英文标题：
《Option Pricing and Hedging for Discrete Time Autoregressive Hidden
Markov Model》
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作者：
Massimo Caccia and Bruno R\\\'emillard
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最新提交年份：
2017
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英文摘要：
In this paper we solve the discrete time mean-variance hedging problem when asset returns follow a multivariate autoregressive hidden Markov model. Time dependent volatility and serial dependence are well established properties of financial time series and our model covers both. To illustrate the relevance of our proposed methodology, we first compare the proposed model with the well-known hidden Markov model via likelihood ratio tests and a novel goodness-of-fit test on the S\\&P 500 daily returns. Secondly, we present out-of-sample hedging results on S\\&P 500 vanilla options as well as a trading strategy based on theoretical prices, which we compare to simpler models including the classical Black-Scholes delta-hedging approach.
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中文摘要：
本文解决了资产收益服从多元自回归隐马尔可夫模型时的离散时间均值-方差套期保值问题。时间依赖性波动率和序列依赖性是金融时间序列的公认属性，我们的模型涵盖了这两个属性。为了说明我们提出的方法的相关性，我们首先通过似然比检验和对标准普尔500指数日收益率的新拟合优度检验，将提出的模型与著名的隐马尔可夫模型进行比较。其次，我们给出了标普500普通期权的样本外套期保值结果以及基于理论价格的交易策略，并将其与包括经典Black-Scholes delta套期保值方法在内的简单模型进行了比较。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Pricing of Securities 证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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