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With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.

The purpose of this paper is to study the market efficiency, unbiasedness and the direction of causality among four agricultural commodity futures contracts for a forecasting horizon of 28 days, 56 days and 84 days which are traded at National Commodity and Derivatives Exchange Ltd.

To analyse the efficiency of futures market in Indian scenario, we focus on maize, chickpea, soybean and wheat which are among the most important agricultural commodities traded in India. In the first step, Augmented Dickey-Fuller test and nonparametric Phillips-Perron approaches have been used to examine the stationarity of all futures and spot price series. After testing the presence of cointegration in futures and spot series using Johansen’s Cointegration approach, the joint restrictions of β ₀=0, β ₁=1 and β ₁=1 on the cointegrating vectors were imposed to test whether the futures price is an unbiased predictor of spot at contract maturity. In the next step, linear Toda and Yamamoto (1995) and the nonparametric Diks and Panchenko (2006) causality tests were applied to examine the direction of causality. Finally, nonlinear test were applied on the vector error correction model (VECM) residuals to investigate whether any remaining causality is strictly nonlinear in nature.

The results of cointegration tests between futures and spot prices of the selected agricultural commodities indicated a long term relationship do exist in three out of four futures contracts. However, the Wald tests results on the cointegrating vectors indicate markets as inefficient and biased. Further, analysis of short-term relationship using alternate tests of causality do not give consistent results for same commodity series indicating that results may vary due to alternate measures and specifications. Finally, if we consider the results of Diks-Panchenko test on the filtered VECM-residuals, results provide evidence that if cointegration is taken into account; neither spot nor future leads or lags the other consistently.

The results are based on the sample of four agricultural futures commodity contracts. The study can be extended to a larger sample of contracts and relative efficiency of each contract can be explored.

There are very few studies that have explored the efficiency, unbiasedness and direction of causality using both linear and nonlinear techniques for Indian agriculture commodity futures market for different forecasting horizons.

The purpose of this paper is to explore the dynamic influence of WTI crude oil returns on the stock returns of China’s traditional energy sectors, including oil and gas exploitation, coal mining and processing, petroleum processing and coking, electricity, heat production and supply and mining services.

Hong’s information spill-over test and the DP Granger causality test are applied to investigate the relationship between the two markets. Moreover, a rolling window is introduced into the above two tests to capture time-varying characteristics of the influence of WTI crude oil returns.

The empirical results indicate that, first, there exists significant bidirectional linear causality between WTI crude oil returns and China’s traditional energy sectoral stock returns, but the nonlinear causality appears weaker. Second, the influence of WTI crude oil returns on traditional energy sectoral stock returns has time-varying characteristics and industry heterogeneity both in the linear and nonlinear cases. Finally, the decline of WTI crude oil prices may strengthen its linear influence on the stock returns of traditional energy sectors, while the excessive rise of market values in traditional energy sectors may weaken the linear and nonlinear influence of WTI on them.

The general nexus between international crude oil market and China’s traditional energy stock market is explored both in the linear and nonlinear perspectives. In particular, the dynamic linear and nonlinear influence of WTI crude oil returns on China’s traditional energy sectoral stock returns and its industry heterogeneity are analysed in detail.

This chapter estimates causality properties between real money demand and a number of determinants, that is, real output, the lending rate and the real exchange rate, across 10 Asian economies through linear and nonlinear causality methodologies spanning the period 1990–2012. The results document both bidirectional and unidirectional causality between monetary aggregates (M1 and M2) and their determinants for different country groups. The empirical findings exemplify the role of the demand for money as a policy tool and can provide useful policy recommendations to the Asian monetary authorities in their vision of forming a future monetary union.

The purpose of this paper is to examine the linear and nonlinear relations between returns volatility and trading volume for the Indian currency futures market.

To examine the contemporaneous relation between returns volatility and volume, the author uses the generalized method of moment estimator. For the linear causal relation, the author makes use of Granger (1969) bivariate vector autoregression model. The author tests for nonlinear Granger causality between returns volatility and trading volume based on a modified version of the Baek and Brock (1992) nonparametric technique developed by Hiemstra and Jones (1994).

The results indicate a negative contemporaneous relation between returns volatility and trading volume; therefore, the mixture of distribution hypothesis is not supported. The results of both linear and nonlinear Granger causality between futures returns volatility and trading volume indicate a significant bidirectional relation between the two variables lending support to the sequential arrival of information hypothesis. The results are robust to divergence of opinions as proxied by open interest.

The findings of this paper are important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant causal relation between returns volatility and trading volume implies that trading volume helps predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Furthermore, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market.

To the best of the author’s knowledge, there is no study that investigates the forecast ability of trading volume to futures returns volatility in an emerging currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price–volume relation in the Indian currency futures market.

This paper aims to empirically examine the effect of Coronavirus disease 2019 (COVID-19) pandemic on cryptocurrency market returns with particular attention to top five cryptocurrencies and COVID-19 confirmed and death cases.

The study applies the linear Toda and Yamamoto and nonlinear Diks and Panchenko Granger causality test to know the causal relationship of cryptocurrencies with COVID-19 pandemic. The study also uses the Narayan and Popp endogenous two structural break tests to capture the break period of the sample.

