Value at Risk Modelling for Energy Commodities using Volatility Adjusted Quantile Regression
Regression Modelling Overview
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![Page 1: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/1.jpg)
Why regression
Today’s Lecture:
Overview of Regression (1:Motivating Examples)
![Page 2: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/2.jpg)
Why regression
Wine Quality
In “Super Crunchers”, Ian Ayres gives a formula for wine quality as:
Wine quality = 12.145 + 0.00117 × winter rainfall +
0.0614 × average growing season temperature −0.00386 × harvest rainfall
What is this formula telling us?
![Page 3: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/3.jpg)
Why regression
Motivating examples
1 The relationship between restaurant characteristics andlocation, and the prices charged
2 The relationship between wine price and critic ratings
3 The relationship between various “risk factors” and theoccurence of heart disease
4 One more example I haven’t decided on yet (and isn’ttherefore in the notes yet - suggestions welcome!)
5 Later we will examine the relationship between variouscharacteristics of golfers and the money they earn
Along the way, we will consider “smaller” datasets to illustratespecific points.
![Page 4: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/4.jpg)
Why regression
Motivating examples 1
New York Restaurants
1 This is intended to give an example of where we want to beby the end of Term 1 - so you have an idea of what we arelearning and why.
2 In other words, relax, sit back and get a feel for what you willbe able to do after we’ve spent a whole term studying it.
![Page 5: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/5.jpg)
Why regression
Motivating examples 1
Zagat Price Guide
Example (Manhattan Restaurant Pricing)
1 Sheather [2009] suggests you have been retained to advise achef on Menu pricing for a new Italian restaurant inManhattan
2 He provides data from “Zagat Survey 2001: New York CityRestaurants, Zagat, New York”
3 Given a model, you can predict the effect of various restaurantcharacteristics on the kind of price you can charge
4 Specifically, we could try to decide whether you can chargemore for a restaurant that is “East” of the river. This isdenoted by an “indicator” variable which takes on the value 1for a restaurant East of the river, and 0 otherwise
![Page 6: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/6.jpg)
Why regression
Motivating examples 1
Loading the data
> nyc.df <- read.csv("data/nyc.csv")
> summary(nyc.df)
Case Restaurant Price
Min. : 1.00 Amarone : 1 Min. :19.0
1st Qu.: 42.75 Anche Vivolo: 1 1st Qu.:36.0
Median : 84.50 Andiamo : 1 Median :43.0
Mean : 84.50 Arno : 1 Mean :42.7
3rd Qu.:126.25 Artusi : 1 3rd Qu.:50.0
Max. :168.00 Baci : 1 Max. :65.0
(Other) :162
Food Decor Service
Min. :16.0 Min. : 6.00 Min. :14.0
1st Qu.:19.0 1st Qu.:16.00 1st Qu.:18.0
Median :20.5 Median :18.00 Median :20.0
Mean :20.6 Mean :17.69 Mean :19.4
3rd Qu.:22.0 3rd Qu.:19.00 3rd Qu.:21.0
Max. :25.0 Max. :25.00 Max. :24.0
East
Min. :0.000
1st Qu.:0.000
Median :1.000
Mean :0.631
3rd Qu.:1.000
Max. :1.000
> pairs(nyc.df[, c(3:6)], main = "Pairs plot for Zagat price data")
![Page 7: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/7.jpg)
Why regression
Motivating examples 1
The data
Price
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Decor
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2025
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20 30 40 50 60
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10 15 20 25
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Service
Pairs plot for Zagat price data
![Page 8: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/8.jpg)
Why regression
Motivating examples 1
Loading the data
One of our predictor variables is not continuous. It is aqualitative/categorical/nominal/factor with one level variablewhich we use as an indicator/dummy variable such that it is equalto 1 if the restaurant is East of the river, and 0 otherwise. We canbest examine the relationship between this variable and the Priceby means of a boxplot.
> boxplot(Price ~ East, data = nyc.df, col = "orange",
main = "Effect of East on price", ylab = "Price",
xlab = "East")
![Page 9: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/9.jpg)
Why regression
Motivating examples 1
The boxplot
0 1
2030
4050
60
Effect of East on price
East
Pric
e
![Page 10: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/10.jpg)
Why regression
Motivating examples 1
Fitting a model
> nyc.lm1 <- lm(Price ~ Food + Decor + Service +
East, data = nyc.df)
> summary(nyc.lm1)
Call:
lm(formula = Price ~ Food + Decor + Service + East, data = nyc.df)
Residuals:
Min 1Q Median 3Q Max
-14.0465 -3.8837 0.0373 3.3942 17.7491
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -24.023800 4.708359 -5.102 9.24e-07
Food 1.538120 0.368951 4.169 4.96e-05
Decor 1.910087 0.217005 8.802 1.87e-15
Service -0.002727 0.396232 -0.007 0.9945
East 2.068050 0.946739 2.184 0.0304
Residual standard error: 5.738 on 163 degrees of freedom
Multiple R-squared: 0.6279, Adjusted R-squared: 0.6187
F-statistic: 68.76 on 4 and 163 DF, p-value: < 2.2e-16
![Page 11: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/11.jpg)
Why regression
Motivating examples 1
The model
Price = −24.02 + 1.54xFood + 1.91xDecor + 0xService + 2.07xEast + εi
We can see that the higher the values of xFood , the higher the pricecharged. We can also see that for every unit increase in xFood , thevalue of yPrice increases by 1.54.There doesn’t appear to be a relationship between Service andPrice. This is rather interesting, as it implies you could employBasil Fawlty to look after all the diners.
