arXiv subjects

John Hull

Publications and source records attributed to John Hull.

7 recordsLinked to original sources

arXiv2025

Aligning Multilingual News for Stock Return Prediction

News spreads rapidly across languages and regions, but translations may lose subtle nuances. We propose a method to align sentences in multilingual news articles using optimal transport, identifying semantically similar content across languages. We apply this method to align more than 140,000 pairs of Bloomberg English and Japanese news articles covering around 3500 stocks in Tokyo exchange over 2012-2024. Aligned sentences are sparser, more interpretable, and exhibit higher semantic similarity. Return scores constructed from aligned sentences show stronger correlations with realized stock returns, and long-short trading strategies based on these alignments achieve 10\% higher Sharpe ratios than analyzing the full text sample.

q-fin.CP↗

arXiv2024

Understanding the Excess Bond Premium

We study the drivers of the Gilchrist and Zakraj\v{s}ek (2012) excess bond premium (EBP) through the lens of the news. The monthly attention the news pays to 180 topics (Bybee et al., 2024) captures up to 80% of the variation in the EBP, and this component of variation forecasts macroeconomic movements. Greater news attention to financial intermediaries and crises tends to drive up the EBP and portend macroeconomic downturns, while greater news attention to politics and science tends to drive down the EBP. Attention-based estimates of EBP largely drive out the forecast power of direct sentiment measures for macroeconomic fluctuations and predict the business cycle going back to the early 1900's. Overall, we attribute predictive variation about the EBP for macroeconomic movements to variation in news attention to financial intermediaries, crises, and politics.

q-fin.GN↗

arXiv2022

Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta neutral at the end of each day by taking a position in the underlying asset. We focus on how trades in the options can be used to manage gamma and vega. The option trades are subject to transaction costs. We consider three different objective functions. We reach conclusions on how the optimal hedging strategy depends on the trader's objective function, the level of transaction costs, and the maturity of the options used for hedging. We also investigate the robustness of the hedging strategy to the process assumed for the underlying asset.

q-fin.CP↗

arXiv2021

Deep Hedging of Derivatives Using Reinforcement Learning

This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process. The paper extends the basic reinforcement learning approach in a number of ways. First, it uses two different Q-functions so that both the expected value of the cost and the expected value of the square of the cost are tracked for different state/action combinations. This approach increases the range of objective functions that can be used. Second, it uses a learning algorithm that allows for continuous state and action space. Third, it compares the accounting P&L approach (where the hedged position is valued at each step) and the cash flow approach (where cash inflows and outflows are used). We find that a hybrid approach involving the use of an accounting P&L approach that incorporates a relatively simple valuation model works well. The valuation model does not have to correspond to the process assumed for the underlying asset price.

q-fin.CP↗

arXiv2021

Deep Learning for Exotic Option Valuation

A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. We refer to this as the model calibration approach (MCA). A disadvantage of MCA is that some information in the volatility surface is lost during the calibration process and the prices of exotic options will not in general be consistent with those of plain vanilla options. We consider an alternative approach where the structure of the user's preferred model is preserved but points on the volatility are features input to a neural network. We refer to this as the volatility feature approach (VFA) model. We conduct experiments showing that VFA can be expected to outperform MCA for the volatility surfaces encountered in practice. Once the upfront computational time has been invested in developing the neural network, the valuation of exotic options using VFA is very fast.

q-fin.CP↗

arXiv2021

Variational Autoencoders: A Hands-Off Approach to Volatility

A volatility surface is an important tool for pricing and hedging derivatives. The surface shows the volatility that is implied by the market price of an option on an asset as a function of the option's strike price and maturity. Often, market data is incomplete and it is necessary to estimate missing points on partially observed surfaces. In this paper, we show how variational autoencoders can be used for this task. The first step is to derive latent variables that can be used to construct synthetic volatility surfaces that are indistinguishable from those observed historically. The second step is to determine the synthetic surface generated by our latent variables that fits available data as closely as possible. As a dividend of our first step, the synthetic surfaces produced can also be used in stress testing, in market simulators for developing quantitative investment strategies, and for the valuation of exotic options. We illustrate our procedure and demonstrate its power using foreign exchange market data.

q-fin.CP↗

arXiv2014

A Trapped Field of 17.6 T in Melt-Processed, Bulk Gd-Ba-Cu-O Reinforced with Shrink-Fit Steel

The ability of large grain, REBa$_{2}$Cu$_{3}$O$_{7-δ}$ [(RE)BCO; RE = rare earth] bulk superconductors to trap magnetic field is determined by their critical current. With high trapped fields, however, bulk samples are subject to a relatively large Lorentz force, and their performance is limited primarily by their tensile strength. Consequently, sample reinforcement is the key to performance improvement in these technologically important materials. In this work, we report a trapped field of 17.6 T, the largest reported to date, in a stack of two, silver-doped GdBCO superconducting bulk samples, each of diameter 25 mm, fabricated by top-seeded melt growth (TSMG) and reinforced with shrink-fit stainless steel. This sample preparation technique has the advantage of being relatively straightforward and inexpensive to implement and offers the prospect of easy access to portable, high magnetic fields without any requirement for a sustaining current source.

cond-mat.supr-con↗