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Hello everyone,
I want to ask about your view on the curriculum of the MSc in Finance at Bocconi from the perspective of Quant/Risk Management position. The program covers, among others:
- financial mathematics (stochastic processes, risk-neutral pricing, Brownian motion, SDEs, Black-Scholes, Monte Carlo Pricing of exotic derivatives)
- econometrics (linear algebra for statistics, linear models, principal component analysis, VAR models, ARMA, ARCH, GARCH, cointegration)
Quant Track specific courses (of choice)
- time-series analysis (Markov Chains, Hidden Markov Models, stochastic volatility, dynamic linear models, Kalman filter and smoother, MCMC)
- bayesian statistics (Monte Carlo, Gibbs sampler, and Metropolis-Hastings. Bayesian regression and classification, Bayesian clustering)
- numerical finance (Valuation and replication of American and path-dependent options via lattice methods, valuation and replication of basket options in a multidimensional diffusive framework, currency markets and quanto options, valuation of European options in the jump-diffusion model)
- machine learning for finance (regression, classification, recurrent and convolutional NNs, NLP)
What would you say about the content of the course, based on the description above? What seems to be missing and should be studies on the side (except for programming - most courses are in R so mostly research focused)? Thanks!
I want to ask about your view on the curriculum of the MSc in Finance at Bocconi from the perspective of Quant/Risk Management position. The program covers, among others:
- financial mathematics (stochastic processes, risk-neutral pricing, Brownian motion, SDEs, Black-Scholes, Monte Carlo Pricing of exotic derivatives)
- econometrics (linear algebra for statistics, linear models, principal component analysis, VAR models, ARMA, ARCH, GARCH, cointegration)
Quant Track specific courses (of choice)
- time-series analysis (Markov Chains, Hidden Markov Models, stochastic volatility, dynamic linear models, Kalman filter and smoother, MCMC)
- bayesian statistics (Monte Carlo, Gibbs sampler, and Metropolis-Hastings. Bayesian regression and classification, Bayesian clustering)
- numerical finance (Valuation and replication of American and path-dependent options via lattice methods, valuation and replication of basket options in a multidimensional diffusive framework, currency markets and quanto options, valuation of European options in the jump-diffusion model)
- machine learning for finance (regression, classification, recurrent and convolutional NNs, NLP)
What would you say about the content of the course, based on the description above? What seems to be missing and should be studies on the side (except for programming - most courses are in R so mostly research focused)? Thanks!
