We investigate the feedback effect of option hedging activity on the stability of the price of the underlying. While previous literature has focused on the effect of hedging activity on the volatility of the underlying, this paper focuses on directional instabilities arising from feedback...

The financial crisis has highlighted the need for models that can identify counterparty risk exposures and shock transmission processes at the systemic level. We use the euro area financial accounts (flow of funds) data to construct a sector-level network of bilateral balance sheet exposures and...

We provide a new method to derive the state price density per unit probability based on option prices and GARCH model. We derive the risk neutral distribution using the result in Breeden and Litzenberger (1978) and the historical density adapting the GARCH model of Barone-Adesi, Engle, and...

We use exchange-traded options to identify risks relevant to capital structure adjustments in firms. These forward-looking market-based risk measures provide significant explanatory power in predicting net leverage changes in excess of accounting data. They matter most during contractionary...

Market efficiency and the pricing kernel are closely related. A non-monotonic decreasing pricing kernel implies the existence of a trading strategy in contingent claims that stochastically dominates a direct investment in the market. Moreover, a market is assumed to be efficient only if no...

We find that option expensiveness, as measured by delta-hedged option returns, is higher for low-ESG stocks, indicating that investors pay a premium in the option market to hedge ESG-related uncertainty. We estimate this ESG premium to be about 0.3% per month. All three components of ESG...

We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those....

This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work...

We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the...

We show that options written on stocks with low prices are over-priced. This effect is robust to a variety of tests, controlling for common stock- and option- risk characteristics, and to reasonable transaction costs. Natural experiments corroborate this finding; options tend to become...