Techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.

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10
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4answers
4k views

R: Fast and efficient way of running a multivariate regression across a (really) large panel (First pass of Fama MacBeth)

I am attempting to run a rolling multivariate regression (14 explanatory variables) across a panel of 5000 stocks: For each of the 5000 stocks, I run 284 regressions (by rolling over my sample ...
1
vote
0answers
100 views

Insignificant or significant explanatory power over risk adjusted returns?

Currently iam working on my master thesis which is about risk adjusted returns in the Asia Pacific REIT market. The goal of the paper is to determine/find variables that conceive explanatory power ...
1
vote
1answer
143 views

Regression of Unequally Weighted Portfolio against a Single Index

When I regress a single stock against a market index, I get a high value of R2 and beta closer to 1. APPL.fit <- lm(APPL ~ JKSE) When I regress an unequally ...
2
votes
0answers
227 views

Any one know how to implement the Heston and Rouwenhorst country-sector effects regression in R?

Heston and Rouwenhorst (1994) devised an empirical estimation strategy to decompose stock returns into three components: a pure industry effect, a pure country effect, and a world-factor return. ...
1
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0answers
33 views

How to set up Heston and Rouwenhorst regression? [duplicate]

Heston and Rouwenhorst (1994) devised an empirical estimation strategy to decompose stock returns into three components: a pure industry effect, a pure country effect, and a world-factor return. ...
1
vote
1answer
364 views

Proxy for Expected Economic Growth

Can anyone help me understand how expected economic growth is usually measured? I've read several papers that talk about using breakeven inflation as a proxy for expected inflation, and then the ...
4
votes
0answers
492 views

How to determine ratios for mean-reverting basket

Suppose I have a basket of 3 securities A, B, and C. I believe that the basket is cointegrated and I want to create a mean-reverting trade. I fit the model: ...
0
votes
2answers
466 views

Regression with Lagged variables

I am new to regression analysis. Let's say initially I have a linear regression x = alag(x1) + blag(x2) + clag(x3) -- eq 1 I want to predict the price x based on the the price of x from previous ...
2
votes
3answers
408 views

Why do long-term equity return forecast models use dependent observations?

I've been reading up on different models used to forecast the equity risk premium, and I've seen a couple of papers that had questionable methods. For example, this paper by Javier Estrada goes into ...
5
votes
2answers
187 views

How does the number of free dimensions of a model affect its required size of sample?

Adding more variables to a model usually increases its accuracy. However, without adequate analysis it could also lead to curve fitting. Another question (How much data is needed to validate a ...
9
votes
1answer
219 views

Regression in liquidity risk model of Jarrow/Protter

In the paper "Liquidity Risk and Risk Measure Computation" authors describe a linear supply curve model for liquidity risks in presence of market impact, i.e. impact-affected asset price $S(t,x)$ is ...
5
votes
1answer
2k views

How to use Newey West covariance corrector?

I have implemented the following model: daily_vol(t+1) = A*daily_vol(t) + B*weekly_vol(t) + C*monthly_vol(t) + error where vol means volatility, and A, B, C are ...
1
vote
1answer
231 views

Regression giving the return on a stock

I have this regression equation: $$ R_{stock} = 3,28\% + 1,65*R_{market} $$ Where $R_{stock}$ is the expected return on a stock and $R_{market}$ being the market risk premium. I have a one-year ...
2
votes
1answer
876 views

Multiple (linear) regression

I am looking for some inputs on a pair trading strategy that I am trying to improve with some semi-fundamental input. The basic idea is to use multiple linear regression to estimate the price of a ...
1
vote
0answers
112 views

What to do with linear regression or regression splines outside of the training range?

This is a cross-post from here In my question on a load forecast model using temperature data as covariates I was advised to use regression splines. This really seems to be a/the solution. Now I ...
2
votes
3answers
567 views

What data transformations to use in regression of credit spreads on equity prices?

Clearly there is a strong relationship between credit spreads and equity prices (both theoretically and empirically). But how would one go about formulating a regression which seeks to explain this ...
5
votes
4answers
616 views

Regressor: Nominal return, continuous return or first difference?

Suppose the application is linear models in financial econometrics. If we want to analyze stocks, the standard approach is to take the continuous/log return: $\ln{ \frac{P_t}{P_{t-1}} }$. Suppose, ...
-1
votes
1answer
109 views

Regression extensions

I'm trying to find extensions for my regression and obviously would like to use PE, BV and CFO. But I've got monthly data, while all company's fundamentals are semi-annually... Can I deal with it ...
6
votes
2answers
537 views

Why are regressors squared and not ^1.5 or ^2.2 or ^2.5?

When a researcher in economics or finance wants to apply a linear regression model but suspects a non-linear relationship between one of the regressors and the dependent variable, it is typical to ...
1
vote
1answer
667 views

How to compute portfolio weights from multivariate regression results?

Assuming that I performed a multivariate regression and I found a set of $k$ coefficients $\alpha_1, ..., \alpha_k$ for each of the factors $F_1, ... F_k$. I have then computed the following ...
6
votes
4answers
1k views

Using rolling returns in a multivariate linear regression?

I am trying to use fundamental factors such as PE, BV, & CFO in a multivariate linear regression with the response variable being the rolling 1 month returns. But this approach seems flawed as the ...
-3
votes
1answer
483 views

Trading Strategies and Portfolio Constructions based on Cross Sectional Regression? [closed]

I often see trading strategies and portfolio construction that are based on cross-sectional regression. For example, I often see regressing some numbers against some factors. I was wondering how ...
8
votes
1answer
1k views

What are the steps to perform properly a risk factor analysis on a portfolio?

I have been asked to perform a factor analysis on a given portfolio, assume it's a Swiss portfolio in CHF. First step, I chose which factors I would like to see in my analysis. The first factors I ...
-4
votes
1answer
665 views

How to calculate the weight of the stocks using the linear regression?

I do a simple example with the follow three series(stocks prices): ...
6
votes
1answer
689 views

How to run an asset replication regression?

I am doing extensive research on portfolio replication and was hoping to get some help with some problems I am encountering. I am running a regression between 2 assets that I believe replicate ...
7
votes
3answers
319 views

How to improve the consistency of explained variance statistics in a linear equity model?

I have an intraday equity returns linear model that, overall, shows good values in terms of $R^2$, p-value and other explained variance statistics. Around 70% of the stocks show consistent R-squared ...
3
votes
1answer
418 views

Linear regression and assets direction prediction

I have the following asset returns Y and the predictions for the same periods Y': Y = { 10, 200, -1000, -1, -7 } Y' = { 1, 2, -3, -4, -5 } The OLR R-squared for ...
3
votes
0answers
337 views

How to balance two Forex crosses correctly to do a linear regression?

I have two cross and an account in EUR: EUR/USD GBP/USD I would like to do a balanced linear regression using R. With "balanced" I mean that I would like to normalize it by calculating the ...
12
votes
1answer
3k views

Which approach to estimating fundamental factor models is better, cross-sectional (unobservable) factors or time-series (observable) factors?

There are many approaches to estimating fundamental factor equity models. I would like to focus on two traditional methods: The time-series regression approach of Fama and French. Factors are ...
7
votes
1answer
901 views

Expected return from a multiple linear regression?

How can I compute the predicted return from a linear regression that includes a number of different terms. For instance, suppose my equation is: $r_{future} = \alpha + \beta_1 r_{history} + \beta_2 ...
6
votes
2answers
436 views

Efficiency vs. Robustness - To use a constant or not in single factor time-series regression?

Arbitrage pricing theory states that expected returns for a security are linear combination of exposures to risk factors and the returns on these risk factors. Betas, or the exposures of the security ...
10
votes
5answers
5k views

Using linear regression on (lagged) returns of one stock to predict returns of another

Suppose I want to build a linear regression to see if returns of one stock can predict returns of another. For example, let's say I want to see if the VIX return on day X is predictive of the S&P ...
8
votes
4answers
2k views

How to perform risk factor calculation?

I am studying Arbitrage Pricing Theory (APT) and I have a question about calculating factor exposures. Assume: \begin{equation} r = \beta_1r_1 + \beta_2r_2 + ... + \beta_kr_k + r_e \end{equation} ...
7
votes
1answer
4k views

Time Series Regression with Overlapping Data

I am seeing a regression model which is regressing Year-on-Year stock index returns on lagged (12 months) Year-on-Year returns of the same stock index, credit spread (difference between monthly mean ...
7
votes
1answer
2k views

How do I reproduce the cross-sectional regression in “Intraday Patterns in the Cross-section of Stock Returns”?

Recently I was trying to reproduce the results of "Intraday Patterns in the Cross-section of Stock Returns". The authors used cross-sectional regression to determine which intraday lags have ...
3
votes
1answer
1k views

What is a persistent variable?

What is a persistent variable in the context of regression analysis? For example, dividend to price ratio (D/P) is considered to be persistent variable when used to model future returns (Stambaugh ...