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.
New pricing methods for -quantile and early-exercise options using Spitzer identities.
New formula for implied volatility from Black-Scholes model.
We consider the pricing and hedging of exotic options in a model-independent set-up using \emph{shortfall risk and quantiles}. We assume that the marginal distributions at certain times are given. This is tantamount to calibrating the model to call options with discrete set of maturities but a continuum of strikes. In …
In this short paper, in order to price occupation-time options, such as (double-barrier) step options and quantile options, we derive various joint distributions of a mixed-exponential jump-diffusion process and its occupation times of intervals.
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.…
EX-DRL improves extreme quantile prediction for financial risk management.
The problem of quantile hedging for basket derivatives in the Black-Scholes model with correlation is considered. Explicit formulas for the probability maximizing function and the cost reduction function are derived. Applicability of the results for the widely traded derivatives as digital, quantos, outperformance and …
With model uncertainty characterized by a convex, possibly non-dominated set of probability measures, the agent minimizes the cost of hedging a path dependent contingent claim with given expected success ratio, in a discrete-time, semi-static market of stocks and options. Based on duality results which link quantile he…
MQF forecasts multivariate quantiles globally.
This paper investigates how the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility. We work in the flexible quantile regression framework and rely on recently developed model-free measures of int…
New method uses DistRL to estimate entire payoff distribution for financial derivatives.
A new hedging strategy uses deep reinforcement learning to manage gamma and vega risks.
Proposes a method to construct risk-neutral marginals from arbitrage-free option prices.
New method recalibrates VaR for option books, reducing forecast errors.
In the paper we develop mathematical tools of quantile hedging in incomplete market. Those could be used for two significant applications: o calculating the \textbf{optimal capital requirement imposed by Solvency II} (Directive 2009/138/EC of the European Parliament and of the Council) when the market and non-market ri…
In this paper we consider the problem of calculating the quantiles of a risky position, the dynamic of which is described as a continuous time regime-switching jump-diffusion, by using Fourier Transform methods. Furthermore, we study a classical option-based portfolio strategy which minimizes the Value-at-Risk of the h…
Optimal portfolio yields a digital option payoff.
An investor faced with a contingent claim may eliminate risk by perfect hedging, but as it is often quite expensive, he seeks partial hedging (quantile hedging or efficient hedging) that requires less capital and reduces the risk. Efficient hedging for European call option was considered in the standard Black-Scholes m…
For an exponential utility maximizing investment strategy in a Black-Scholes Setting, fixed upper and lower constraints are introduced on the terminal wealth. This is equivalent to combining the optimal strategy with options. The resulting distribution is investigated in terms of change of quantiles. The theory is illu…
New approach to goal-based investing using hedging and reinforcement learning.
Locus scores predictions for risk, reducing large-loss events.
The paper develops a new class of financial market models. These models are based on generalized telegraph processes: Markov random flows with alternating velocities and jumps occurring when the velocities are switching. While such markets may admit an arbitrage opportunity, the model under consideration is arbitrage-f…
Investigates methods to regularize quantile regression for accurate predictions.
Characteristic functions of several popular classes of distributions and processes admit analytic continuation into unions of strips and open coni around . The Fourier transform techniques reduces calculation of probability distributions and option prices to evaluation of integrals whose i…
A new method avoids quantile crossing in time series forecasting.
New risk measures for quantiles under ambiguity improve risk sharing.
Paper finds robust -quantiles equal to extremal distributions.
SCQRNN prevents quantile crossing and improves computational efficiency.
Axiomatizes -quantiles, a generalization of quantiles.
Develops a method to ensure accurate quantile forecasts across multiple levels.
Proposes a method to estimate conditional quantiles using both high-fidelity and low-fidelity data.
Sequential quantile estimation refers to incorporating observations into quantile estimates in an incremental fashion thus furnishing an online estimate of one or more quantiles at any given point in time. Sequential quantile estimation is also known as online quantile estimation. This area is relevant to the analysis …
The paper proposes a method for predicting equity premium using penalized quantile regression.
Supervised learning is an active research area, with numerous applications in diverse fields such as data analytics, computer vision, speech and audio processing, and image understanding. In most cases, the loss functions used in machine learning assume symmetric noise models, and seek to estimate the unknown function …
Improved conformalized quantile regression for adaptive prediction intervals.
Proposes a deep learning method to ensure non-crossing quantiles in conditional distributions.
Smoothed SGD improves quantile estimation without crossing curves.
Private estimation of many quantiles using differential privacy.
Bayesian method improves quantile estimation and subset selection.
New quantile methods improve uncertainty quantification across various models.
This paper analyzes quantiles of heavy-tailed distributions, separating projection direction and quantile threshold effects.
We develop a novel approach for the construction of quantile processes governing the stochastic dynamics of quantiles in continuous time. Two classes of quantile diffusions are identified: the first, which we largely focus on, features a dynamic random quantile level and allows for direct interpretation of the resultin…
ConquerNet smooths quantile regression for deep learning with minimax guarantees.
Improved quantile estimation model for VaR.
We develop quantile regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk ma…
Paper proposes a method to estimate multiple dynamic quantiles jointly.
This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, …
A new method forecasts financial tail risks by combining and weighting quantiles.