Search results

This paper aims to examine the destabilization effect in the case of India’s agricultural commodity market for the sample period of 01 January 2009 to 31 May 2013.

The daily data of eight agricultural commodities traded on the National Commodity & Derivatives Exchange, viz., barley, castor seed, chana (chickpea), chilli, potato, pepper, refined soya and soybean, have been used in this study. At the first stage of the empirical analysis, the study estimates the time-varying spot market volatility by using the exponential generalized autoregressive conditional heteroscedasticity model and applies three different high and band-pass filters, viz., the two-sided linear band-pass filter by Hodrick and Prescott (1997), the fixed-length symmetric band-pass filter by Baxter and King (1999) and the asymmetric band-pass filter by Christiano and Fitzgerald (2003), to calculate the unexpected liquidity of sample commodities. At the second stage of the empirical analysis, the study applies linear Granger causality and recently developed non-linear causality given by Diks and Panchenko (2006) to examine the cause and effect between time-varying volatility of spot market and futures market liquidity of sample commodities.

The linear and non-linear causality results suggest the destabilizing effect of commodity futures on the underlying spot market for chana, chilli and pepper. The empirical findings are in contrast with the recommendations of Abhijit Sen’s committee and provide important direction for further policy research.

The study has a limitation in that it is based on the daily data. The use of intra-day data would have been more suitable for such type of analysis.

The study has strong policy implications from a financial policy perspective, as there is already disagreement among researchers and policy makers with regard to the functioning of commodity derivatives markets in India. There have been many occasions when commodity market regulators have to undertake decisions of suspension of trading of many commodities. The study also provides new directions of policy research with regards to the restructuring of the commodity derivatives market in India.

The findings of this study may further help the regulators and policy makers to undertake decisions about how to provide an alternative platform for farmers to sell their agricultural produce more efficiently. This will certainly have some impact on the socioeconomic set-up of the country, as India is primarily an agriculture-dominated country.

So far not many studies have investigated the destabilization hypothesis in the case of emerging markets. This study is a novel attempt to fill the gap. In the case of emerging markets and especially in the case of India’s commodity derivatives market, this is the first study that examines the destabilization hypothesis in the case of India by applying new methods of high and band-pass filters and non-linear causality.

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 examine the relationship between stock prices and exchange rates in 12 OECD countries.

The authors examine the nexus of stock prices and exchange rates for 12 OECD countries by using the vector error correction model, the bounds testing methodology and linear and non‐linear Granger causality methods.

The empirical results substantiate that a long‐run level equilibrium relationship among the exchange rates and stock prices exists in only seven out of twelve countries. The results of the linear causality tests indicate that significant short‐run and long‐run causal relationships exist between the two financial markets. The results of the tests for non‐linear Granger causality suggest that unidirectional and bidirectional non‐linear causal relationships exist between stock prices and exchange rates among these OECD countries.

The findings from this paper suggest the causal relationships between stock prices and exchange rates are not only linear, but also non‐linear.

The purpose of this paper is to show how non-linear causal modelling knowledge, already accumulated by other disciplines, is central to unravelling wicked problem scoping and definition in social marketing.

The paper is an illustrative case study approach, highlighting three real-world exemplars of causal modelling for wicked problem definition.

The findings show how the traditional linear research methods of social marketing are not sensitive enough to the dynamics and complexities of wicked problems. A shift to non-linear causal modelling techniques and methods, using interaction as the unit of analysis, provides insight and understanding into the chains of causal dependencies underlying social marketing problems.

This research extends the application of systems thinking in social marketing through the illustration of three non-linear causal modelling techniques, namely, collective intelligence, fuzzy cognitive mapping and system dynamics modelling. Each technique has the capacity to visualise structural and behavioural properties of complex systems and identify the central interactions driving behaviour.

Non-linear causal modelling methods provide a robust platform for practical manifestations of collaborative-based strategic projects in social marketing, when used with participatory research, suitable for micro, meso, macro or systems wide interventions.

