A research group focused on the pricing of financial assets — in particular options and derivatives. We study the deep structure of risk and price at the crossroads of theory, computation, and market data.
No-arbitrage plus diffusion assumptions produce analytic European option prices.
C = SN(d1) − Ke−rTN(d2)
F. Black & M. Scholes (1973); R. Merton's extension. Nobel context: 1997.
Continuous rebalancing controls local exposure and links pricing with hedging.
dΠ = dV − Δ dS, Δ =
Continuous-time finance foundations by Merton and modern derivatives desks.
Discounted derivative value equals expectation under an equivalent martingale measure.
V0 = e−rT EQ[Payoff]
Fundamental theorem of asset pricing and martingale methods.
Mean-variance ideas extend from portfolio construction to beta-based premia tests.
E[Ri] − Rf = βi(E[Rm] − Rf)
Markowitz (portfolio theory) and Sharpe (CAPM), Nobel Prize lineage.
Based at the School of Economics, Beijing Institute of Technology, meemlab.cc focuses on theoretical modeling, numerical methods, and empirical research in financial asset pricing. Option pricing sits at the center of our agenda, extending into stochastic volatility, interest-rate derivatives, credit risk, and high-frequency microstructure.
We believe the dialogue between rigorous mathematical models and real market data is key to understanding how asset prices form. The group maintains long-term collaborations with universities and financial institutions worldwide, and trains researchers with both theoretical depth and engineering capability.
Starting from Black–Scholes, we study non-standard models — stochastic volatility, jump-diffusion, and rough volatility — and methods for calibration and pricing.
Using high-frequency and cross-sectional data from Chinese and global markets, we test factor models, anomalies, and the sources of risk premia — often with machine-learning approaches to prediction and attribution.
We study the dynamics of implied volatility surfaces, variance risk premia, and VIX-like indices, and their role in risk management and investment decisions.
We develop efficient numerical schemes — Monte Carlo, PDE solvers, Fourier methods, and deep-learning solvers — for pricing and hedging high-dimensional derivatives.
We investigate limit order books, liquidity provision, and high-frequency trading, and their impact on price discovery and market efficiency.
Around VaR, ES, tail dependence, and systemic-risk measures — we build forward-looking indicators drawing on information embedded in derivative markets.
Our joint paper on American option pricing under rough volatility has been accepted by a top-tier journal.
Prof. XX delivered a keynote on option-implied information and macroeconomic forecasting.
We are recruiting 2 PhD and 3 Master students. Interested candidates are welcome to reach out.
The project studies derivative pricing and risk management under nonlinear stochastic volatility.
Covers the Black–Scholes framework, risk-neutral pricing, binomial trees and PDE methods, Monte Carlo, and volatility models.
Factor models, Fama–MacBeth regressions, GMM, and panel methods — with applications to the Chinese market.
Utility theory, CAPM, APT, no-arbitrage pricing, and their empirical performance in real markets.
For students pursuing a doctoral degree in finance or quantitative economics through the School of Economics' standard admission process.
For master's students interested in option pricing and financial engineering — growing through both research and engineering practice.
For undergraduates seeking early research exposure — we offer short-term training and thesis mentorship.
BIT is a national key university directly under the Ministry of Education. The School of Economics spans economics, finance, and international trade, with strong roots in financial engineering and quantitative economics.