quiz poue econometrics.docx

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. 'Introductory Econometrics for Finance' © Chris Brooks 20...

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. 'Introductory Econometrics for Finance' © Chris Brooks 2008 Main fields of econometrics Main fields of econometrics Modeling: Correlation vs causality Econometrics helps reveal relationships between economic variables that might not be immediately obvious or anticipated Forecasting To predict future financial trends based on historical data, helping us anticipate and prepare for future developments. Statistical inference This involves drawing conclusions about a broader population based on the characteristics of a sample Simulation Assessing how changes in one variable can impact another, providing a way to model different scenarios and their potential outcomes. 01 02 04 03 'Introductory Econometrics for Finance' © Chris Brooks 2008 Examples of the kind of problems that may be solved by an Econometrician 1. Forecasting Asset Prices: Predicting future prices of assets like gold, currencies, or cryptocurrencies (e.g., Bitcoin) based on past trends and market data. 2. Measuring the Impact of Interest Rates: Analyzing how changes in interest rates affect stock market prices or housing markets. 3. Evaluating Investment Risk: Estimating the risk associated with different investments, such as stocks, bonds, or mutual funds, to help investors make informed decisions. 4. Predicting Company Stock Performance: Using financial data to estimate how a company's stock price might react to new earnings reports or major news events. 5. Assessing Portfolio Diversification: Analyzing how mixing different types of investments (stocks, bonds, etc.) can reduce risk while maximizing returns. 6. Analyzing Market Trends: Identifying long-term trends in financial markets (e.g., stock market or commodity prices) to guide investment strategies. 7. Evaluating Economic Policies: Studying how government policies (like tax changes) impact inflation, currency values, or stock market performance. The four pillars of financial econometrics Financial Data Models Financial theory Econometrics software (R, STATA...) Financial econometrics 'Introductory Econometrics for Finance' © Chris Brooks 2008 Financial data refers to the quantitative information related to the financial performance, position, and transactions of an individual, organization, or economy. 1) Frequency & quantity of data Stock market prices are measured every time a transaction takes place or someone publishes a new quotation ("supply vs. demand"). Financial Data: What are the Special Characteristics 'Introductory Econometrics for Finance' © Chris Brooks 2008 2) Quality While recorded asset prices reflect the actual transaction prices, financial data can be quite \"noisy.\" This means that even though we have precise transaction prices, the data can include random fluctuations and errors, making it sometimes hard to interpret (Inefficiency of Markets Theory). o Behavioral Biases: Investors often act irrationally, with biases like overconfidence o Information Asymmetry: Not all market participants have access to the same information, causing prices to adjust unevenly. o Market Frictions: Transaction costs, taxes, and liquidity constraints delay the adjustment of prices to new information. Financial Data: What are the Special Characteristics Financial Data Types and structures Data Types 1. The data may be quantitative (e.g. exchange rates, stock prices, number of shares outstanding), qualitative (e.g. day of the week). 2. The data may be discrete (e.g. number of trades, number of bonds) or continuous (e.g. stock prices, interest rates). 3. The data may be ordinal (e.g. risk levels like low, medium, high) or nominal (e.g. different investment types like stocks, bonds, and real estate). Data Structure 1. The data may be time series (e.g. daily stock prices over a year), cross-sectional (e.g. stock prices of different companies on a single day), or panel data (e.g. stock prices of the same companies tracked monthly over several years). Financial Data Types Financial Data Data type Time-indexed data, for use in problems involving time series: forecasting, modeling, etc. Refers to data observed at the same time for different individuals. Definition Limits No information on the impact of individual characteristics No information on adjustment dynamics Allows us to take into consideration: 1- The impact of individual characteristics (individual heterogeneity) 2- Adjustment dynamics Panel data advantages Time series data Panel Data Cross-sectional data Financial Data Structures Financial Data structure: Time series As shown in the following table, we have the same entity \'i\' observed across different time periods \'t\' Financial Data structure: Time series Financial Data structure: Crosssectional data In contrast to the time series structure, cross-sectional data pertains to the observation of different entities (denoted as \"i\") during the same period "t". Financial Data structure: Crosssectional data Cross-sectional data captures a snapshot of many things at one point in time. This figure presents the popularity of different cryptocurrencies at a specific moment. Financial Data structure: Panel Data The combination of the two previous types of data---time series and cross-sectional data--- is referred to as panel data