# 2018/19 Taught Postgraduate Module Catalogue

## MATH5315M Applied Statistics and Probability

### 15 creditsClass Size: 80

Module manager: Dr Martin Lopez-Garcia
Email: M.LopezGarcia@leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2018/19

### Pre-requisite qualifications

Fulfilment of the entry requirements to any of the above programmes is sufficient

Module replaces

LUBS5042M Financial Econometrics (MSc Financial Mathematics)

This module is not approved as an Elective

### Objectives

The aim of the module is to provide a grounding in the aspects of statistics, in particular statistical modelling, that are of relevance to actuarial and financial work. The module introduces and develops the fundamental concepts of probability and statistics used in applied financial analysis.

The course also provides training in practical skills required for empirical analyses.

Learning outcomes
Students will have opportunities to develop a good understanding of the fundamentals of probability and statistics.

They should be able to use these techniques in empirical analyses of financial data and present, interpret, discuss the results in a written report. Students are also expected to become proficient in the use of statistical software.

Skills outcomes
On completion of the module students are expected to be able to:
- communicate both verbally and in writing the theoretical and applied concepts of probability and statistics within a finance context
- carry out statistical tests and interpret the findings.

### Syllabus

PART I: Fundamentals of Probability
- Summarising data
- Introduction to probability
- Random variables
- Probability distributions
- Generating functions
- Joint distributions
- The central limit theorem
- Conditional expectation.

PART II: Fundamentals of Statistics
- Sampling and statistical inference
- Point estimation
- Confidence intervals
- Hypothesis testing.

PART III: Applied Statistics
- Correlation and regression (OLS)
- Analysis of variance (ANOVA)
- Univariate time series analysis and forecasting (ARMA)
- Multivariate time series analysis (VAR)
- Cointegration
- Volatility models (ARCH/GARCH).

### Teaching methods

 Delivery type Number Length hours Student hours Lecture 16 2.00 32.00 Practical 5 1.00 5.00 Seminar 6 1.00 6.00 Private study hours 107.00 Total Contact hours 43.00 Total hours (100hr per 10 credits) 150.00

### Private study

- Pre-lecture reading 1 h each (32 h)
- Post-lecture reading 1 h each (32 h)
- Seminar reading and preparation (30 h)
- Completion of assessed coursework (20h).

### Opportunities for Formative Feedback

- Student contributions made to seminar discussion.
- At least one piece of assessed coursework.

### Methods of assessment

Coursework
 Assessment type Notes % of formal assessment Project max 2,000 words 30.00 Total percentage (Assessment Coursework) 30.00

The resit for this module will be 100% by 2hours examination

Exams
 Exam type Exam duration % of formal assessment Standard exam (closed essays, MCQs etc) 2 hr 70.00 Total percentage (Assessment Exams) 70.00

The resit for this module will be 100% by 2 hours examination