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LSE-Fudan Double Master's in Financial Statistics and Chinese Economy

Page contents > Prerequisite Requirements and Mutually Exclusive Options

Guidelines for interpreting programme regulations

Classification scheme for the award of a taught master's degree (four units)
Exam sub-board local rules

Year 1 at LSE

The first year is spent at LSE. Students will take three compulsory statistics courses, and will also choose courses to the value of two units, from a range of options both within statistics and related areas, with a maximum of one unit's worth of options from outside the Statistics department.

Year 2 at Fudan

Upon successful completion of the first year, students will move to study the second year at the School of Economics at Fudan University. If students don't have an economics background, they will take additional economics courses.

Please note that places are limited on some optional courses. Admission onto any particular course is not guaranteed and may be subject to timetabling constraints and/or students meeting specific prerequisite requirements. Please refer to the progression rules below

MSc in Statistics (Financial Statistics) (LSE and Fudan)

Programme Code: TMFSEC

Department: Statistics

For students starting this programme of study in 2022/23

Year 1

Paper 1

ST425 Statistical Inference: Principles, Methods and Computation (1.0) #

Paper 2

ST422 Time Series (0.5) # and ST436 Financial Statistics (0.5) #

Paper 3

Courses to the value of 1.0 unit(s) from the following:

 

ST405 Multivariate Methods (0.5) #

 

ST411 Generalised Linear Modelling and Survival Analysis (0.5) #

 

ST416 Multilevel Modelling (0.5) #

 

ST418 Non-Linear Dynamics and the Analysis of Real Time Series (0.5) #  (not available 2022/23)

 

ST429 Statistical Methods for Risk Management (0.5) #

 

ST433 Computational Methods in Finance and Insurance (0.5) #

 

ST439 Stochastics for Derivatives Modelling (0.5) #

 

ST440 Recent Developments in Finance and Insurance (0.5) #

 

ST442 Longitudinal Data Analysis (0.5) #  (not available 2022/23)

 

ST443 Machine Learning and Data Mining (0.5) #

 

ST444 Computational Data Science (0.5) #

 

ST445 Managing and Visualising Data (0.5) #

 

ST446 Distributed Computing for Big Data (0.5) #

 

ST448 Insurance Risk (0.5) #

 

ST449 Artificial Intelligence (0.5)

 

ST451 Bayesian Machine Learning (0.5) #

 

ST454 Applied spatio-temporal analysis (0.5) #

 

ST455 Reinforcement Learning (0.5) #

 

ST456 Deep Learning (0.5) #

 

ST457 Graph Data Analytics and Representation Learning (0.5) #

 

MY459 Quantitative Text Analysis (0.5) #

 

MY461 Social Network Analysis (0.5)

Paper 4

Courses to the value of 1.0 unit(s) from the following:

 

ST405 Multivariate Methods (0.5) #

 

ST409 Stochastic Processes (0.5) #

 

ST411 Generalised Linear Modelling and Survival Analysis (0.5) #

 

ST416 Multilevel Modelling (0.5) #

 

ST418 Non-Linear Dynamics and the Analysis of Real Time Series (0.5) #  (not available 2022/23)

 

ST426 Applied Stochastic Processes (0.5)

 

ST429 Statistical Methods for Risk Management (0.5) #

 

ST433 Computational Methods in Finance and Insurance (0.5) #

 

ST439 Stochastics for Derivatives Modelling (0.5) #

 

ST440 Recent Developments in Finance and Insurance (0.5) #

 

ST442 Longitudinal Data Analysis (0.5) #  (not available 2022/23)

 

ST443 Machine Learning and Data Mining (0.5) #

 

ST444 Computational Data Science (0.5) #

 

ST445 Managing and Visualising Data (0.5) #

 

ST446 Distributed Computing for Big Data (0.5) #

 

ST448 Insurance Risk (0.5) #

 

ST449 Artificial Intelligence (0.5)

 

ST451 Bayesian Machine Learning (0.5) #

 

ST454 Applied spatio-temporal analysis (0.5) #

 

ST455 Reinforcement Learning (0.5) #

 

ST456 Deep Learning (0.5) #

 

ST457 Graph Data Analytics and Representation Learning (0.5) #

 

FM402 Financial Risk Analysis (0.5) #

 

FM404 Advanced Financial Economics (0.5) #

 

FM413 Fixed Income Markets (0.5) #

 

FM429 Asset Markets A (0.5) #

 

FM441 Derivatives (0.5) #

 

FM442 Quantitative Methods for Finance and Risk Analysis (0.5) #

 

MA407 Algorithms and Computation (0.5) #

 

MA415 The Mathematics of the Black and Scholes Theory (0.5) #

 

MA416 The Foundations of Interest Rate and Credit Risk Theory (0.5) #

 

MA420 Quantifying Risk and Modelling Alternative Markets (0.5) #  (not available 2022/23)

 

MA427 Mathematical Optimisation (0.5) #

 

MA435 Machine Learning in Financial Mathematics (0.5) #

 

MY456 Survey Methodology (0.5) #

 

MY457 Causal Inference for Observational and Experimental Studies (0.5) #

 

MY459 Quantitative Text Analysis (0.5) #

 

MY461 Social Network Analysis (0.5)

 

Or other non-ST course(s), with permission.

Prerequisite Requirements and Mutually Exclusive Options

# means there may be prerequisites for this course. Please view the course guide for more information.

The total value of all non-ST courses should not exceed one unit.

The Bologna Process facilitates comparability and compatibility between higher education systems across the European Higher Education Area. Some of the School's taught master's programmes are nine or ten months in duration. If you wish to proceed from these programmes to higher study in EHEA countries other than the UK, you should be aware that their recognition for such purposes is not guaranteed, due to the way in which ECTS credits are calculated.

Progress rules to proceed to year in Fudan: 

Students must pass at least three out of four units including the core courses ST425 (at paper 1) and ST422 and ST436 (at paper 2).

Students that have a one unit fail (but not a Bad Fail) including in any course taken as paper 1 or 2 will need to achieve compensation marks in their other courses in order to progress to Fudan as follows: a mark of 60 in at least one of the three passed units or an aggregate of 330 in those three passed units. A student that has failed any of the core courses ST425, ST422 and ST436 and can progress is still required to pass that failed course in order to be eligible for the award of the degree.

Any student with a fail that is unable to achieve the compensation rules as above must resit the failed course and pass in order to progress.

Any student who receives a Bad Fail mark cannot progress until that Bad Fail has been successfully resat and they then meet the progression rules outlined above. A Bad Fail mark cannot be compensated by other marks.

Students have one opportunity only to resit a failed LSE course.

The full programme must be successfully completed in order to be awarded the double degree. This means students who complete the year at LSE but go on to fail to progress after exhausting all of their attempts or are unable to complete the year at Fudan cannot be awarded an interim degree. I.e. they must successfully complete both LSE and Fudan programmes to achieve an overall award.

Note for prospective students:
For changes to graduate course and programme information for the next academic session, please see the graduate summary page for prospective students. Changes to course and programme information for future academic sessions can be found on the graduate summary page for future students.

 
 
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