Reaction of the Capital Market to Monetary Policy Shocks under Economic Uncertainty: Evidence from the TVP-QVAR Model

Abstract
This study investigates the reaction of the capital market to monetary policy shocks under conditions of economic uncertainty using the Time-Varying Parameter Quantile Vector Autoregression (TVP-QVAR) model. The TVP-QVAR framework is applied to the quarterly data from 2001–2024 to analyze dynamic spillovers and net connectedness among stock market growth, liquidity growth (as a proxy for monetary policy), and the economic uncertainty index. The findings reveal that economic uncertainty is the primary driver of fluctuations in both liquidity growth and stock market returns. When uncertainty shocks are persistent and long-term, the net spillover from uncertainty to liquidity growth and stock market growth intensifies significantly. Short-term liquidity shocks that persist tend to transmit volatility to the stock market in the medium term; combined with rising uncertainty, this leads to severe turbulence in liquidity and equity markets over the long term. Consequently, controlling short-term economic volatility prevents excessive liquidity expansion. If policymakers overlook this, medium-term liquidity growth becomes destabilized through the uncertainty channel, transmitting stronger volatility to the stock market in the long run. These results underscore the need for timely uncertainty management to safeguard financial stability.
 
Extended abstract
Purpose: Developing economies rely heavily on well-functioning capital markets to channel savings into productive investment and support long-term growth. In Iran, the Tehran Stock Exchange plays a pivotal role as a barometer of economic health, yet it remains highly sensitive to macroeconomic instability. Monetary policies, primarily implemented through liquidity growth, interact with the stock market in complex ways, especially when economic uncertainty is elevated. Uncertainty, often measured by indices capturing policy volatility, inflation expectations, and geopolitical risks, amplifies the transmission of shocks and can lead to asymmetric and time-varying responses in asset prices.
Previous literature has documented that monetary policy shocks affect stock returns through interest-rate, credit, and liquidity channels, but these effects are not uniform. Conventional interest-rate shocks and unconventional balance-sheet policies exert different influences, while uncertainty can strengthen or weaken transmission depending on its duration and intensity. In the Iranian context, where oil revenues, fiscal dominance, and external sanctions contribute to persistent uncertainty, the interplay between liquidity growth, stock market performance, and economic uncertainty has received limited attention using modern time-varying and quantile-based methods. The earlier studies employing linear VAR, SVAR, or ARDL models have overlooked the dynamic, nonlinear, and tail-dependent nature of these relationships.
This study addresses the gap by examining how monetary policy shocks (proxied by liquidity growth) affect the capital market under varying levels of economic uncertainty. The central research questions are ‘How do spillovers and net connectedness among liquidity growth, stock market returns, and economic uncertainty evolve across short-, medium-, and long-term horizons?’ and ‘How does uncertainty act as a net transmitter of volatility?’ The objective is to provide policymakers with evidence-based insights into managing liquidity and uncertainty so as to promote financial stability. The analysis covers the period 2001–2024, capturing multiple business cycles, sanctions episodes, and post-COVID turbulence. It uses quarterly data from the Central Bank of Iran and the economic uncertainty index constructed by Baker et al. (2016).
 
