A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Persistence norms explain financial uncertainty better than volatility.
The paper addresses pricing interest rate derivatives in markets with volatility uncertainty.
Proposes a simpler method for quantifying uncertainty in time-series with volatility clustering.
The study examines how global economic policy uncertainty affects crude oil futures volatility.
We present an adaptive approach for valuing the European call option on assets with stochastic volatility. The essential feature of the method is a reduction of uncertainty in latent volatility due to a Bayesian learning procedure. Starting from a discrete-time stochastic volatility model, we derive a recurrence equati…
New method identifies uncertainty shocks in financial markets using revised VIX.
Paper studies portfolio investment under volatility uncertainty and short-sale constraints, improving risk-adjusted returns.
This paper improves VAE-based imputation of FX implied volatilities, reducing errors and handling uncertainty.
Proposes a new way to represent uncertainty using implied volatility.
Model quantifies uncertainty's impact on European option prices.
In this paper, we study term structure movements in the spirit of Heath, Jarrow, and Morton [Econometrica 60(1), 77-105] under volatility uncertainty. We model the instantaneous forward rate as a diffusion process driven by a G-Brownian motion. The G-Brownian motion represents the uncertainty about the volatility. With…
Paper defines multi-dimensional fractional Brownian motion under volatility uncertainty.
We establish the duality-formula for the superreplication price in a setting of volatility uncertainty which includes the example of "random G-expectation." In contrast to previous results, the contingent claim is not assumed to be quasi-continuous.
Study examines time-varying betas and their volatility in bank interest income and expense margins.
The paper analyzes investment and consumption strategies under uncertain market conditions.
The paper generalizes Feynman-Kac formula for volatility uncertainty.
We investigate financial markets under model risk caused by uncertain volatilities. For this purpose we consider a financial market that features volatility uncertainty. To have a mathematical consistent framework we use the notion of G-expectation and its corresponding G-Brownian motion recently introduced by Peng (20…
We construct a time-consistent sublinear expectation in the setting of volatility uncertainty. This mapping extends Peng's G-expectation by allowing the range of the volatility uncertainty to be stochastic. Our construction is purely probabilistic and based on an optimal control formulation with path-dependent control …
We study the pricing and hedging of derivative securities with uncertainty about the volatility of the underlying asset. Rather than taking all models from a prespecified class equally seriously, we penalise less plausible ones based on their "distance" to a reference local volatility model. In the limit for small unce…
We study the Hull-White model for the term structure of interest rates in the presence of volatility uncertainty. The uncertainty about the volatility is represented by a set of beliefs, which naturally leads to a sublinear expectation and a G-Brownian motion. The main question in this setting is how to find an arbitra…
We give explicit solutions for utility maximization of terminal wealth problem in the presence of Knightian uncertainty in continuous time in a complete market. We assume there is uncertainty on both drift and volatility of the underlying stocks, which induce nonequivalent measures on canonical space o…
Investment disputes increase stock volatility, especially for companies with negative outcomes.
The paper studies risk-based prices in financial markets under volatility uncertainty.
We consider classical Merton problem of terminal wealth maximization in finite horizon. We assume that the drift of the stock is following Ornstein-Uhlenbeck process and the volatility of it is following GARCH(1) process. In particular, both mean and volatility are unbounded. We assume that there is Knightian uncertain…
Paper develops a robust hedging framework to reduce market risk and uncertainty.
Study finds monetary policy uncertainty negatively impacts Bitcoin returns.
We study robust notions of good-deal hedging and valuation under combined uncertainty about the drifts and volatilities of asset prices. Good-deal bounds are determined by a subset of risk-neutral pricing measures such that not only opportunities for arbitrage are excluded but also deals that are too good, by restricti…
Study improves stock return uncertainty prediction using Gaussian mixture distributions.
We study super-replication of contingent claims in an illiquid market with model uncertainty. Illiquidity is captured by nonlinear transaction costs in discrete time and model uncertainty arises as our only assumption on stock price returns is that they are in a range specified by fixed volatility bounds. We provide a …
Cryptocurrency markets show higher spreads during extreme fear and greed phases.
Study shows COVID-19 cases increase stock market volatility in Pakistan.
We address the information content of European option prices about volatility in terms of the Fisher information matrix. We assume that observed option prices are centred on the theoretical price provided by Heston's model disturbed by additive Gaussian noise. We fit the likelihood function on the components of the VIX…
Study approximates worst-case stock trading under uncertainty, quantifying sensitivity.
We formulate and analyze an inverse problem using derivatives prices to obtain an implied filtering density on volatility's hidden state. Stochastic volatility is the unobserved state in a hidden Markov model (HMM) and can be tracked using Bayesian filtering. However, derivative data can be considered as conditional ex…
ProbRes calibrates probabilistic forecasts by learning volatility dynamics.
New model predicts financial market abnormalities using stock index uncertainties.
Optimal liquidation of an asset with unknown constant drift and stochastic regime-switching volatility is studied. The uncertainty about the drift is represented by an arbitrary probability distribution; the stochastic volatility is modelled by -state Markov chain. Using filtering theory, an equivalent reformulation…
LLMs produce volatile sentence-level sentiment classifications that affect financial decision-making.
The Financial Chaos Index models stock market volatility across three regimes based on mutual price fluctuations.
This paper studies insurers' robust strategies in a stochastic game with model uncertainty and volatility risk.
We introduce the concept of virtual volatility. This simple but new measure shows how to quantify the uncertainty in the forecast of the drift component of a random walk. The virtual volatility also is a useful tool in understanding the stochastic process for a given portfolio. In particular, and as an example, we were…
Bitcoin's attention is linked to Google Trends data, not general uncertainty.
Coronavirus impacts oil prices through volatility and direct effects.
We consider dynamic sublinear expectations (i.e., time-consistent coherent risk measures) whose scenario sets consist of singular measures corresponding to a general form of volatility uncertainty. We derive a càdlàg nonlinear martingale which is also the value process of a superhedging problem. The superhedging strate…
A machine learning approach to compute Black-Scholes prices with uncertain volatility.
This paper investigates how realized and option implied volatilities are related to the future quantiles of commodity returns. Whereas realized volatility measures ex-post uncertainty, volatility implied by option prices reveals the market's expectation and is often used as an ex-ante measure of the investor sentiment.…
Study finds market inefficiencies vary by time scale, with news uncertainty key.
Quantile deep learning improves time series prediction accuracy and uncertainty quantification.