Option-based Equity Risk Premiums

Deep Hedging: Learning to Simulate Equity Option Markets

We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data sets available for the training and evaluation of option trading strategies. We show that network-based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs…

Accounting for Earnings Announcements in the Pricing of Equity Options

We study an option pricing framework that accounts for the price impact of an earnings announcement (EA), and analyze the behavior of the implied volatility surface prior to the event. On the announcement date, we incorporate a random jump to the stock price to represent the shock due to earnings. We consider different distributions of the scheduled earnings jump as well as different underlying stock price dynamics before and after the EA date. Our main contributions include analytical option pricing formulas when the underlying stock price follows the Kou model along with a double-exponential…

镜中观Marketron：从不完全市场中的权益动态到期权定价

Marketron Through the Looking Glass: From Equity Dynamics to Option Pricing in Incomplete Markets

本文将Marketron模型从权益市场扩展到不完全市场下的期权定价，提出基于效用的对偶定价方法并高效求解HJB方程，同时验证校准后的模型能否同时再现标的资产对数收益的统计特性。

Efficient Pricing and Calibration of High-Dimensional Basket Options

This paper studies equity basket options -- i.e., multi-dimensional derivatives whose payoffs depend on the value of a weighted sum of the underlying stocks -- and develops a new and innovative approach to ensure consistency between options on individual stocks and on the index comprising them. Specifically, we show how to resolve a well-known problem that when individual constituent distributions of an equity index are inferred from the single-stock option markets and combined in a multi-dimensional local/stochastic volatility model, the resulting basket option prices will not generate a skew…

Optimal market completion through financial derivatives with applications to volatility risk

This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We introduce an efficient and accurate simulation-based method, applicable to generalized diffusion models, to approximate the optimal derivatives-based portfolio strategy. We build upon the double optimization approach (i.e. expected utility maximization and risk exposure minimization) proposed in Escobar-Anel et al. (2022); demonstrating that strangle options are the best choices for market completion within equity options. Furthermore, we…

Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments

This paper studies the equal risk pricing (ERP) framework for the valuation of European financial derivatives. This option pricing approach is consistent with global trading strategies by setting the premium as the value such that the residual hedging risk of the long and short positions in the option are equal under optimal hedging. The ERP setup of Marzban et al. (2020) is considered where residual hedging risk is quantified with convex risk measures. The main objective of this paper is to assess through extensive numerical experiments the impact of including options as hedging instruments w…

Deep Hedging of Long-Term Financial Derivatives

This study presents a deep reinforcement learning approach for global hedging of long-term financial derivatives. A similar setup as in Coleman et al. (2007) is considered with the risk management of lookback options embedded in guarantees of variable annuities with ratchet features. The deep hedging algorithm of Buehler et al. (2019a) is applied to optimize neural networks representing global hedging policies with both quadratic and non-quadratic penalties. To the best of the author's knowledge, this is the first paper that presents an extensive benchmarking of global policies for long-term c…

可违约债券流动性利差估计：一种基于期权的方法

Defaultable bond liquidity spread estimation: an option-based approach

本文提出基于回望期权的模型估计公司债券流动性利差，考虑利率波动与信用风险，并应用于意大利未报价债券，为定价非流动债券提供稳健工具。

获取波动率风险溢价：一种学习排序方法

Harvesting the Volatility Risk Premium: A Learning-to-Rank Approach

本文将跨截面学习排序应用于SPXW零到期日期权，结合保证金头寸规模等控制，经多窗口测试表明该方法的夏普比率远超被动基准与内部基线，特征交互项对其性能至关重要。

衡量风险：指数期权卖权策略中的凯利准则、VIX与混合方法

Sizing the Risk: Kelly, VIX, and Hybrid Approaches in Put-Writing on Index Options

本文针对标普500指数超短期限期权卖权策略，比较凯利准则、VIX状态缩放与新型混合方法三种头寸规模方案，发现混合方法在低波动环境下能兼顾收益与回撤控制，为机构投资者构建稳健的波动率溢价策略提供动态框架。