paper-with-me

홈 › Papers

A Tale of Two Tails: A Model-free Approach to Estimating Disaster Risk Premia and Testing Asset Pricing Models

2021-05-18 · Tjeerd de Vries

I introduce a model-free methodology to assess the impact of disaster risk on the market return. Using S&P500 returns and the risk-neutral quantile function derived from option prices, I employ quantile regression to estimate local differences between the conditional physical and risk-neutral distributions. The results indicate substantial disparities primarily in the left-tail, reflecting the influence of disaster risk on the equity premium. These differences vary over time and persist beyond crisis periods. On average, the bottom 5% of returns contribute to 17% of the equity premium, shedding light on the Peso problem. I also find that disaster risk increases the stochastic discount factor's volatility. Using a lower bound observed from option prices on the left-tail difference between the physical and risk-neutral quantile functions, I obtain similar results, reinforcing the robustness of my findings.

📄 PDF Abstract BibTeX arXiv:2105.08208

Code (0)

등록된 구현이 없습니다.

Tasks

quantile regression

Similar Papers 제목 키워드 기반

Tale of tails using rule augmented sequence labeling for event extraction

2019-08-19 · Ayush Maheshwari, Hrishikesh Patel, Nandan Rathod, Ritesh Kumar 외

The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data. Moreover, the task becomes complex for tail (rarely occurring) labels wh…

Event Extraction

GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction

2025-09-12 · Joshua Dimasaka, Christian Geiß, Robert Muir-Wood, Emily So arxiv

In the aftermath of disasters, many institutions worldwide face challenges in monitoring changes in disaster risk, limiting assessment of progress towards the UN Sendai Framework for Disaster Risk Reduction 2015-2030. Wh…

CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics

2023-10-24 · Vsevolod Morozov, Artem Galliamov, Aleksandr Lukashevich, Antonina Kurdukova 외

Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disasters. Despite the widespread acceptance o…

A tale of two tails: 130 years of growth-at-risk

2023-02-17 · Martin Gächter, Elias Hasler, Florian Huber

We extend the existing growth-at-risk (GaR) literature by examining a long time period of 130 years in a time-varying parameter regression model. We identify several important insights for policymakers. First, both the l…

Vocal Bursts Valence Prediction

Model-Free Risk-Sensitive Reinforcement Learning

2021-11-04 · Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein 외

We extend temporal-difference (TD) learning in order to obtain risk-sensitive, model-free reinforcement learning algorithms. This extension can be regarded as modification of the Rescorla-Wagner rule, where the (sigmoida…

Decision Makingmodelreinforcement-learningReinforcement Learning+1