🗊Презентация The Nature and Purpose of Econometric

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 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. 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 For instance, the number of people in a particular underground carriage or the number of shares traded during a day. They do not necessarily have to be integers (whole numbers) though, and are often defined to be count numbers. 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. Examples of cardinal numbers would be the price of a share or of a building, and the number of houses in a street. Ordinal numbers can only be interpreted as providing a position or an ordering. Thus, for cardinal numbers, a figure of 12 implies a measure that is `twice as good' as a figure of 6. On the other hand, for an ordinal scale, a figure of 12 may be viewed as `better' than a figure of 6, but could not be considered twice as good. Examples of ordinal numbers would be the position of a runner in a race.

Cardinal, Ordinal and Nominal Numbers (Cont’d) Nominal numbers occur where there is no natural ordering of the values at all. Such data often arise when numerical values are arbitrarily assigned, such as telephone numbers or when codings are assigned to qualitative data (e.g. when describing the exchange that a US stock is traded on. Cardinal, ordinal and nominal variables may require different modelling approaches or at least different treatments, as should become evident in the subsequent chapters.

Returns in Financial Modelling It is preferable not to work directly with asset prices, so we usually convert the raw prices into a series of returns. There are two ways to do this: Simple returns or log returns   where, Rt denotes the return at time t pt denotes the asset price at time t ln denotes the natural logarithm We also ignore any dividend payments, or alternatively assume that the price series have been already adjusted to account for them.    

Log Returns The returns are also known as log price relatives, which will be used throughout this book. There are a number of reasons for this: 1. They have the nice property that they can be interpreted as continuously compounded returns. 2. Can add them up, e.g. if we want a weekly return and we have calculated daily log returns: r1 = ln p1/p0 = ln p1 - ln p0 r2 = ln p2/p1 = ln p2 - ln p1 r3 = ln p3/p2 = ln p3 - ln p2 r4 = ln p4/p3 = ln p4 - ln p3 r5 = ln p5/p4 = ln p5 - ln p4  ln p5 - ln p0 = ln p5/p0

A Disadvantage of using Log Returns   There is a disadvantage of using the log-returns. The simple return on a portfolio of assets is a weighted average of the simple returns on the individual assets: But this does not work for the continuously compounded returns.

Real Versus Nominal Series   The general level of prices has a tendency to rise most of the time because of inflation We may wish to transform nominal series into real ones to adjust them for inflation This is called deflating a series or displaying a series at constant prices We do this by taking the nominal series and dividing it by a price deflator: real seriest = nominal seriest  100 / deflatort (assuming that the base figure is 100) We only deflate series that are in nominal price terms, not quantity terms.

Deflating a Series   If we wanted to convert a series into a particular year’s figures (e.g. house prices in 2010 figures), we would use: real seriest = nominal seriest  deflatorreference year / deflatort This is the same equation as the previous slide except with the deflator for the reference year replacing the assumed deflator base figure of 100 Often the consumer price index, CPI, is used as the deflator series.

Steps involved in the formulation of econometric models Economic or Financial Theory (Previous Studies) Formulation of an Estimable Theoretical Model Collection of Data Model Estimation Is the Model Statistically Adequate? No Yes Reformulate Model Interpret Model Use for Analysis

Some Points to Consider when reading papers in the academic finance literature 1. Does the paper involve the development of a theoretical model or is it merely a technique looking for an application, or an exercise in data mining? 2. Is the data of “good quality”? Is it from a reliable source? Is the size of the sample sufficiently large for asymptotic theory to be invoked? 3. Have the techniques been validly applied? Have diagnostic tests been conducted for violations of any assumptions made in the estimation of the model?

Some Points to Consider when reading papers in the academic finance literature (cont’d) 4. Have the results been interpreted sensibly? Is the strength of the results exaggerated? Do the results actually address the questions posed by the authors? 5. Are the conclusions drawn appropriate given the results, or has the importance of the results of the paper been overstated?

Bayesian versus Classical Statistics The philosophical approach to model-building used here throughout is based on ‘classical statistics’ This involves postulating a theory and then setting up a model and collecting data to test that theory Based on the results from the model, the theory is supported or refuted There is, however, an entirely different approach known as Bayesian statistics Here, the theory and model are developed together The researcher starts with an assessment of existing knowledge or beliefs formulated as probabilities, known as priors The priors are combined with the data into a model

Bayesian versus Classical Statistics (Cont’d) The beliefs are then updated after estimating the model to form a set of posterior probabilities Bayesian statistics is a well established and popular approach, although less so than the classical one Some classical researchers are uncomfortable with the Bayesian use of prior probabilities based on judgement If the priors are very strong, a great deal of evidence from the data would be required to overturn them So the researcher would end up with the conclusions that he/she wanted in the first place! In the classical case by contrast, judgement is not supposed to enter the process and thus it is argued to be more objective.