aimed at mitigating the impact of external shocks.

The purpose of this paper is to analyze the nature of returns and volatility spillovers between exchange rates and stock price in the IBSA nations (India, Brazil, South Africa).

The study uses VAR framework and the recently proposed Spillover measure of Diebold and Yilmaz to examine the returns and volatility spillover between exchange rates and stock prices of IBSA nations. In addition, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.

The results of multivariate GARCH model suggests the integration between stock and foreign exchange markets and indicates the existence of bi‐directional volatility spillover between stock and foreign exchange markets in the IBSA countries. Spillover results using the Diebold Yilmaz model suggest the bi‐directional contribution between stock and foreign exchange market, in terms of both returns and volatility spillovers. Overall, results confirm the presence of returns and volatility spillovers within the IBSA nations and, in particular, the stock markets play a relatively more important role than foreign exchange markets in the first and second moment interactions and spillovers.

The market participants may consider the relationship between the exchange rate and stock index to predict the future movement of each other effectively. Multinational companies interested in exchange rate forecasting may consider the stock market as an important attribute. There is an interesting implication for portfolio managers too because of the spillover stock and foreign exchange markets. This knowledge would help to create a fund which performs well. Moreover, the paper can help regulators and policy makers in IBSA nations to understand the structure of the market in a better way and then design their policies.

The study contributes to the literature by extending the existing studies on the spillover between stock price and exchange rate by investigating the issue for three emerging economies, India, Brazil and South Africa. Unlike most studies in the literature which focus on multivariate GARCH model, this is the first study which explores the issue of returns and volatility spillover between the stock prices and the exchange rates using spillover measure of Diebold and Yilmaz and much longer and recent daily data. Moreover, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.