Safety, Liquidity, and Crises

Dmitry Kuvshinov (UPF and BSE)

Normally, we think of crises as being caused by economic agents taking on too much risk, e.g. firms and households accumulating too much risky debt. But in many recent and historical crisis episodes, something went wrong in the markets for safe and liquid (as opposed to risky) assets: i.e., those assets whose values should be stable, and which are, in principle, easy to sell (for example, government and mortgage bonds). However, despite their importance in crisis narratives and theories, there has been no systematic empirical analysis of the role these types of assets play in crises, mainly because we lack the necessary data.

This proposal will construct the first long-run database of quantities and prices of safe and liquid assets, covering many countries over the last 150 years, and use these data to study the contribution of safe assets and liquidity to crises, and to macro-financial risk more generally. SAFECRISES will study which assets are actually safe and liquid, how their quantities and prices have evolved over the long run, and how they change and interact with economic and financial risks before and after systemic crisis events.

Uniform Inference with Time Series

Katerina Petrova (UPF and BSE)

This project proposes a novel econometric approach suited for hypothesis testing and confidence interval construction in the presence of generic time series regressors with arbitrary persistence degree. The project will develop inference for a large class of regressor processes commonly encountered in macroeconomic and financial data, ranging from stationary, local-to-unit-root, explosive, long memory, time-varying parameter and other nonstationary processes as well as multivariate systems containing mixed components.

The key idea behind the approach is to build a new explanatory variable from the data which conforms to a standard central limit theory even when the original regressor does not. The resulting instrumental variable estimators based on this endogenously constructed instrument are shown to be asymptotically mixed-Gaussian regardless of the true stochastic nature of the regressor, implying standard inference for any IV-based self-normalised test.

The main contribution of the project is to place a large class of nonstandard processes with a wide range of dynamics and memory properties under a common econometric framework which delivers standard inference regardless of the regressorís stochastic properties. The asymptotic development of the procedure requires fundamental theoretical contributions such as a novel Granger-Johansen type representation theory for multivariate time series with mixed stochastic components and the asymptotic analysis of time series with different persistence types. The novel procedure is shown to be valid uniformly across persistence regimes and automatically delivers asymptotically correct inference without a priori knowledge of the regressor's true stochastic nature. In addition to its generality and theoretical coherence, the approach has the added advantage of ease of implementation (with closed-form estimators and tests that employ standard critical values), thus making it suitable for general practical application. This project aims to start in 2025.