The findings of the study confirm the existence of unidirectional causal relation from COVID-19 confirmed and death cases to cryptocurrency price returns. While examining the break periods, the post-break period result indicates the presence of unidirectional linear causality from COVID-19 confirmed cases to Bitcoin and Ethereum price returns. This shows that prior knowledge of COVID-19 pandemic growth helps to predict the return of cryptocurrencies.

The study suggests the investors or crypto lovers to observe the growth of COVID-19 situations during their investment in cryptocurrency markets.

The nature of price volume relationship in asset market has been an interesting subject in financial research as it reveals a very important aspect which has implications for market efficiency. The purpose of this paper is to examine price volume relationships in Indian commodity futures market.

There are two competing models in price volume relationship. Mixture of distribution hypothesis, suggesting a positive contemporaneous relationship and sequential information arrival hypothesis (SIH), suggesting a positive intertemporal causal relationship. Both are tested using correlation coefficient and Granger causality test with vector auto regressive methodology.

Though there exists contemporaneous correlation between volume and price change in some of the cases, but in general on the basis of the presence of Granger causality it follows that SIH is supported.

As only three commodities futures have been studied in this paper, this study can be extended to include more number of commodities currently being traded so as to make it more exhaustive.

The research has been done with the data of MCX Gold, MCX Silver and MCX Crude. The results of causality suggest that inefficiency level is maximum in Silver which may be attributed to informational asymmetry.

The Indian commodity futures market is of very recent origin. Hence, very little research work has been undertaken in this space. The paper presents an assessment of the existence of informational asymmetry among the three commodity futures under the study.

This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

Since financial sector plays a critical economic role in Hong Kong, the current research aims to comprehensively analyze the association between financial development and economic growth in the country to draw correct conclusions about the impact that financial sector's development has on the growth of the economy. This requires both using of more comprehensive data that includes all or nearly all elements of the country's financial sector and utilizing advanced econometrics techniques to provide more reliable evidence based on the findings. In the study, both issues have been addressed more academically to aid the relevant authorities better.

This study empirically examines the financial development-economic growth nexus in Hong Kong employing data covering 1980–2019. The quantile-on-quantile (QQ) approach of Sim and Zhou (2015) is utilized to investigate certain subtle aspects of the association linking financial development and economic growth. In addition, the authors benefit from applying the nonlinear Granger causality test of Diks and Panchenko (2006) to assess the variables' nexus in a nonlinear manner.

In contrast to the evidence of a unidirectional linkage documented in many related studies, the empirical findings suggest that a bi-directional relationship exists between financial development and economic growth for Hong Kong. This is a helpful input for the relevant policymakers and implies that they can set appropriate policies and regulations to balance financial development and economic growth in this country.

The originality of this study can be divided into two parts. Methodologically, unlike past studies that utilized mostly linear and parametric methods, the paper contributes to the literature by applying the more robust nonparametric and nonlinear methodologies. Theoretically, most researchers have used various financial development indicators, which led to very different conclusions. Therefore, this study attempts to resolve this deficiency in the literature by using a more comprehensive index for financial development.

The primary objective of the paper is to examine the asymmetric Cointegration and asymmetric causality between financial development and poverty alleviation on annual data in Indian context over the period from 1980 to 2019.

First nonlinearity test by Brooks et al. (1999) is applied to ascertain the nonlinear behavior of the variables used. Once the nonlinear behavior of variables is confirmed, asymmetric and nonlinear unit root tests by Kapetanios and Shin (2008) are applied to check for the order of integration of selected variables. Next, nonlinear autoregressive distributed lag model (NARDL) is employed to analyze the asymmetric Cointegration. Finally, Hatemi-j- asymmetric causality tests is applied to work out the direction of asymmetric causality.

The empirical findings document the existence of asymmetries in the short-run as well as long-run between poverty and financial development. The asymmetry reveals that negative financial development shocks leave a more profound impact on poverty alleviation than their positive equivalents. The findings of Wald's test also confirm the presence of asymmetric Cointegration. The asymmetric cumulative dynamic multipliers used to examine the behavior of asymmetries and adjustments with respect to time lend credence to the results calculated using NARDL estimator. This result exhibits the robustness of the model. Furthermore, the result emanating from recently introduced asymmetric causality test reveals a unidirectional asymmetric causality between negative shocks in financial development and poverty. The findings of the present study necessitate the need for investigating asymmetric and nonlinear effects in finance–poverty nexus, which existent literature has completely neglected, in order to have relevant policy conclusions.

The study used “Per capita consumption expenditure” as a measure for poverty due to lack of continuous time series data on headcount ratio. In future, researchers can extend this study by incorporating headcount ratio as a measure of poverty in their respective works. There is further scope of research on this issue by finding out the impact of formal and informal sources of credit on poverty separately. A panel data study for developing countries over a period of time could further confirm/negate the findings of the present study.

To the best of the authors’ knowledge none of the studies in Indian context has scrutinized asymmetric and nonlinear impact of financial development on poverty. To dredge up asymmetric structures at work, the authors have used the highly celebrated NARDL estimator. To enrich the existent body of knowledge along the lines of asymmetric (nonlinear) linkages, the authors have also used recently introduced asymmetric causality test by Hatemi-j-(2012) to find out the direction asymmetric causality.