---
title: Robert C. Merton's Contributions
url: https://www.ml-quant.com/papers/ssrn/4480625/
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 4480625
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4480625
featured: 2025-12-28
citations: unknown
topic: Derivatives & Volatility
---


# Robert C. Merton's Contributions

Robert C. Merton is a significant finance scholar known for his work on derivatives pricing and finance theories.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4480625
- Identifier: SSRN 4480625
- Released: 2023-03-17
- First featured: Quant Letter No. 124 (2025-12-28): https://www.ml-quant.com/issues/2025-12-28/
- 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.
- [Analysis of Fundamental and Technical Financial Ford Motor Company with The Arrangements of Implication Black Volatility](https://www.ml-quant.com/papers/ssrn/4482880/): The study shows that Ford Motor Company had its smallest earnings per share payout gap in 2020 compared to previous years.
- [Asymptotic Expansions for High-Frequency Option Data](https://www.ml-quant.com/papers/ssrn/4440168/): A new method for analyzing financial data helps test for sudden volatility changes, with evidence from SP500 options indicating significant variation.
- [Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2512.12250/): A combined model of Stochastic Volatility and Long Short Term Memory networks offers better volatility predictions for the S&P 500, outperforming traditional models for improved risk assessment.
- [Realised Volatility Forecasting: Machine Learning via Financial Word Embedding](https://www.ml-quant.com/papers/arxiv/2108.00480/): A new NLP framework shows that adding news text can improve stock volatility forecasting, particularly during volatile times, when combined with standard models.