# BUS 10123 Lecture Notes - Lecture 12: Financial Econometrics, Government Budget Balance, Stock Market Index

by OC2511532

School

Kent State UniversityDepartment

Business Administration InterdisciplinaryCourse Code

BUS 10123Professor

Eric Von HendrixLecture

12This

**preview**shows page 1. to view the full**5 pages of the document.**• Chapter 1

• Introduction

•

The Nature and Purpose of Econometrics

• What is Econometrics?

• Literal meaning is “measurement in economics”.

• Definition of financial econometrics:

The application of statistical and mathematical techniques to problems in finance.

• Examples of the kind of problems that

may be solved by an Econometrician

1. Testing whether financial markets are weak-form informationally efficient.

2. Testing whether the CAPM or APT represent superior models for the determination of

returns on risky assets.

3. Measuring and forecasting the volatility of bond returns.

4. Explaining the determinants of bond credit ratings used by the ratings agencies.

5. Modelling long-term relationships between prices and exchange rates

• Examples of the kind of problems that

may be solved by an Econometrician (cont’d)

6. Determining the optimal hedge ratio for a spot position in oil.

7. Testing technical trading rules to determine which makes the most money.

8. Testing the hypothesis that earnings or dividend announcements have no effect on

stock prices.

9. Testing whether spot or futures markets react more rapidly to news.

10.Forecasting the correlation between the returns to the stock indices of two countries.

• What are the Special Characteristics

of Financial Data?

• Frequency & quantity of data

Stock market prices are measured every time there is a trade or somebody posts a

new quote.

• Quality

Recorded asset prices are usually those at which the transaction took place. No

possibility for measurement error but financial data are “noisy”.

• Types of Data and Notation

• There are 3 types of data which econometricians might use for analysis:

1. Time series data

2. Cross-sectional data

3. Panel data, a combination of 1. & 2.

• The data may be quantitative (e.g. exchange rates, stock prices, number of shares

outstanding), or qualitative (e.g. day of the week).

• Examples of time series data

Series Frequency

GNP or unemployment monthly, or quarterly

government budget deficit annually

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money supply weekly

value of a stock market index as transactions occur

• Time Series versus Cross-sectional Data

• Examples of Problems that Could be Tackled Using a Time Series Regression

- How the value of a country’s stock index has varied with that country’s

macroeconomic fundamentals.

- How the value of a company’s stock price has varied when it announced the

value of its dividend payment.

- The effect on a country’s currency of an increase in its interest rate

• Cross-sectional data are data on one or more variables collected at a single point in

time, e.g.

- A poll of usage of internet stock broking services

- Cross-section of stock returns on the New York Stock Exchange

- A sample of bond credit ratings for UK banks

• Cross-sectional and Panel Data

• Examples of Problems that Could be Tackled Using a Cross-Sectional Regression

- The relationship between company size and the return to investing in its shares

- The relationship between a country’s GDP level and the probability that the

government will default on its sovereign debt.

• Panel Data has the dimensions of both time series and cross-sections, e.g. the daily

prices of a number of blue chip stocks over two years.

• It is common to denote each observation by the letter t and the total number of

observations by T for time series data, and to to denote each observation by the letter i

and the total number of observations by N for cross-sectional data.

• Continuous and Discrete Data

• Continuous data can take on any value and are not confined to take specific numbers.

• Their values are limited only by precision.

o For example, the rental yield on a property could be 6.2%, 6.24%, or 6.238%.

• On the other hand, discrete data can only take on certain values, which are usually

integers

o For instance, the number of people in a particular underground carriage or the

number of shares traded during a day.

o They do not necessarily have to be integers (whole numbers) though, and are

often defined to be count numbers.

o For example, until recently when they became ‘decimalised’, many financial

asset prices were quoted to the nearest 1/16 or 1/32 of a dollar.

• Cardinal, Ordinal and Nominal Numbers

• Another way in which we could classify numbers is according to whether they are

cardinal, ordinal, or nominal.

• Cardinal numbers are those where the actual numerical values that a particular variable

takes have meaning, and where there is an equal distance between the numerical

values.

o Examples of cardinal numbers would be the price of a share or of a building, and

the number of houses in a street.

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