Dataset Information

Macroeconomic factors and frequency domain causality between Gold and Silver returns in India

ABSTRACT: This paper examines the relationship between gold and silver returns in India, using monthly data for the period May 1991 to June 2018. To this end, we employ the recently developed frequency domain rolling-window analysis (which is able to show that transitory high frequency shocks are not equal to permanent low frequency shocks over time), as well as the conditional, partial conditional, difference conditional approaches, in addition to the Toda Yamamoto and frequency domain Granger Causalities methods. Further, the relationship is examined in conditional and unconditional frameworks. To condition the relationship, three macroeconomic variables, namely interest rate, BSE stock index and inflation rate are used as the control variables. The results uncover some interesting predictability patterns that vary along the spectrum. Specifically, by applying the rolling-window analysis, we find mixed results of the causality between the gold and silver markets based on the frequencies of different lengths. Our results provide policy inputs, assist investors and hedgers who wish to invest in these markets by constructing strategies and diversify their portfolios based on different frequencies. Highlights • Impact of macroeconomic factors on frequency domain causality between Gold and Silver returns is examined.• The results uncover some interesting predictability patterns that vary along the spectrum.• We find a significant bi-directional causality between the gold and silver markets in both the high and low frequencies but of different lengths.

SUBMITTER: Pradhan A

PROVIDER: S-EPMC7326452 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesMendelian randomization (MR) was used to estimate the causal relationship between body mass index (BMI), ever smoked, heart failure, alcohol intake frequency, inflammatory bowel disease (IBD), and pulmonary embolism (PE). This study aimed to investigate whether there is a causal relationship between BMI, the presence of smoking, heart failure, frequency of alcohol intake, IBD, and PE.MethodsPooled data on PE from a published GWAS meta-analysis involving approximately 461,164 participants of European ancestry were selected. A publicly available pooled dataset of BMI (461,460), ever smokers (461,066), heart failure (977,323), IBD (75,000), and frequency of alcohol intake (462,346) was used from another independent GWAS. MR was performed using established analysis methods, including Wald ratios, inverse variance weighted (IVW), weighted median (WM), and MR-Egger. Also, the final expansion was validated with multivariate MR.ResultsIn the IVW model, genetically elevated BMI was causally associated with PE [OR = 1.002, 95% CI (1.001, 1004), P = 0.039]. Cochran's Q test was used to detect heterogeneity in the MR-Egger analysis (P = 0.576). Therefore, the effect of gene-level heterogeneity was not considered. In the MR analysis of other risk factors, we observed genes for ever smoking [IVW OR = 1.004, 95% CI (0.997, 1.012)], heart failure [IVW OR = 0.999, 95% CI (0.996, 1.001)], IBD [IVW OR = 1.000, 95% CI (0.999, 1.001)], and frequency of alcohol intake [IVW OR = 1.002, 95% CI (1.000, 1.004)] were not causally associated with PE. Analysis using multivariate MR expansion showed no causal effect of BMI on PE considering the effect of height as well as weight (P = 0.926).ConclusionIn European populations, a causal relationship exists between BMI and PE: increased BMI leads to PE. In contrast, ever smoking, heart failure, frequency of alcohol intake, and IBD are not directly associated with PE. There was no causal effect of BMI with PE in multivariate Mendelian randomized analysis.

Project description:BackgroundWhilst epidemiological studies have provided evidence of associations between certain risk factors and glioma onset, inferring causality has proven challenging. Using Mendelian randomization (MR), we assessed whether associations of 36 reported glioma risk factors showed evidence of a causal relationship.MethodsWe performed a systematic search of MEDLINE from inception to October 2018 to identify candidate risk factors and conducted a meta-analysis of two glioma genome-wide association studies (5739 cases and 5501 controls) to form our exposure and outcome datasets. MR analyses were performed using genetic variants to proxy for candidate risk factors. We investigated whether risk factors differed by subtype diagnosis (either glioblastoma (n =?3112) or non-glioblastoma (n =?2411)). MR estimates for each risk factor were determined using multiplicative random effects inverse-variance weighting (IVW). Sensitivity analyses investigated potential pleiotropy using MR-Egger regression, the weighted median estimator, and the mode-based estimator. To increase power, trait-specific polygenic risk scores were used to test the association of a genetically predicated increase in each risk factor with glioma onset.ResultsOur systematic search identified 36 risk factors that could be proxied using genetic variants. Using MR, we found evidence that four genetically predicted traits increased risk of glioma, glioblastoma or non-glioblastoma: longer leukocyte telomere length, liability to allergic disease, increased alcohol consumption and liability to childhood extreme obesity (>?3 standard deviations from the mean). Two traits decreased risk of non-glioblastoma cancers: increased low-density lipoprotein cholesterol (LDLc) and triglyceride levels. Our findings were similar across sensitivity analyses that made allowance for pleiotropy (genetic confounding).ConclusionsOur comprehensive investigation provides evidence of a causal link between both genetically predicted leukocyte telomere length, allergic disease, alcohol consumption, childhood extreme obesity, and LDLc and triglyceride levels, and glioma. The findings from our study warrant further research to uncover mechanisms that implicate these traits in glioma onset.

Dataset Information

Macroeconomic factors and frequency domain causality between Gold and Silver returns in India

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets