21
votes
Accepted
Fama-Macbeth second step confusion
Then for each month $t$, you run a cross-section regression:
$r_{i,t} = \lambda_0 + \hat{\beta}_i {\lambda}_t + \alpha_{i,t}$
Where: $\hat{\beta}_i \equiv [\beta_{i, MktRf}, \beta_{i, SMB}, \beta_{i,...
- 7,817
20
votes
Accepted
Why and when we should use the log variable?
Based on your paper and variables, I assume you ask about the use in econometric models. There are some rules of thumb for taking logs (do not take them for granted). See for example Wooldrigde: ...
- 7,659
13
votes
Accepted
Calculating alpha and its meaning
Alphas from a time-series regression are error terms in the cross-sectional, linear relationship between expected returns and factor betas. If a factor model were correct those error terms (the alphas)...
- 6,864
13
votes
Choosing the right statistical test for Mutual Fund Performance Evaluation
Define excess return $r^x_{it} = r_{it} - r^f_{t}$ as the return $i$ minus the risk free rate, and $f_{jt}$ similarly denotes the excess return of factor $j$ at time $t$. Let's say we have some factor ...
- 6,864
12
votes
Fama-Macbeth second step confusion
The two step Fama-Macbeth regression works as follows:
First, run a cross sectional regression in each period. I believe that you want to estimate risk premia for each of the Fama and French factors. ...
- 392
12
votes
Fama Mac-Beth (1973) vs Fixed effect
A more apples to apples comparison would be between (i) Fama-Macbeth procedure and (2) clustering standard-errors by date. Adding fixed-effects is somewhat different.
Problem: cross-sectional ...
- 6,864
11
votes
Machine Learning vs Regression and/or Why still use the latter?
I was just like you when I started out: I had learned a lot about machine learning (mainly neural networks and genetic algorithms/programming) and used it heavily. I also had learned about classic ...
- 27.3k
9
votes
Definitions of Beta
I slightly disagree with Alex’s comment. The CAPM does not read as
\begin{align*}
r_{i,t} = r_{f,t}+ \beta_{i} (r_{m,t}-r_{f,t}) + \varepsilon_{i,t}.
\end{align*}
There is an important difference ...
- 15.1k
8
votes
Accepted
What drives the idiosyncratic volatility puzzle?
Preliminary
The empirical finding of a strong negative cross-sectional relation between idiosyncratic volatility and future stock returns is highly inconsistent with the predictions of all theoretical ...
- 2,876
8
votes
Accepted
CAPM model as a regression
If you really believed the CAPM's prediction that $\alpha=0$, then imposing $\alpha=0$ in your estimation would indeed lead to your 2nd formula.
The problems?
The CAPM doesn't work so imposing a ...
- 6,864
8
votes
What is the textbook answer to dealing with multicollinearity?
As one of the interviewers suggested, the expected answer starts with PCA and SVD.
Before detailing it, let's take a paragraph about the way you seem to "misunderstand" the problem: ...
- 11.4k
7
votes
Accepted
Interpreting the coefficients of Fama-MacBeth regression
No, you cannot interpret the average return for the factor as the risk premium. The second stage regression is equivalent to building a set of portfolios that have no net investment, a unit exposure ...
- 1,396
7
votes
Accepted
Fama / French 3 Factor Data Not Giving Expected Results
That's perfectly normal. You are running a regression for a single stock. Single stocks have a lot of idiosyncratic risk (which is what the $R^2$ is capturing).
I just run the fama-french regression ...
- 7,817
6
votes
Accepted
Using cross-sectional factor model (BARRA type) returns in a time series factor model (Fama-French type)?
What you're describing sounds like the reverse of a Fama-Macbeth regression. The original Fama-Macbeth approach estimated rolling time series regressions to get CAPM betas and then doing a cross-...
- 5,371
6
votes
Does Kalman filter always improve over linear regression?
There is no a "yes/no answer" to that question. Generally Kalman Filter tends to be better than linear regression, but everything depends on
the data which you have,
how you calibrate your model.
...
6
votes
Accepted
Modelling and forecasting mixed frequency financial data
MIDAS is useful when you have a low frequency series and you want to include high frequency data in the regression. So for instance, if you want to forecast quarterly GDP data and want to include ...
- 5,371
6
votes
How exactly do I calculate and interpret factors in Fama-French model?
The clearest hands-on explanation I have seen so far is the following:
Bernstein, W.: Rolling Your Own: Three-Factor Analysis
Everything is explained very clearly and step-by-step with Excel.
...
- 27.3k
5
votes
Does Kalman filter always improve over linear regression?
There is no magic in the Kalman Filter. The linear regression model usually assumes the coefficients follow a random walk and as such it essentially boils down to an estimation followed by exponential ...
- 199
5
votes
Accepted
Linear Regression vs Mean Variance Optimization
In a linear regression approach you do the following:
$$
(X \beta - y)^2 \rightarrow Min
$$
thus you try to predict something. Your objective is quadratic. You usually add constraints on $\sum \...
- 13.6k
5
votes
Accepted
Hansen and Jagannathan distance
It would be easier to answer if you tell us where that equation came from (there are many ways of deriving the HJ distance) - in any case the numerator of your equation should be the expected return ...
- 7,817
5
votes
Accepted
How to do Fama French (1993) cross sectional regressions? A few questions
You say:
At this point I don't really get any further, as I am unsure about
which "cross section" is being talked about here. Since I have created
25 portfolios, I can only have all in all ...
- 7,817
5
votes
Why and when we should use the log variable?
I cite from the fantastic book by Bali, Engle, and Murray (2016): Empirical Asset Pricing: The Cross Section of Stock Returns.
In what follows, they talk about the pricing of size in the stock market (...
- 15.1k
4
votes
Testing Valuation, Size and Momentum (proprietary factors) from 1988-2013: No evidence of driving cross-sectional returns
I think your best shot is to share with us your 3,000 stocks. How far can that be from FF sample?
As a quick check I took the 25 book-to-market portfolios and the Fama-French 3 factor model and run ...
- 7,817
4
votes
Filtering out AR(1) effects before using stochastic volatility model
Even though it's a straightforward extension, it took me a while (a year? yikes!); but now you can easily incorporate Bayesian ar(1) (or more generally, Bayesian regression) in joint estimation by ...
4
votes
CAPM Calculations
The question above looks somewhat confused. Where's the error term?
A recipe for a standard calculation
It's customary to work with monthly returns.
For each portfolio $i$, calculate monthly ...
- 6,864
4
votes
Accepted
How can you determine the correct significance of the Shiller P/E regression?
Overlapping observations leads to correlation of error terms
Let $r_{t \rightarrow t+k}$ be the log return from time $t$ to $t+k$. Imagine you're running a regression forecasting $k$ year returns ...
- 6,864
4
votes
Is my data fittet to be significant?
The question you ask is in fact about what people in machine learning call overfitting:
the more you choose your "metaparameters" to provide high returns on your sample of days
the less you can trust ...
- 11.4k
4
votes
Calculating fund alpha using Fama-French 3 factor model?
In the long run, you'd probably be better off learning a real programming language like Python, R, or MATLAB. While you can do this in Excel using mmult, ...
- 6,864
4
votes
Question about Fama Macbeth Regression (Confusion about paper)
Fama-MacBeth procedure (Step 1):
So if my understanding is clear, first we would use the cross sectional regression to estimate 4*5,000=20,000 betas?
That is not right, because betas (and other risk-...
- 2,876
4
votes
Fama-Macbeth practitioner's step by step guide?
For each stock run a time series regression:
$r_{i,t} = \alpha + \beta F_t + \epsilon_t$
Then for each month $t$, you run a cross-section regression:
$r_{i,t} = \lambda_0 + \hat{\beta}_i {\lambda}_t + ...
- 7,817
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