We document that a theoretically founded, real-time, and easy-to-implement option-based measure, termed synthetic-stock difference (SSD), accurately estimates the part of stock's expected return arising from stock's transaction costs. We calculate SSD for U.S. optionable stocks. SSD can be more...

The Black-Scholes model, which is widely used to price financial options, assumes that volatility is constant as a function of strike price. However when market option prices are used to infer the volatility that is implied by those prices, it often exhibits a marked dependence on strike price...

This paper investigates the pricing of single-asset autocallable barrier reverse convertibles in the Heston local-stochastic volatility (LSV) model. Despite their complexity, autocallable structured notes are the most traded equity-linked exotic derivatives. The autocallable payoff embeds an...

Since commodity derivatives typically trade by futures (a.k.a. forwards), there is a need to model the dynamics of the forward curve. This article presents a generic multi-factor model for pricing commodity derivatives. Our theoretical results show that commodity prices are driven by multiple...

This paper conducts a first look into the regulated Bitcoin options market in the United States. We find bitcoin options to be ten times more illiquid than stock options, as measured by bid-ask spreads. The illiquidity significantly affects bitcoin options pricing: Given that investors are on...

We show that much of the profitability in equity option return strategies, that try to capture option mis-pricing by taking exposure to underlying volatility, can be explained by an IPCA model. The alpha reduction, relative to competing static factor models, is between 50% and 75% depending on...

The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent...

We develop a simple and efficient tree approach for pricing options under stochastic volatility. Our method encompasses the models of Heston, Hull-White, Stein-Stein, α-Hypergeometric, 3/2 and 4/2 models. Numerical results are provided to illustrate the effectiveness of the proposed method

The curse of dimensionality problem refers to a set of troubles arising when dealing with huge amount of data as happens, e.g., applying standard numerical methods to solve partial differential equations related to financial modeling. To overcome the latter issue, we propose a Deep Learning...

In this paper, we introduce a 3D finite dimensional Gaussian process (GP) regression approach for learning arbitrage-free swaption cubes. Based on the possibly noisy observations of swaption prices, the proposed 'constrained' GP regression approach is proven to be arbitrage-free along the strike...

Astonishingly little attention has been paid in academic literature to the 2008-2009 foreign exchange (FX) options debacle in Poland, the scale of which was unheard of. It affected not only an individual organization but a significant part of economy, being an example of a situation in which...

Hedging down-and-out puts (and up-and-out calls), where the maximum payoff is reached just before a barrier is hit that would render the claim worthless afterwards, is challenging. All hedging methods potentially lead to large errors when the underlying is already close to the barrier and the...

This article establishes the Poisson optional stopping times (POST) method by [22] as a near-universal method for solving liquidity-constrained American options, or, equivalently, penalised optimal-stopping problems. In this setup, the decision maker is permitted to "stop", i.e. exercise the...

We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By...

Breakthroughs and backlashes have marked progress in the development and diffusion of Artificial Intelligence (AI). These shocks make the investment in developing an Artificial General Intelligence (AGI) subject to considerable uncertainty. This paper applies a real options model, extended to...

We infer the forward-looking Bitcoin risk premium from options contracts. Using data from 2018 to 2020, we show that the expected excess returns for Bitcoin are time-varying and significantly higher than in equities or gold, averaging almost 80% per annum. A temporal analysis of the term...

This paper evaluates the underperformance of individual equity options relative to their replicating portfolios. Considering a high-dimensional set of variables, we use a machine learning approach to identify the characteristics of options and their underlying stocks that provide incremental...

The number of crossings of the implied volatility function with a fixed level is bounded above by the number of crossings of the risk-neutral density with the density of a log-normal distribution with the same mean as the forward price. It is bounded below by the number of convex payoffs priced...

We document a political risk premium of about 0.30% per month in the equity option market. High-political risk firms exhibit delta-hedged returns that are significantly lower than those of low-political risk firms. The effect holds both in a cross-sectional and in a time-series context. A...

In the past years, there has been an extensive investigation of the class of stochastic volatility models for the evaluation of options and complex derivatives. These models have proven to be extremely useful in generalizing the classic Black-Scholes economy and accounting for discrepancies...

India is often taken as a microcosm for much of the developing world because people living in many different circumstances in the country are representative of what is found in large parts of the developing world. This is particularly relevant for clean technologies that are consumer-led and...