Personal Website of Oleg Sokolinskiy, PhD



Conditional Dependence in Post-Crisis Markets: Dispersion and Correlation Skew Trades — published paper. Presentation slides

The Risk Management Implications of Using End of Day Consensus Pricing for Single Name CDS, with T. Ronen and B. Sopranzetti, published paper


Dynamic Stochastic Optimization in Python — html file download (best format for desktops) and github repository

Code sample: using C++ with MPI for HPC — github repository


Researched conditional dependence between equities (using QQQ ETF options):

  • developed a practical implementation of the dispersion trade with better correlation targeting

  • identified and analyzed an attractive correlation skew trade

  • obtained insights into factors driving the performance of correlation trades

Conducted an empirical analysis of intra-day quotes and end-of-day single name CDS consensus prices:

  • applied models and methods include intensity-based CDS pricing model, panel data analysis, exponential GARCH with Johnson SU innovations to forecast VaR

  • using end-of-day CDS consensus prices to risk manage CDS positions

Analyzed over 325,000 corporate bond transactions to quantify the impact of optionality (call provisions) on bond risk characteristics (greeks):

  • built a structural model for pricing corporate bonds that is practical to calibrate in an automated fashion

  • implemented a numerical solution of the pricing bivariate partial differential equation using ADI

  • developed the application in C++ with MPI and deployed on a supercomputer

Identified a flaw in stochastic volatility models:

  • calibrated process for the underlying is incompatible with its actual counterpart (using high-frequency econometric measures of variance and estimated by maximum likelihood) 

Working in an international team from 3 different universities, developed and applied a series of statistical tests to determine the best description for conditional dependence between bond yields in a forecasting application:

  • modeling framework: GARCH with empirical margins and copulas describing the dependence structure

  • tests can improve asset allocation, forecasting and risk management of FX and sovereign bond positions

Developed a structural model for quantifying the impact of debt rollover risk on option prices:

  • model generates local volatility dynamics as a consequence of debt rollover risk

  • higher fidelity treatment of bankruptcy

Optimized inventory replenishment policy in an environment with debt rollover risk:

  • dynamic stochastic programming (reinforcement learning, AI) methods


Precautionary Replenishment in Financially-Constrained Inventory Systems Subject to Credit Rollover Risk and Supply Disruption, with B. Melamed and B. Sopranzetti. Annals of Operations Research, 2018.

Stochastic Volatility Models: Faking a Smile, with D. Diavatopoulos. Handbook of Financial  Econometrics, Mathematics, Statistics, and Technology. World Scientific, expected publication date: 2019.

Equilibrium Rate Analysis of Cash Conversion Systems: The Case of Corporate Subsidiaries, with W. Chen, B. Melamed and B. Sopranzetti, Handbook of Financial  Econometrics, Mathematics, Statistics, and Technology. World Scientific, expected publication date: 2019.

Debt rollover-induced local volatility model. Review of Quantitative Finance and Accounting, 2018.

Inventory Management and Endogenous Demand: Investigating the Role of Customer Referrals, Defections, and Product Market Failure, with R. Leushner, D. Rogers and B. Sopranzetti. Decision Sciences, 2018.

Cash Conversion Systems in Corporate Subsidiaries, with W. Chen, B. Melamed and B. Sopranzetti, Manufacturing & Service Operations Management, Vol. 19, No. 4, 2017.

R-2GAM stochastic volatility model: flexibility and calibration, with C.-F. Lee. Review of Quantitative Finance and Accounting, Vol. 45, Issue 3, October 2015.

Comparing the accuracy of multivariate density forecasts in selected regions of the copula support, with C. Diks, V. Panchenko, and D. van Dijk. Journal of Economic Dynamics and Control, Vol. 48, November 2014.