The paper identifies non-linear causality as central to wicked problem scoping identification, documentation and analysis in social marketing. This paper advances multi-causal knowledge in the social marketing paradigm by using fuzzy, collective and interpretative methods as a bridge between linear and non-linear causality in wicked problem research.

The purpose of this paper is to analyse the adaptive market efficiency in the price–volume (P–V) relationship of the stocks listed in the Mexican Stock Exchange. The period under study goes from 1982 to 2015. In order to detect causality and, thus, determine adaptive efficiency in the market, one linear and two non-linear tests are applied. There are few papers in the literature that study the P–V relationship in Latin American markets; as such, this paper may be of interest and importance to financial academics and practitioners alike.

The Diks and Panchenko (DP) non-parametric Granger causality and the Brooks and Hinich (BH) cross-bicorrelation tests are applied.

Derived from the DP test, the findings show that there exists bi-directional non-linear Granger causality in 25.71 per cent of the firms studied, compared to 8 per cent when applying the linear Granger causality test. Therefore, there is evidence of weak-form efficiency in the market. From the BH test, evidence is shown of the adaptive market efficiency, since 71.42 per cent of firms exhibited some form of non-linear dependence in certain periods of time. With these results, the information process should be better studied for a greater comprehension of regulatory policies in the market and better decision-making tools for the investors.

This paper complements studies on the P–V relationship and efficiency in a Latin American market.

Este documento analiza la eficiencia adaptativa del mercado para la relación precio-volumen de las empresas que cotizan en la Bolsa Mexicana de Valores. El periodo bajo estudio es de 1982 a 2015. Para detectar causalidad y determinar la eficiencia adaptativa del mercado, se aplicó una prueba lineal y dos no-lineales. Existen pocos documentos en la literatura que estudien la relación precio-volumen en mercados latinoamericanos. Como tal, este documento puede ser de interés e importancia tanto para académicos como para profesionales de las finanzas.

Se aplicó la prueba de causalidad no-paramétrica de Diks y Panchenko y la prueba de bicorrelación cruzada de Brooks y Hinich.

Derivado de la prueba DP, los hallazgos muestran que existe causalidad no-lineal bidireccional en 25.71% de las empresas bajo estudio, comparado a un 8% cuando se aplica la prueba de causalidad lineal de Granger. Por lo tanto, existe evidencia de eficiencia en forma débil del mercado. De la pruba BH, se muestra evidencia de eficiencia adaptativa del mercado, dado que el 71.42% de las empresas exhibieron alguna forma de dependencia no-lineal en ciertos periodos de tiempo. Con estos resultados, el proceso de información debe ser mejor estudiado para una mayor comprensión de las políticas regulatorias del mercado y mejores herramientas para la toma de decisiones por los inversionistas.

Este documento complementa los estudios sobre la relación precio-volumen y la eficiencia en un mercado latinoamericano.

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.

Circular causality is one of several unorthodox assumptions underlying cybernetics. This paper identifies “blind spots” which obscure the soundness of this assumption, rendering cybernetics liable to rejection. The purpose of this paper is to aid students of cybernetics in appreciating circular causality.

The presented argument is based on textual and diagrammatic explication of several more or less obvious causal scenarios. Some of these modes are shown to obscure circular causality from observation.

Previously discussed “blind spots” obscuring circular causality are referenced. The notion of probabilistic causality is developed from the notion of collateral effects, which is introduced by extension of the notion of contributory causality. The possible “lossiness” of probabilistic causation is shown to constitute another “blind spot” obscuring circular causality, for example in design.

The presented argument aims to promote acceptance of circular causality. Assuming a radical-constructivist perspective, it discusses the construction of mental models of causal relationships.

Ignorance of circular causality has previously been attributed to preferences for description in terms of energy, and in terms of timeless logic. Additionally, this paper proposes the obscuring effect of probabilistic causality, and the possible co-occurrence of multiple “blind spots.”

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.