Financial Statistics
News
Courseprogramme
The preliminary course programme for fall 2017 can later be found here:
There will be four (six) seminars for you to present the take home exam/project. These are December 20th (10.15-12 in MH:332b), December 22nd (10.15-12 in MH:229, 13.15-15 in MH:228), January 9th (10.15-12 in MH:227, 13.15-15 in MH:227) and January 13th (10.15-12 in MH:227).
The seminars on December 20th and January 9th will use data set A, the seminars on December 22nd and January 13th will use data set B.
You can sign up for the seminars via the
doodle
The data needed is
StockReturnA.mat,
CreditDataA.mat,
CreditReadMeA.txt, or
StockReturnB.mat,
CreditDataB.mat,
CreditReadMeB.txt,
Lectures
Exercises
- Exercise 5: 14.1, 14.2
- Exercise 4: Solve assignments 13.1, 13.2, 13.3
- Exercise 3: Solve assignments 7.3, 8.2, 8.5, 8.6 and 8.7, (and if you have time 7.1, 8.9)
- Exercise 2: Solve assignments 5.5, 5.6 (and if you have
time 5.4)
- Exercise 1: Solve assignments 4.5, and 4.1 (if you have
time).
Computer exercises
MLmax help: You can find a
demo file and
demo log-likelihood function.
- Computer Exercise 4 can be found here: Financial Statistics lab 4
Data files needed are
- Computer Exercise 3 can be found here:
Financial Statistics lab 3. Data files needed are
- Computer Exercise 2 can be found here: Financial
Statistics lab 2. Data files needed are
You may also want to use extra the Matlab routines ccc_mvgarch.p and ccc_mvgarch.m
- Computer exercise 1 can be found here: Financial
Statistics lab 1 You need some additional files:
Sign up for the computer exercise via SAM
Course
Coordinator/Lecturer
Erik
Lindström, tel 046-222 45 78, MH:221.
Teaching assistant
Carl Åkerlindh
Sidi
Mohamed Aly,
Course secretary
Maria
Lövgren
General information
University credits: 7.5 Grading scale: TH Level: A
Language of Instruction: The Course will be given in English
Lectures: Monday 8.15-10.00, MH:309a or MH:227, Tuesday
10.15-12.00 MH:362d
Exercise: Thursday 10.15-12.00 MH:362d
Computer exercises: varies
The course starts November 3rd.
Computer exercises
Aim
The course should be regarded as the statistical part of a
course package also including TEK180 Financial Valuation and Risk
Management and FMSN25 Valuation of Derivative Assets.
Its purpose is to give the student tools for constructing
models for risk valuation and pricing, based on data.
Knowledge and Understanding
For a passing grade the student must:
- handle variance models such as the GARCH family,
stochastic volatility, and models use for high-frequency
data,
- use basic tool from stochastic calculus: Ito's formula,
Girsanov transformation, martingales, Markov processes,
filtering,
- use tools for filtering of latent processes, such as
Kalman filters and particle filters,
- statistically validate models from some of the above
model families.
Skills and Abilities
For a passing grade the student must:
- be able to find suitable stochastic models for
financial data,
- work with stochastic calculus for pricing of financial
contracts and for transforming models so that data becomes
suitable for stochastic modelling,
- understand when and how filtering methods should be
applied,
- validate a chosen model in relative and absolute terms,
- solve all parts of a modelling problem using economic
and statistical theory (from this course and from other
courses) where the solution includes model specification,
inference, and model choice,
- present the solution in a written technical report, as
well as orally,
- utilise scientific articles within the field and
related fields.
Contents
The course deals with model building and estimation in
non-linear dynamic stochastic models for financial systems.
The models can have continuous or discrete time and the model
building concerns determining the model structure as well as
estimating possible parameters. Common model classes are,
e.g., GARCH models with discrete time or models based on
stochastic differential equations in continuous time. The
course participants will also meet statistical methods, such
as Maximum-likelihood and (generalised) moment methods for
parameter estimation, kernel estimation techniques, non-linear
filters for filtering and prediction, and particle filter
methods. The course also discusses prediction, optimisation,
and risk evaluation for systems based on such descriptions.
Formal required Qualifications
FMSF10/FMS045 Stationary stochastic processes
or corresponding courses.
Assume Qualifications
Preferably also one or several
of MIO140 Financial Management, FMS051
Time series analysis, TEK180 Financial Valuation and
Risk Management, and FMSN25MASM24
Valuation of Derivative Assets.
Literature
Lindstrom, E., Madsen, H and Nielsen, J. N. (2015) , Statistics for Finance CRC Press/Chapman
Hall
Additional handouts on the home page
Errata can be found here
Assessment
Written report and oral presentation of a larger project and
compulsory computer exercises. The course grade is based on
the project grade.