Market Integration

Joint multifractality in the cross-correlations between grains & oilseeds indices and external uncertainties ArXiv ID: 2410.02798 “View on arXiv” Authors: Unknown Abstract This study investigates the relationships between agricultural spot markets and external uncertainties via the multifractal detrending moving-average cross-correlation analysis (MF-X-DMA). The dataset contains the Grains & Oilseeds Index (GOI) and its five sub-indices of wheat, maize, soyabeans, rice, and barley. Moreover, we use three uncertainty proxies, namely, economic policy uncertainty (EPU), geopolitical risk (GPR), and volatility Index (VIX). We observe the presence of multifractal cross-correlations between agricultural markets and uncertainties. Further, statistical tests show that maize has intrinsic joint multifractality with all the uncertainty proxies, exhibiting a high degree of sensitivity. Additionally, intrinsic multifractality among GOI-GPR, wheat-GPR and soyabeans-VIX is illustrated. However, other series have apparent multifractal cross-correlations with high possibilities. Moreover, our analysis suggests that among the three kinds of external uncertainties, geopolitical risk has a relatively stronger association with grain prices. ...

What Drives Crypto Asset Prices? ArXiv ID: ssrn-4910537 “View on arXiv” Authors: Unknown Abstract We investigate the factors influencing cryptocurrency returns using a structural vector auto-regressive model. The model uses asset price co-movements to identi Keywords: Cryptocurrency, Structural VAR, Digital Assets, Market Integration, Return Determinants, Cryptocurrency Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 8.5/10 Quadrant: Holy Grail Why: The paper employs a structural vector auto-regressive model with sign restrictions, requiring advanced econometric and statistical theory, placing it on the higher end of math complexity. Empirically, it uses daily market data (Bitcoin, Treasury yields, S&P 500, stablecoin market cap) and applies the model to real historical periods (2020-2024) with specific event studies, demonstrating significant data processing and implementation readiness. flowchart TD A["Research Goal: Identify factors driving cryptocurrency returns"] --> B["Data: 50+ crypto assets, 2015-2023"] B --> C["Methodology: Structural VAR Model"] C --> D["Computation: Impulse Response Functions & Variance Decomposition"] D --> E["Key Findings: 1) Liquidity shocks dominate volatility; 2) Bitcoin acts as market driver; 3) Stablecoins provide safe haven"]