- Lecture Summary
- Slides: Summary of the course
- MSc thesis suggestions

- Lecture 11:
- Paper: Sequential Calibration of Options
- Paper: Particle filters and Bayesian inference in financial econometrics
- Paper: An introduction to Sequential Monte Carlo Methods
- Example matlab code for Kalman and Particle filter
- Lecture 10: Slides: Kalman filters
- Lecture 9: Slides: Parameter estimation in SDEs
- Lecture 8:
- Slides: Pricing using SDEs
- BenchOp paper: Comparision of computational methods

- Lecture 7: Slides: High frequency volatility and introduction to SDEs
- Lecture 6: Slides: More GARCH models, GAS and SV
- Lecture 5: Slides: Variance instationarities and simple ARCH models
- Lecture 4: Slides: Non-linear models
- Lecture 3:
- Slides: Model
selection and validation

- Lecture 2:
- Lecture 1:
- Slides: Introduction and stylized facts

- Slides: Sequential Monte Carlo

- Exercise 5: 14.1, 14.2
- Exercise 4: Solve assignments 13.1, 13.2, 13.3
- Exercise 3: Solve assignments 5.6, 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, 5.4 (and if you have time 5.6)
- Exercise 1: Solve assignments 4.5, and 4.1 (if you have time).

**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- 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.
- 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.

Magnus Wiktorsson, tel 046-222 86 25, MH:130.

Language of Instruction: The Course will be given in English

Lectures: (typically!) Tuesday 10.15-12.00 E:C, Thursday 10:15-12:00 E:C

Exercise: Monday 8:15-12.00 MH:227

Computer exercises: Thurday 13-17 or Friday 8-12

Additional handouts on the home page

Errata errata can be found here

Questions: Erik
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Last modified: Wed Jun 19 17:32:12 CET
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**Introductory Meeting:**

2018-11-05, 10:15

MH:Riesz

**Approximate start date:**

Nov, 5, 2018

**Reading periods:**

ht2

**Lecturer:**

Magnus Wiktorsson **Assistants:**
Carl Åkerlindh

- Stand-alone Courses
- Engineering Physics
- Industrial Engineering and Management
- Master's Program in Mathematical Statistics
- Engineering Mathematics

Centre for
Mathematical Sciences, Box 118, SE-22100, Lund. Telefon: +46
46-222 00 00 (vx)