Methodology: The study adopts the Time-Varying Parameter Quantile Vector Autoregression (TVP-QVAR) model introduced by Ando et al. (2018) and extended in the connectedness literature by Tiwari et al. (2022). Unlike standard VAR or DCC-GARCH frameworks, TVP-QVAR allows both the coefficients and variance-covariance matrix to evolve over time while capturing tail behavior at different quantiles. This approach is particularly suited to the financial data characterized by structural breaks, volatility clustering, and asymmetric responses during crises.
Three key variables are analyzed including a) stock market growth (quarterly percentage change in the Tehran Stock Exchange price index), b) liquidity growth (quarterly growth rate of M2 as the monetary policy instrument), and c) the economic uncertainty index (constructed following Baker, Bloom, and Davis, 2016). All the series are stationary according to the Elliott-Rothenberg-Stock (ERS) unit-root test and exhibit non-normal, leptokurtic distributions (confirmed by Jarque-Bera statistics).
The TVP-QVAR(p) is specified at quantile τ ∈ (0,1) and estimated via the generalized forecast error variance decomposition (FEVD) of Koop, Pesaran, and Potter (1996) and Pesaran and Shin (1998). The spillover indices are computed following Diebold and Yilmaz (2014) and adapted to the quantile and time-varying setting. The total connectedness index, directional “to” and “from” spillovers, and net spillovers are calculated for three horizons, namely short-term (1–4 months), medium-term (4–10 months), and long-term (beyond 10 months). The dynamic rolling-window estimations reveal how connectedness evolves over the sample period. The robustness checks include alternative quantile levels and lag specifications.
 
Findings and Discussion: Descriptive statistics show that liquidity growth recorded the highest mean and volatility, while stock market growth exhibited the lowest mean but still significant fluctuations. The TVP-QVAR results indicate that economic uncertainty is the dominant net transmitter of volatility across all horizons. In the short term, total connectedness reaches 11.78%, with stock market growth and uncertainty contributing the largest to spillovers (14.56% and 11.13%, respectively). Liquidity growth acts as the largest net receiver (−2.79%), suggesting that uncertainty drives liquidity fluctuations rather than vice versa.
In the medium term, total connectedness declines to 4.02%. Uncertainty and stock market growth remain net transmitters, while liquidity growth continues to absorb shocks. Persistent short-term liquidity shocks create medium-term transmission channels to the stock market, amplifying volatility when uncertainty rises. In the long term, total connectedness falls further to 3.77%, yet there is a rise in the net spillover from uncertainty to both liquidity and stock market growth. Long-lasting uncertainty shocks intensify turbulence in liquidity and equity markets, with stock market growth emerging as a net transmitter in medium- and long-term horizons.
Dynamic connectedness plots reveal structural shifts; connectedness was predominantly short-term until 2020, after which long-term linkages strengthened markedly. Peak spillovers from the stock market occurred around 2020, coinciding with heightened uncertainty during the COVID-19 period and the intensified sanctions. From 2021 onward, long-term spillovers from stock market growth to liquidity growth and vice versa became pronounced, underscoring the deepening feedback loops under prolonged uncertainty. Overall, uncertainty emerges as the central driver; that is, short-term control of volatility prevents liquidity expansion, while failure to do so allows uncertainty to destabilize liquidity in the medium term and transmit amplified shocks to the stock market in the long term.
 
Conclusions and Policy Implications: This study demonstrates that economic uncertainty is the primary source of volatility in Iran’s monetary policy (liquidity growth) and capital market. The TVP-QVAR framework reveals strong time-varying and horizon-dependent spillovers that linear models cannot capture. Uncertainty acts as a net transmitter, with its influence growing when shocks are persistent. Short-term liquidity fluctuations that persist create medium-term transmission to equities, while long-term uncertainty generates severe joint turbulence in liquidity and stock returns.
These findings impart certain policy recommendations. First, monetary authorities should prioritize short-term stabilization measures, such as transparent forward guidance and rule-based liquidity management, to break the uncertainty or liquidity feedback loop. Second, fiscal discipline and reduced reliance on monetization of deficits are essential to lower structural uncertainty. Third, given Iran’s oil-dependent economy, channeling oil revenues into productive investment rather than consumption can dampen uncertainty and support stable liquidity growth. Finally, market regulators should enhance risk-management tools, including derivatives and circuit breakers, so as to cushion long-term spillovers to the stock market.
By addressing uncertainty proactively, policymakers can mitigate the amplification of monetary shocks and foster a more resilient capital market. Future research could extend the framework to include additional macro-prudential variables or explore asymmetric quantile effects during specific crisis episodes. The results align with recent international evidence and extend it while providing context-specific insights for the emerging markets facing high uncertainty.