![Page 12: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/12.jpg)
Why regression
Motivating examples 1
What to do about Service
The most important thing to note for now is that these values areonly estimates! We will study inference more formally, but for nowwe shall use a
Key Point: Rule of two??????
If the absolute value of an estimate divided by its standard erroris less than 2, we can’t even be sure what sign the estimateshould have
![Page 13: Regression Modelling Overview](https://reader033.fdocuments.us/reader033/viewer/2022052905/55853643d8b42a86388b529b/html5/thumbnails/13.jpg)
Why regression
Motivating examples 1
Modifying the model
Some people might remove this variable from our model.> nyc.lm2 <- update(nyc.lm1, Price ~ . - Service)
> summary(nyc.lm2)
Call:
lm(formula = Price ~ Food + Decor + East, data = nyc.df)
Residuals:
Min 1Q Median 3Q Max
-14.0451 -3.8809 0.0389 3.3918 17.7557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -24.0269 4.6727 -5.142 7.67e-07
Food 1.5363 0.2632 5.838 2.76e-08
Decor 1.9094 0.1900 10.049 < 2e-16
East 2.0670 0.9318 2.218 0.0279
Residual standard error: 5.72 on 164 degrees of freedom
Multiple R-squared: 0.6279, Adjusted R-squared: 0.6211
F-statistic: 92.24 on 3 and 164 DF, p-value: < 2.2e-16
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Why regression
Motivating examples 1
Can you look at the adjusted R2
Key Point: Adjusted R2
?????????
The R2 is a diagnostic (values between 0 and 1) which tells usthe“proportion of variation in Y explained by our model. Theadjusted R2 incorporates a penalty to account for the numberof variables we have used.
What do you notice about the adjusted R-squared?
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Why regression
Motivating examples 1
We could also compare the two models:
Model 1:
Price = −24.02 + 1.54xFood + 1.91xDecor + 0xService + 2.07xEast + εi
Model 2:
Price = −24.03 + 1.54xFood + 1.91xDecor + 2.07xEast + εi
So it looks as if we could indeed suggest prices to charge, based onratings of the other variables.
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Why regression
Motivating examples 1
Residual checking
As well as fitting models, we have to make sure they are sensible.This involves checking all the assumptions we made when fittingthe model. Again, we haven’t said anything about the assumptionsyet. But just to introduce the ideas let’s check two
1 Check that the residuals are Normally distributed
2 Check that the residuals have constant variance
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Why regression
Motivating examples 1
Checking the Normality of the residuals
One method we could use it to examine a histogram of theresiduals:
> hist(resid(nyc.lm2), freq = FALSE)
> curve(dnorm(x, 0, summary(nyc.lm2)$sigma),
add = TRUE, col = "red")
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Histogram of resid(nyc.lm2)
resid(nyc.lm2)
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Figure: Histogram of residuals from model fit, normal modelsuperimposed
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Motivating examples 1
Checking the constant variance assumption
We will use some kind of plot of residuals against the fitted values(the points on the regression line corresponding to individuals inthe dataset). We start with a simple plot of residuals against fittedvalues.
> plot(fitted(nyc.lm2) ~ resid(nyc.lm2))
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Figure: Plot of fitted values versus residuals
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Motivating examples 1
Assumption checking
We could conclude that (a) the residuals appear Normal and (b)the variance appears constant.As we are happy with our model, we can answer the mostsubtantive question. Having a restaurant East of the river seems toadd $2.07 to the price you can charge for a meal.
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Why regression
Motivating examples 1
Summary of what we have done
1 We have specified a problem and collected some data
2 We have carried out an exploratory data analysis
3 We have fitted an appropriate model to the data
4 We have checked the assumptions made when fitting thatmodel
5 We have made some adjustments to the model
6 We have attempted to draw some conclusions
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Motivating examples 1
What we need to do
1 Think about the kinds of problems we can examine byregression modelling
2 Learn (revise and extend what you did in STAT1401) how tocarry out an exploratory data analysis
3 Learn about the types of models we can build, and theassumptions we make when building them
4 Learn more about how to check the model assumptions, andunderstand some of the problems when they not met
5 Learn more about how to alter the structure of a model, inparticular how to decide in observational studies whichvariables to include and exclude
6 How to interpret the results of model fitting and, whenappropriate, how to carry out statistical inference on theresults
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How we can assess this
1 Ask you to discuss the reasons for a particular study, how wedeal with the different variables (exam)
2 Ask you to carry out eda (coursework), or comment on an edathat has been carried out (exam)
3 Fit (coursework) an appropriate model for a particular dataset.Discuss and explain the principles behind various models(exam)
4 Carry out residual checks and make adjustments to a model(coursework), comment on residual checks / explain whyadjustments have been made (exam)
5 Carry out and report a model building exercise (coursework),explain someone else’s model building (exam)
6 Interpret the results of your own (coursework) or someoneelse’s model fitting (exam)
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Why regression
Motivating examples 1
R.D. Cook and S. Weisberg. Applied Regression IncludingComputing and Graphics. John Wiley, Hoboken NJ, 1999.
Simon Sheather. A Modern Approach to Regression with R.Springer Texts in Statistics. Sheather. Springer Verlag, NewYork, 2009.