Oskar Allerslev

Quantitative Infrastructure & Actuarial Engineering

MSc Actuarial Mathematics student at UCPH and Student Actuary at PFA Pension. I specialize in quantitative risk management (QRM), stochastic calculus, and machine learning. My focus is translating advanced mathematical theory into scalable, low-latency pricing engines and infrastructure using C++, Rust, and R.

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Experience & Education

Quantitative / Software Engineer

July 2026 - Present

Festina Finance

Incoming role focusing on financial software, risk management engines, and scalable investment infrastructure.

Student Actuary, Insurance Insights

Nov 2024 - June 2026

PFA Pension

Developed internal actuarial packages (Lifepack), engineered automated DevOps ETL pipelines, and built quantitative stakeholder reporting tools.

MSc Actuarial Mathematics

Sep 2024 - Present

University of Copenhagen

Focus: Advanced probability, QRM, ML, financial econometrics.

BSc Actuarial Mathematics

Sep 2021 - Jun 2024

University of Copenhagen

Thesis: Hybrid insurance pricing framework.
Highlight: Fire severity modeling using Extreme Value Theory (EVT).

Core Toolkit

Languages

R (Advanced/Package Dev) SQL Python C++ LaTeX

Tools & Infrastructure

Git Azure DevOps VS Code Shiny Tidyverse

Quantitative Math

Extreme Value Theory GLM Time Series (Cointegration) Machine Learning

Beyond Code

Applying data-driven optimization outside the terminal. Elite sprint swimmer utilizing structured biohacking and sleep analytics for peak performance. Personal Best: 30.09s in 50m Breaststroke at the Danish Open.

Featured Infrastructure

First Rust API (Stock VaR)

A high-performance backend built in Rust using Axum. Fetches ticker data and calculates Historical Value at Risk (VaR) based on continuous returns and empirical quantiles.

RustAxumAPI

Query Control Panel

Target Endpoint

Ticker

Interval

Range

Target URI

Rough Volatility Monte Carlo Pricer

Advanced derivative pricing framework based on fractional Brownian motions (fBM). Solves Stochastic Differential Equations (SDEs) to capture the roughness of electricity spot prices and financial markets.

C++MathSDEsfBM

Quantitative Research Note

Click to expand mathematical proof

Rough Heston Asset SDE

dS_t = \mu S_t dt + \sqrt{V_t} S_t dW_t^1

Variance Process (Rough Volatility)

V_t = V_0 \exp\left( \eta B_H(t) - \frac{1}{2} \eta^2 t^{2H} \right)

Cholesky Covariance Decomposition

Simulating the fractional Brownian motion $B_H(t)$ for $H < 0.5$ requires careful consideration of its auto-covariance structure. The C++ engine performs an exact Cholesky decomposition of the dense covariance matrix $\Sigma = LL^T$ to generate valid rough volatility paths, ensuring statistical integrity.

Nitor Energy Forecasting Competition 2026

Predictive machine learning pipeline forecasting highly volatile energy spot prices across 6 distinct markets. Optimized for RMSE utilizing specialized XGBoost models and domain-specific meteorological features.

RtidymodelsXGBoostMachine Learning

Lifepack (R Package)

Comprehensive R package for life insurance mathematics. Implements robust object-oriented programming (OOP), unit testing, and full roxygen2 documentation for production use.

> library(lifepack)

> Lambda <- function(x) matrix(c(-0.1, 0.1, 0.05, -0.05), 2, 2)

> R <- function(x, mu) matrix(c(mu, 0, 0, mu), 2, 2)

> equiv_premium(0, 80, Lambda, R, dR, 0.05, 0.03, 100)

[1] 0.04512

RdevtoolsOOPUnit Testing

CRAN