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.
This paper proposes a new integrated variance estimator based on order statistics within the framework of jump-diffusion models. Its ability to disentangle the integrated variance from the total process quadratic variation is confirmed by both simulated and empirical tests. For practical purposes, we introduce an itera…
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…
The paper reviews recent statistical methods for financial markets, focusing on jumps, volatility, and microstructure noise.
Identifying the instances of jumps in a discrete-time-series sample of a jump diffusion model is a challenging task. We have developed a novel statistical technique for jump detection and volatility estimation in a return time series data using a threshold method. The consistency of the volatility estimator has been ob…
Investigates JM for reducing downside risk in market regimes.
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…
The paper predicts cryptocurrency prices using a path-dependent Monte Carlo simulation.
We investigate the statistics of records in a random sequence of time steps. The sequence 's represents the position at step of a random walk `bridge' of steps that starts and ends at the origin. At each step, the increment of the position is a random ju…
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…
New model clusters mixed-type data with missing values, improving air quality analysis.
Develops a statistical model for SOFR term structure in incomplete markets.
Method detects jumps in high-frequency order prices using local minima.
Robustly detects jumps in high-frequency CIR and CKLS models.
Modeling Bitcoin prices and media attention using jump-type processes.
The main purpose of this work is to examine the behavior of the implied volatility smiles around jumps, contributing to the literature with a high-frequency analysis of the smile dynamics based on intra-day option data. From our high-frequency SPX S\&P500 index option dataset, we utilize the first three principal compo…
We consider the occurrence of record-breaking events in random walks with asymmetric jump distributions. The statistics of records in symmetric random walks was previously analyzed by Majumdar and Ziff and is well understood. Unlike the case of symmetric jump distributions, in the asymmetric case the statistics of reco…
Framework uses deep learning and statistical models to solve PDEs with discontinuous coefficients.
Unified analytical tool for non-Markovian jump processes.
This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metrop…
A simple Hawkes model have been developed for the price tick structure dynamics incorporating market microstructure noise and trade clustering. In this paper, the model is extended with random mark to deal with more realistic price tick structures of equities. We examine the impact of jump in price dynamics to the futu…
This paper introduces a non-parametric framework to statistically examine how news events, such as company or macroeconomic announcements, contribute to the pre- and post-event jump dynamics of stock prices under the intraday seasonality of the news and jumps. We demonstrate our framework, which has several advantages …
New method estimates volatility for Lévy processes with unbounded jumps efficiently.
This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of …
The study reveals unspanned risks in equity option risk premiums, explaining negative premiums for certain options.
The claim arrival process to an insurance company is modeled by a compound Poisson process whose intensity and/or jump size distribution changes at an unobservable time with a known distribution. It is in the insurance company's interest to detect the change time as soon as possible in order to re-evaluate a new fair v…
In informationally efficient financial markets, option prices and this implied volatility should immediately be adjusted to new information that arrives along with a jump in underlying's return, whereas gradual changes in implied volatility would indicate market inefficiency. Using minute-by-minute data on S&P 500 inde…
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…
New method estimates volatility for processes with jumps of unbounded variation.
Neural jump model improves option pricing accuracy.
Study compares statistical properties and power of divergence measures for credit risk monitoring.
New method improves uncertainty quantification in latent variable models.
Hybrid model improves synthetic equity data generation.
Develops a new model for pricing without arbitrage opportunities.
Proposes a new decision rule for continuous treatments.
Continuous time random walks (CTRWs) are used in physics to model anomalous diffusion, by incorporating a random waiting time between particle jumps. In finance, the particle jumps are log-returns and the waiting times measure delay between transactions. These two random variables (log-return and waiting time) are typi…
This paper extends subordinated models to include stochastic time changes, improving financial modeling.
Extends QHawkes to MQHawkes for analyzing financial co-jumps.
We set up a structural model to study credit risk for a portfolio containing several or many credit contracts. The model is based on a jump--diffusion process for the risk factors, i.e. for the company assets. We also include correlations between the companies. We discuss that models of this type have much in common wi…
Study proposes pricing mechanism for cryptocurrency options.
The paper models financial data with multivariate jump processes.
Model predicts jump risk premia influencing cryptocurrency futures and option performance.
Study short maturity Asian options in jump-diffusion models with local volatility.
Study on short-term behavior of ATM-IV for jump-diffusion model.
Extends nonlinear filtering to predictable jump times.
A machine learning method for short-maturity options with jumps and stochastic volatility.
This paper solves the inversion problem for jump processes using Markovian projections.
Optimal wealth strategy derived for jump-diffusion models with liabilities.
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…