4 papers in the archive (2023–2025), average empirical rigor 2.4/10, average math complexity 8/10. Quadrant mix: Lab Rats ×4.
Works mostly in Volatility Modeling & Forecasting, Statistical Arbitrage & Pairs Trading, Options & Derivatives Pricing, Risk Management & Tail Risk.
Frequent co-authors: Kostiantyn Ralchenko (2), Giulia Di Nunno (1), Kęstutis Kubilius (1), Anton Yurchenko-Tytarenko (1), Andrey Pilipenko (1), Vitaliy Golomoziy (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
In this paper, we present a comprehensive survey of continuous stochastic volatility models, discussing their historical development and the key stylized facts that have driven the field. Special attention is dedicated to fractional and rough methods: we outline the motivation behind them and charac
We investigate the Gatheral model of double mean-reverting stochastic volatility, in which the drift term itself follows a mean-reverting process, and the overall model exhibits mean-reverting behavior. We demonstrate that such processes can attain values arbitrarily close to zero and remain near ze
We consider a continuous-time financial market with an asset whose price is modeled by a linear stochastic differential equation with drift and volatility switching driven by a uniformly ergodic jump Markov process with a countable state space (in fact, this is a Black-Scholes model with Markov swit
Entropic Value-at-Risk (EVaR) measure is a convenient coherent risk measure. Due to certain difficulties in finding its analytical representation, it was previously calculated explicitly only for the normal distribution. We succeeded to overcome these difficulties and to calculate Entropic Value-at-