Structural VAR

Returns and Order Flow Imbalances: Intraday Dynamics and Macroeconomic News Effects ArXiv ID: 2508.06788 “View on arXiv” Authors: Makoto Takahashi Abstract We study the interaction between returns and order flow imbalances in the S&P 500 E-mini futures market using a structural VAR model identified through heteroskedasticity. The model is estimated at one-second frequency for each 15-minute interval, capturing both intraday variation and endogeneity due to time aggregation. We find that macroeconomic news announcements sharply reshape price-flow dynamics: price impact rises, flow impact declines, return volatility spikes, and flow volatility falls. Pooling across days, both price and flow impacts are significant at the one-second horizon, with estimates broadly consistent with stylized limit-order-book predictions. Impulse responses indicate that shocks dissipate almost entirely within a second. Structural parameters and volatilities also exhibit pronounced intraday variation tied to liquidity, trading intensity, and spreads. These results provide new evidence on high-frequency price formation and liquidity, highlighting the role of public information and order submission in shaping market quality. ...

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"]