Marlon Azinovic-Yang

Job Market Paper
Intergenerational Consequences of Rare Disasters, with Jan Žemlička, 2023.

We analyze the intergenerational consequences of rare disasters in a calibrated overlapping generations model featuring realistic household portfolios and equilibrium asset prices. Households own houses and trade in bonds and equity. In a disaster, young households suffer from reduced labor income and tightened borrowing constraints. Older households lose a large portion of their savings invested in risky assets. The relative winners are households shortly before retirement, who have a more stable labor income, are not borrowing constrained, and young enough to benefit from large returns of assets purchased during the disaster at depressed prices. In order to solve the model, we advance contemporary deep learning based solution methods along two complementary dimensions. First, we introduce an economics-inspired neural network architecture that, by construction, ensures that market clearing conditions are always satisfied. Second, we illustrate how to solve models with multiple assets by introducing them step-wise into the economy. These two innovations enable us to reduce the number of equilibrium conditions, which are not fulfilled exactly, and to substantially improve the stability of the training algorithm.

Supplementary material: Recording of the presentation at the DSE 2023 conference, method part only, on youtube, recordings of overview presentations at the ACM EC-24 conference at Yale University and the SUERF, ECB, Bank of Finland and Bank of Italy Workshop on the Use of AI in Economic Modelling and Forecasting workshop.
Links to a previous version, including only the methodological part: arxiv , ssrn.

Presented at: Reading Group, * UPenn (2022); CERGE-EI,* Prague (2023); Advances in Computational Economics and Finance,* UZH (2023); CEF Conference,* Nice (2023); DSE Conference, Lausanne (2023); EEA-ESEM Conference,* Barcelona (2023); Math Seminar,* ETH Zurich; Brown Bag Seminar, John Hopkins University (2023); Macro Lunch Seminar, UPenn (2023); Seminar, Toronto Metropolitan University (2024); Seminar, University of Copenhagen (2024); Seminar, University of North Carolina at Chapel Hill, (2024); Seminar, ESSEC Paris (2024); Seminar, IE Madrid (2024); Seminar, University of Sussex (2024); Seminar, National University of Singapore, (2024); SUERF, ECB, Bank of Finland and Bank of Italy Workshop on the Use of AI in Economic Modelling and Forecasting, (recording available on youtube, 2024); SED conference,* Barcelona (2024); ACM EC conference, Yale University (2024); ESIF AI+ML Conference, Cornell University (2024); Seminar, Wake Forest University (2024); SITE conference, Stanford (2024); SEA Conference, DC (2024); Seminar, University of Waterloo (2025); Seminar, Arizona State University (2025); T2M conference,* Paris (2025); SED conference,* University of Copenhagen (2025).

Publications
Deep Equilibrium Nets, with Luca Gaegauf and Simon Scheidegger, 2022. International Economic Review, 63: 1471-1525.

We introduce deep equilibrium nets (DEQNs)—a deep learning-based method to compute approximate functional rational expectations equilibria of economic models featuring a significant amount of heterogeneity, uncertainty, and occasionally binding constraints. DEQNs are neural networks trained in an unsupervised fashion to satisfy all equilibrium conditions along simulated paths of the economy. Since DEQNs approximate the equilibrium functions directly, simulating the economy is computationally cheap, and training data can be generated at virtually zero cost. We demonstrate that DEQNs can accurately solve economically relevant models by applying them to two challenging life-cycle models and a Bewley-style model with aggregate risk.

Supplementary material: code (link to github repository), poster (3rd place best poster award at Pasc conference 2019)
Teaching material: Part 1: Simplest start , Part 2: Uncertainty and constraints, Part 3: Exercises (with blancs) , Part 3: Exercises (with solutions) .

Working Papers
Asset Pricing in a Low Rate Environment, with Harold Linh Cole and Felix Kubler, 2023.

We examine asset prices in environments where the risk-free rate lies considerably below the growth rate. To do so, we introduce a tractable model of a production economy featuring heterogeneous trading technologies, as well as idiosyncratic and aggregate risk. We show that allowing for the possibility of firms exiting is crucial for matching key macroeconomic moments and, simultaneously, the risk-free rate, the market price of risk, and price-earnings ratios. In particular, our model allows us to consider calibrations that match the high observed market price of risk and average interest rates as low as 2-3.5 percent below the average growth rate. High values for risk aversion or non-standard preferences are not necessary for this. We use the model to examine the wealth distribution and asset prices in economies with very low real rates. We also examine under which conditions realistic calibrations allow for an infinite rollover of government debt. For our benchmark calibration, rollover is impossible even if the average risk-free rate lies 3.5 percent below the average growth rate.

Presented at: Macro Lunch Seminar,* Wharton (2023); Bristol Macro Workshop, University of Bristol (2023); EEA-ESEM Conference, Barcelona (2023); Stockman Workshop,* University of Rochester (2023); Macroeconomics Seminar,* University of Pittsburgh (2023); Macro-CRC Seminar,* University of Mannheim (2024); Seminar,* Ohio State University (2024); 18th Annual Conference on General Equilibrium and its Applications, Cowles Foundation, Yale University (2024); Seminar,* Tinbergen Institute (2024); SED conference, Barcelona (2024).

*: Presentation by coauthor

Work in Progress
Aging, the Aggregate MPC, and Business Cycle Volatility in OLGNK. Coming soon.

I document that younger households have a higher marginal propensity to consume (MPC) than older households, also after controlling for liquid wealth, income and household size. I replicate this finding in a general equilibrium model with overlapping generations, liquid and illiquid assets, and idiosyncratic income risk. Using the calibrated model as a laboratory, I study the implications of an aging society on business cycle volatility. Aging decreases the aggregate MPC in the economy and reduces business cycle volatility and the efficacy of fiscal stimulus.

Publications in Physics
Assessment of Quantum Annealing for the Construction of Satisfiability Filters, with Daniel Herr, Bettina Heim, Ethan Brown, and Matthias Troyer, 2017. SciPost Physics, 2, 013 (2017).

Satisfiability filters, introduced by S. A. Weaver et al. in 2014, are a new and promising type of filters to address set membership testing. In order to construct satisfiability filters, it is necessary to find disparate solutions to hard random k-SAT problems. This paper compares simulated annealing, simulated quantum annealing and walkSAT, an open-source SAT solver, in terms of their ability to find such solutions. The results indicate that solutions found by simulated quantum annealing are generally less disparate than solutions found by the other solvers and therefore less useful for the construction of satisfiability filters.

Job Market PaperIntergenerational Consequences of Rare Disasters, with Jan Žemlička, 2023.

PublicationsDeep Equilibrium Nets, with Luca Gaegauf and Simon Scheidegger, 2022. International Economic Review, 63: 1471-1525.

Working PapersAsset Pricing in a Low Rate Environment, with Harold Linh Cole and Felix Kubler, 2023.

Work in Progress Aging, the Aggregate MPC, and Business Cycle Volatility in OLGNK. Coming soon.

Job Market Paper
Intergenerational Consequences of Rare Disasters, with Jan Žemlička, 2023.

Publications
Deep Equilibrium Nets, with Luca Gaegauf and Simon Scheidegger, 2022. International Economic Review, 63: 1471-1525.

Working Papers
Asset Pricing in a Low Rate Environment, with Harold Linh Cole and Felix Kubler, 2023.

Work in Progress
Aging, the Aggregate MPC, and Business Cycle Volatility in OLGNK. Coming soon.