巨灾风险与巨灾股票看跌期权定价

IF 1.3 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS Computational Management Science Pub Date : 2021-01-01 Epub Date: 2021-03-18 DOI:10.1007/s10287-021-00391-y
Massimo Arnone, Michele Leonardo Bianchi, Anna Grazia Quaranta, Gian Luca Tassinari
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引用次数: 0

摘要

在本文中,在回顾了保险公司用来对冲巨灾风险的最常见金融策略和产品之后,我们研究了一种基于跳跃过程的期权定价模型,其中巨灾事件由一个具有负跳跃的复合泊松过程来捕捉。鉴于巨灾股票看跌期权(CatEPuts)在此背景下的重要性,我们引入了一种定价方法,该方法不仅在理论上有所贡献,其适用性仍局限于纯粹的数字示例和实验,而且可以从真实数据开始实施,并应用于真实 CatEPuts 的评估。我们提出了一个基于历史对数收益率、市值和期权隐含波动率的校准框架。然后将校准后的参数用于对 2020 年 1 月至 4 月高波动期意大利主要保险公司股票的 CatEPuts 进行定价。我们发现,普通看跌期权和 CatEPuts 期权之间的比率严格取决于隐含波动率微笑的形状,并且随着时间的推移而变化。
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Catastrophic risks and the pricing of catastrophe equity put options.

In this paper, after a review of the most common financial strategies and products that insurance companies use to hedge catastrophic risks, we study an option pricing model based on processes with jumps where the catastrophic event is captured by a compound Poisson process with negative jumps. Given the importance that catastrophe equity put options (CatEPuts) have in this context, we introduce a pricing approach that provides not only a theoretical contribution whose applicability remains confined to purely numerical examples and experiments, but which can be implemented starting from real data and applied to the evaluation of real CatEPuts. We propose a calibration framework based on historical log-returns, market capitalization and option implied volatilities. The calibrated parameters are then considered to price CatEPuts written on the stock of the main Italian insurance company over the high volatile period from January to April 2020. We show that the ratio between plain-vanilla put options and CatEPuts strictly depends on the shape of the implied volatility smile and it varies over time.

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来源期刊
Computational Management Science
Computational Management Science SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
1.90
自引率
11.10%
发文量
13
期刊介绍: Computational Management Science (CMS) is an international journal focusing on all computational aspects of management science. These include theoretical and empirical analysis of computational models; computational statistics; analysis and applications of constrained, unconstrained, robust, stochastic and combinatorial optimisation algorithms; dynamic models, such as dynamic programming and decision trees; new search tools and algorithms for global optimisation, modelling, learning and forecasting; models and tools of knowledge acquisition. The emphasis on computational paradigms is an intended feature of CMS, distinguishing it from more classical operations research journals. Officially cited as: Comput Manag Sci
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