arXiv research

Develops a new model for pricing without arbitrage opportunities.

A new method for pricing options with stochastic volatility and jumps.

The paper models financial data with multivariate jump processes.

We apply the formalism of the continuous time random walk to the study of financial data. The entire distribution of prices can be obtained once two auxiliary densities are known. These are the probability densities for the pausing time between successive jumps and the corresponding probability density for the magnitud…

The paper tackles efficient change point detection with limited samples.

This paper models CSI 300 index volatility using machine learning and addresses jump prediction.

Robustly detects jumps in high-frequency CIR and CKLS models.

Bayesian inference for Levy density with Gibbs posterior in discrete sampling.

Combustion reaction kinetics models are used for the description of a special class of bursty Financial Time Series. The small number of parameters they depend upon enable financial analysts to predict the time as well as the magnitude of the jump of the value of the portfolio. Several Financial Time Series are analyse…

Study shows how to control jump-diffusion processes with stable feedback controls in reinforcement learning.

European options can be priced by solving parabolic partial(-integro) differential equations under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models. American option prices can be obtained by solving linear complementary problems (LCPs) with the same operators. A finite difference di…

Detects jumps in financial asset prices with U-shape volatility.

This paper considers magnitude, asymptotics and duration of drawdowns for some Lévy processes. First, we revisit some existing results on the magnitude of drawdowns for spectrally negative Lévy processes using an approximation approach. For any spectrally negative Lévy process whose scale functions are well-behaved at …

Unified framework for growth models with environmental risk and pollution-dependent disasters.

A new method for pricing derivatives using self-exciting dynamics and finite-difference transforms.

We propose an efficient method to evaluate callable and putable bonds under a wide class of interest rate models, including the popular short rate diffusion models, as well as their time changed versions with jumps. The method is based on the eigenfunction expansion of the pricing operator. Given the set of call and pu…

Paper analyzes LPSA algorithm for constrained optimization, revealing phase transitions and bias-variance trade-offs.

We introduce and study the notion of sure profit via flash strategy, consisting of a high-frequency limit of buy-and-hold trading strategies. In a fully general setting, without imposing any semimartingale restriction, we prove that there are no sure profits via flash strategies if and only if asset prices do not exhib…

The paper introduces a new risk measure for financial models with jumps.

Develops a new framework for perpetual futures on binary prediction markets.

Prices in financial markets exhibit extreme jumps far more often than can be accounted for by external news. Further, magnitudes of price changes are correlated over long times. These so called stylized facts are quantified by scaling laws similar to, for example, turbulent fluids. They are believed to reflect the comp…

Developed a monotone numerical method for MV portfolio optimization under jump-diffusion models.

Estimates change point in high-dimensional dynamic graphical models.

News might trigger jump arrivals in financial time series. The "bad" and "good" news seems to have distinct impact. In the research, a double exponential jump distribution is applied to model downward and upward jumps. Bayesian double exponential jump-diffusion model is proposed. Theorems stated in the paper enable est…

We quantify how co-jumps impact correlations in currency markets. To disentangle the continuous part of quadratic covariation from co-jumps, and study the influence of co-jumps on correlations, we propose a new wavelet-based estimator. The proposed estimation framework is able to localize the co-jumps very precisely th…

Neural jump model improves option pricing accuracy.

Typically options with a path dependent payoff, such as Target Accumulation Redemption Note (TARN), are evaluated by a Monte Carlo method. This paper describes a finite difference scheme for pricing a TARN option. Key steps in the proposed scheme involve tracking of multiple one-dimensional finite difference solutions,…

We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quanti…

We investigate the extension of the multilevel Monte Carlo path simulation method to jump-diffusion SDEs. We consider models with finite rate activity, using a jump-adapted discretisation in which the jump times are computed and added to the standard uniform dis- cretisation times. The key component in multilevel analy…

A fast Monte Carlo method for additive processes and option pricing.

Study proposes pricing mechanism for cryptocurrency options.

Extends nonlinear filtering to predictable jump times.

The paper studies the continuous-time dynamics of VIX with stochastic volatility and jumps in VIX and volatility. Built on the general parametric affine model with stochastic volatility and jump in logarithm of VIX, we derive a linear relation between the stochastic volatility factor and VVIX index. We detect the exist…

A method to identify new classes of price jumps in financial markets.

Efficiently reconstructs jump-diffusion processes from data using neural networks.

Method detects jumps in high-frequency order prices using local minima.

Model predicts jump risk premia influencing cryptocurrency futures and option performance.

A machine learning method for short-maturity options with jumps and stochastic volatility.

RL for jump-diffusions applies to financial portfolio selection and option hedging.

In order to understand the origin of stock price jumps, we cross-correlate high-frequency time series of stock returns with different news feeds. We find that neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps. We find that the volatility patterns around jumps and aro…

In this note we investigate the consistency under inversion of jump diffusion processes in the Foreign Exchange (FX) market. In other terms, if the EUR/USD FX rate follows a given type of dynamics, under which conditions will USD/EUR follow the same type of dynamics? In order to give a numerical description of this pro…

The paper introduces walks with jumps for modeling neuron activity in hyperbolic space.

In quantitative finance, we often model asset prices as semimartingales, with drift, diffusion and jump components. The jump activity index measures the strength of the jumps at high frequencies, and is of interest both in model selection and fitting, and in volatility estimation. In this paper, we give a novel estimat…

Develops efficient methods for approximating densities of financial models with jumps.

Study on short-term behavior of ATM-IV for jump-diffusion model.

Study short maturity Asian options in jump-diffusion models with local volatility.

Estimation of the covariance matrix of asset returns from high frequency data is complicated by asynchronous returns, market mi- crostructure noise and jumps. One technique for addressing both asynchronous returns and market microstructure is the Kalman-EM (KEM) algorithm. However the KEM approach assumes log-normal pr…

New insights into Khovanov polynomials using tangle calculus.