Journal of Economic Dynamics & Control最新文献

We set up a DSGE model to study the macroeconomic consequences of a foreign central bank digital currency (CBDC) available to residents in a small open economy. We find that a gradual and permanent increase in the domestic households' preferences toward the foreign CBDC leads to a structural reduction in economic activity, especially if the CBDC is designed to be similar to domestic deposits. Imposing capital flow management measures on outflows, relaxing macroprudential policy, or selling foreign reserves can smooth the transition. A Taylor rule that targets PPI inflation is more effective in limiting the disruptive effects than a CPI targeting or an exchange rate peg. A central bank's liquidity facility available to commercial banks is able to avoid the long-run GDP loss, at the cost of a larger short-run consumption fall. We also show that an economy with a large stock of foreign CBDC is better shielded from exogenous increases in the interest rate on foreign debt, if the CBDC remuneration remains constant.

This paper introduces non-linear dimension reduction in factor-augmented vector autoregressions to analyze the effects of different economic shocks. I argue that controlling for non-linearities between a large-dimensional dataset and the latent factors is particularly useful during turbulent times of the business cycle. In simulations, I show that non-linear dimension reduction techniques yield good forecasting performance, especially when data is highly volatile. In an empirical application, I identify a monetary policy as well as an uncertainty shock excluding and including observations of the COVID-19 pandemic. Those two applications suggest that the non-linear FAVAR approaches are capable of dealing with the large outliers caused by the COVID-19 pandemic and yield reliable results in both scenarios.

This paper identifies smoothly varying industry uncertainty networks from option prices that contain valuable information about business cycles, especially in terms of forecasting. Such information is stronger when the network is formed on uncertainty hubs, firms identified as the main contributors to uncertainty shocks. The stronger predictive ability of the hubs-based network is robust to a wide range of checks, the inclusion of a large set of controls, and is also confirmed out-of-sample.

We propose a model in which sticky expectations concerning short-term interest rates generate joint predictability patterns in bond and currency markets. Our parsimonious specification can explain the downward sloping term structure of carry trade returns, difficult to replicate in a rational expectations framework. We offer empirical support for our approach and show that including a sticky short rate expectations channel into a standard affine term structure allows the model to better capture the drift patterns in the data.

I examine whether a financial intermediary issuing demandable debt can eliminate run equilibria by properly adjusting or restricting payouts in case of heavy redemptions. I study a model where investors learn their own liquidity needs over time and may withdraw preemptively before knowing their future liquidity needs. The difficulty in preventing runs is that, on the one hand, imposing temporary restrictions on withdrawals in case of excess redemptions may be necessary to avoid costly asset liquidations, while on the other hand, it is precisely the prospect of such restrictions on withdrawals that may induce investors to withdraw preemptively. Implementation of the first-best allocation without a run equilibrium is possible if and only if either liquidation costs are not too high or investors are not too risk averse.

We propose a reinforcement learning (RL) approach to solve the continuous-time mean-variance portfolio selection problem in a regime-switching market, where the market regime is unobservable. To encourage exploration for learning, we formulate an exploratory stochastic control problem with an entropy-regularized mean-variance objective. We obtain semi-analytical representations of the optimal value function and optimal policy, which involve unknown solutions to two linear parabolic partial differential equations (PDEs). We utilize these representations to parametrize the value function and policy for learning with the unknown solutions to the PDEs approximated based on polynomials. We develop an actor-critic RL algorithm to learn the optimal policy through interactions with the market environment. The algorithm carries out filtering to obtain the belief probability of the market regime and performs policy evaluation and policy gradient updates alternately. Empirical results demonstrate the advantages of our RL algorithm in relatively long-term investment problems over the classical control approach and an RL algorithm developed for the continuous-time mean-variance problem without considering regime switches.

We analyze the risk-return trade-off in the US Treasury market using a term structure model that features volatility-in-mean effects of multiple sources, and yet preserves tractable bond prices. We find a strong positive relation between risks and risk premia over the 1966-2018 period. While interest-rate risk is the main driver of such positive relation, macro risk plays a non-trivial role, and its omission leads to unstable estimates of the trade-off. Notably, macro risk contributes to the surge and consequent fall of risk premia around the 1980s, whereas it moves inversely with risk premia during the recent ‘low yield’ period.

We propose a new approach for the efficient and robust Bayesian estimation of medium- and large-scale DSGE models with occasionally binding constraints. At its core lies the Ensemble Kalman filter, a novel nonlinear recursive filter, which allows for fast likelihood approximations even for models with large state spaces. We combine the filter with a computationally efficient solution method for piece-wise linear models a state-of-the-art MCMC sampler. Using artificial data, we demonstrate that our approach accurately captures the true parameters of models with a lower bound on nominal interest rates, even with very long lower bound episodes. We use the approach to analyze the US business cycle dynamics until the Covid-19 pandemic, with a focus on the long lower bound episode after the Global Financial Crisis.

I show that the US tax multiplier depends on the direction of the tax change. The tax multiplier is significantly larger (in absolute value) for tax hikes than for tax cuts – regardless of whether I identify tax shocks via (i) the narrative approach or (ii) sign restrictions. The tax hike multiplier is strongly pro-cyclical, i.e., substantially larger in expansions. Variation in the tax cut multiplier over the business cycle is milder and statistically insignificant. A simple business cycle model with downward nominal wage rigidities can explain these results.

We investigate stale reference pricing and liquidity provision in dark pools using proprietary, participant-level regulatory data. We show a substantial amount of stale trading occurs, imposing large costs on passive dark pool participants. Consistent with these costs, HFTs almost never provide liquidity in the dark, instead frequently consuming liquidity, in particular in order to take advantage of stale reference prices. Finally, we show that market design interventions randomizing dark execution times are successful at countering dark pool latency arbitrage, protecting passive providers of dark liquidity. Our results have substantial implications for practitioners and policymakers aiming to improve liquidity provision in dark pools.