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1 – 10 of over 1000L.L. Leachman, Christie H. Paksoy and J.B. Wilkinson
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…
Abstract
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.
Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modeling. Identification is often achieved by imposing restrictions on the…
Abstract
Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modeling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focuses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach.
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Amanjot Singh and Manjit Singh
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…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Refet S. Gürkaynak, Burçin Kısacıkoğlu and Barbara Rossi
Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random…
Abstract
Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.
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Florin Aliu, Alban Asllani and Simona Hašková
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…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
Practitioners and traders can employ the gold price sentiment index to make a profit in the stock market in Thailand.
Originality/value
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.
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Zhonglu Liu, Haibo Sun and Songlin Tang
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Muhammad Najib Razali, Ain Farhana Jamaluddin, Rohaya Abdul Jalil and Thi Kim Nguyen
This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.
Abstract
Purpose
This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.
Design/methodology/approach
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.
Findings
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.
Originality/value
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
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Ghanshyam Pandey, Surbhi Bansal and Shruti Mohapatra
The purpose of this paper is to examine the market integration and direction of causality of wholesale and retail prices for the chickpea legume in major chickpea markets in India.
Abstract
Purpose
The purpose of this paper is to examine the market integration and direction of causality of wholesale and retail prices for the chickpea legume in major chickpea markets in India.
Design/methodology/approach
In this paper, the authors employ the Johansen co-integration test, Granger causality test, vector autoregression (VAR), and vector error correction model (VECM) to examine the integration of markets. The authors use monthly wholesale and retail price data of the chickpea crop from select markets in India spanning January 2003–December 2020.
Findings
The results of this study strongly confirm the co-integration and interdependency of the selected chickpea markets in India. However, the speed of adjustment of prices in the wholesale market is weakest in Bikaner, followed by Daryapur and Narsinghpur; it is relatively moderate in Gulbarga. In contrast, the speed of adjustment is negative for Bhopal and Delhi, weak for Nasik, and moderate for retail market prices in Bangalore. The results of the causality test show that the Narsinghpur, Daryapur, and Gulbarga markets are the most influential, with bidirectional relations in the case of wholesale market prices. Meanwhile, the Bangalore market is the most connected and effective retail market among the selected retail markets. It has bidirectional price transmission with two other markets, i.e. Bhopal and Nasik.
Research limitations/implications
This paper calls for forthcoming studies to investigate the impact of external and internal factors, such as market infrastructure; government policy regarding self-reliant production; product physical characteristics; and rate of utilization indicating market integration. They should also focus on strengthening information technology for the regular flow of market information to help farmers increase their incomes.
Originality/value
Very few studies have explored market efficiency and direction of causality using both linear and nonlinear techniques for wholesale and retail prices of chickpea in India.
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Zekeriya Yildirim and Mehmet Ivrendi
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
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