Summary:
The scale of excess reserves held by the banking system is a core variable for understanding the central bank's monetary policy transmission mechanism, market interest rate formation, and the stability of the financial system. Particularly in the context of China's monetary policy framework transitioning towards price-based regulation and the increasing complexity of the banking system's liquidity environment, an in-depth analysis of the micro-level drivers of excess reserves holds significant theoretical value and practical relevance. However, existing research predominantly focuses on the experiences of developed economies, leaving the reserve decision-making mechanisms within China's unique institutional context underexplored. This paper aims to systematically investigate the driving mechanisms and heterogeneous patterns of excess reserve holdings among Chinese commercial banks, following a framework of “theory construction-empirical identification-mechanism deepening”. Theoretically, this paper draws on the concept of convenience yield, innovatively introducing the notion of “convenience yield of excess reserves” into the bank liquidity management framework. It derives a log-linear reserve demand equation that incorporates interest rate spreads, deposit scale and structure, and bond holdings, thus laying a micro-founded theoretical basis for the empirical analysis. Empirically, the paper primarily employs panel fixed-effects models based on semi-annual panel data of 42 A-share listed Chinese commercial banks from 2010 to 2024 (data sourced from CEIC and Wind databases). To precisely identify the complex effects of monetary policy, the model introduces a central bank announcement dummy variable and its interaction term with the interest rate spread, aiming to disentangle “price shocks” from “information shocks”. Simultaneously, to capture the intertemporal effects of banks' asset allocation and mitigate endogeneity, lagged two-period bond holdings are used. Furthermore, this paper constructs a interbank bond market liquidity model and complements the analysis with subgroup and subperiod regressions to fully explore heterogeneous mechanisms. The empirical investigation yields four core findings: First, banks' precautionary motives are primarily driven by the instability of their liability structure. Demand deposits are the dominant driver of excess reserve holdings (coefficient approx. 0.55～0.60), while the impact of time deposits is insignificant, confirming that reserve decisions are made to hedge the liquidity risk from more volatile liabilities. Second, banks' asset allocation strategies exhibit significant intertemporal effects and persistence. Lagged one-year (two-period) bond holdings are significantly negatively correlated with current excess reserves, revealing the “stickiness” of banks' yield-seeking behavior rather than a simple risk-hedging motive. Third, central bank announcements, via the “information shock” channel, significantly alter banks' responses to interest rate signals, but the effect is highly “state-dependent”. During non-announcement periods, rising interest rates lead banks to increase reserves (precautionary motive dominates). However, during announcement windows, this relationship systematically reverses, with banks tending to release reserves in pursuit of higher yields. Yet, this communication effectiveness is not unconditional: the “information effect” of announcements is most significant during policy tightening phases, while during early monetary easing periods and high-uncertainty periods, the role of central bank communication is significantly weakened. Fourth, liquidity management paradigms differ fundamentally across bank sizes. Small and medium-sized banks' behavior aligns with the classic “risk-cost” trade-off model, with their reserve decisions responding to deposits, asset allocation, and interest rate signals. In contrast, large banks, as systemic payment nodes, have reserve holdings primarily driven by functional demand, exhibiting significant “passivation” (insensitivity) to short-term interest rate signals representing opportunity costs. These findings carry important implications for optimizing the monetary policy framework and liquidity regulation system. First, policy operations must shift from an aggregate-focused to a structure-sensitive perspective, fully considering the heterogeneity of the banking system, particularly the “structural rigidity” of large banks' reserve demand, while optimizing liquidity support frameworks for smaller banks. Second, the effectiveness of forward guidance should be enhanced by establishing a “state-dependent” communication strategy. Policymakers should flexibly adjust communication content and methods based on real-time market liquidity status and policy stances to maximize expectation-stabilizing effects. Third, the liquidity regulation framework should reflect bank heterogeneity and intertemporal risks. This requires moving beyond static, point-in-time assessments to establish intertemporal macroprudential monitoring of banks' balance sheet strategies, while considering differentiated weights reflecting banks' systemic importance and payment-clearing roles.
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