# Financial Econometrics Explained | Understanding the Basics

https://www.youtube.com/watch?v=FE-9HL3XYZk

[00:14] In the introductory video, we had seen that markets tell stories through numbers.
[00:19] We saw that the same profit number can lead to completely different market reactions.
[00:26] Which means numbers alone are not enough.
[00:32] In this video, we go one step deeper.
[00:35] When we look at market data, how do we investigate it systematically?
[00:49] Financial econometrics is not about memorizing formulas.
[00:52] It is about using data to test financial ideas.
[00:59] In finance, we constantly hear claims such as higher risk gives higher return, interest rates affect stock prices, or diversification reduces risk.
[01:13] Financial econometrics asks a very simple but
[01:16] powerful question.
[01:19] Does the data actually support these claims?
[01:26] It is a highly empirical branch of economics because it does not rely on belief but it relies on evidence.
[01:44] To understand where financial econometric fits, imagine a table with four legs.
[01:49] Mathematics gives structure.
[01:52] That is how we express ideas clearly.
[01:56] Statistics helps us deal with uncertainty because markets are never certain.
[02:01] Economics gives logic that is how people and firms behave.
[02:04] And finance connects everything to the real world such as markets, assets and risk.
[02:14] Financial econometric sits at the center
[02:17] of these four.
[02:20] Remove any one of them and the analyzis collapses.
[02:34] Let us start with mathematics first.
[02:37] Let me reassure you that the math used in financial econometrics is not meant to scare you.
[02:43] It is meant to clarify your thinking.
[02:51] Let me walk you through some basic mathematical concepts.
[02:56] A variable is simply something that can change.
[03:04] The values of stock returns, interest rates, and inflation keep changing and so they are called variables.
[03:09] An equation is nothing more than a structured way of saying a financial idea.
[03:16] It establishes the relationship between two or more variables.
[03:19] variables may move in the same direction
[03:22] or opposite direction leading to a
[03:24] positive or negative relationship respectively.
[03:29] When we say returns depend on risk.
[03:33] In mathematical language, we write it as
[03:36] return is equal to f of risk and we read
[03:40] it as return is a function of risk.
[03:44] Linear relationships help us understand
[03:47] proportional changes.
[03:50] Wherever you see a graph with a straight line, understand
[03:54] that the relationship is linear.
[04:01] Optimization helps us find the best
[04:04] possible choice like the best portfolio
[04:07] for a given level of risk.
[04:15] And when many variables move together,
[04:18] we use matrices to keep our thinking
[04:21] Organized.
[04:23] As you can see here the data is represented in a table with rows and columns or we say this is a matrix of the given data.
[04:34] So mathematics here is not computation it is a language.
[04:43] Finance is fundamentally uncertain and statistics helps us measure that uncertainty.
[04:51] The mean tells us the average return.
[04:54] Variance and standard deviation tell us how risky that return is.
[04:57] Regression analysis allows us to move from vague statements to measurable relationships.
[05:07] But markets are messy.
[05:10] That is why every model includes an error term to capture shocks, news, emotions, and surprises.
[05:18] Arch and GAGE models study how today's
[05:22] market volatility is influenced by yesterday's volatility.
[05:26] If the market was volatile yesterday, it is more likely to be volatile today.
[05:34] Behind every econometric analysis lies an economic model.
[05:39] Models can be diagrammatic, graphical, or mathematical.
[05:44] In finance, some well-known models include the efficient market hypothesis, the capital asset pricing model, arbitrage pricing theory, and the farmer French model.
[05:56] These models propose stories about how markets should behave.
[06:02] In this course, we will test whether those stories hold true in reality.
[06:11] Financial markets do not exist in isolation.
[06:13] Interest rates, inflation, and GDP quietly shape decisions.
[06:16] When a central bank changes interest rates,
[06:23] Borrowing costs change, corporate profits adjust, and markets respond.
[06:29] Financial econometrics helps us quantify these connections instead of guessing them.
[06:42] So how do we build the connection and quantify the relationship between variables?
[06:49] As analysts, we try to understand the data generating process.
[06:54] This includes three major steps.
[06:57] The first and the most important step is choosing a model.
[07:01] This is where your intuition comes into play.
[07:03] You will not use a thermometer to measure the distance.
[07:06] Right?
[07:09] The same idea follows here.
[07:12] We must know how a model is built and try to understand the variables used and what relationships they follow and then decide which model will best answer our question.
[07:25] After we select a model, we estimate the model using data.
[07:31] Estimation means to find numerical values for our parameters.
[07:35] And finally, we test whether the results or values we obtained are valid and reliable.
[07:45] This disciplined workflow separates analyszis from emotion.
[07:51] We have been talking a lot about data.
[07:54] But you must be wondering from where do we get the data?
[08:00] Financial data comes from markets, firms, regulators, governments, and increasingly from digital sources like news and online behavior.
[08:13] Depending on the question, we work with time series, cross-sectional, or panel data.
[08:20] Time series data tracks one financial variable over a period of time.
[08:25] Cross-section data compares many
[08:27] Financial units at one point in time.
[08:31] And panel data lets us see how different financial units change over time together.
[08:37] Remember that we track the same variables over time in a panel data.
[08:42] Now that you understand what financial econometrics is made of, we can start playing with data.
[08:51] The real challenge in this course is not building a model but knowing whether the model deserves your trust.