---
title: Tastes, Productivities, and Macroeconomic Volatility
url: https://www.ml-quant.com/papers/ssrn/5220954/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5220954
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5220954
featured: 2025-04-23
citations: unknown
topic: Derivatives & Volatility
---


# Tastes, Productivities, and Macroeconomic Volatility

The paper uses a general equilibrium model to examine how differences in product tastes and firm technologies impact macroeconomic fluctuations.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5220954
- Identifier: SSRN 5220954
- Released: 2025-04-17
- First featured: Quant Letter No. 94 (2025-04-23): https://www.ml-quant.com/issues/2025-04-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Risk Revisited](https://www.ml-quant.com/papers/ssrn/4825844/): The study identifies recency, cluster, and sign as three factors shaping investors' risk perceptions of a stock, influencing trading volume and future volatility.
- [Rough Volatility: Fact or Artefact?](https://www.ml-quant.com/papers/arxiv/2203.13820/): Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise.
- [Numerical analysis of a particle system for the calibrated Heston-type local stochastic volatility model](https://www.ml-quant.com/papers/arxiv/2504.14343/): The article presents a Monte Carlo method for simulating the Heston-type local stochastic volatility model, addressing drift and diffusion coefficient challenges and proving a strong chaos propagation under certain conditions.
- [On the Rate of Convergence of Estimating the Hurst Parameter of Rough Stochastic Volatility Models](https://www.ml-quant.com/papers/arxiv/2504.09276/): The research extends the convergence result of a scale-invariant estimator, proving its consistent estimation of the Hurst parameter in rough stochastic volatility models.
- [Unified GARCH-Recurrent Neural Network in Financial Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2504.09380/): The paper proposes a new GARCH-GRU model for financial volatility forecasting, showing better computational efficiency and forecasting accuracy than other models.
- [Modeling Regime Structure and Informational Drivers of Stock Market Volatility via the Financial Chaos Index](https://www.ml-quant.com/papers/arxiv/2504.18958/): The research uses the Financial Chaos Index to study stock market volatility, identifying three market types and using sentiment predictors for volatility forecasting.