Simulated portfolio & benchmark for purposes of testing basic features of univariate regression

20.16.2. Peter is an analyst who is evaluating an investment fund whose managers claim has outperformed their benchmark. He collected monthly returns for the last five years; i.e., the sample size is excess return pairs over n = 60 months. He plots excess returns, which are defined as the returns in excess of the riskfree rate; ie., an excess return equals the gross return minus the riskfree rate. The scatterplot is displayed below:

*scatterplot*

The correlation coefficient is 0.708. In regard to the univariate data, the standard deviation of the portfolio’s returns is 22.84% and the standard deviation of the benchmark’s returns is 9.79%. The average excess return of the benchmark was -0.37% and the average excess return of the portfolio was 2.61%. Each of the following statements is true EXCEPT which is false?

- The slope of the regression line is approximately 1.65 and the intercept is approximately 3.22%
- Visual inspection confirms the error variance is not constant and we can, therefore, assert the presence of heteroskedastic shocks
- This regression line passes through the coordinates of averages, (μ_x, μ_y) = (-0.37%, +2.61%), although this is not an actual pairwise observation
- This model appears to at least meet the three essential restrictions of a linear regression model including linearity in the coefficients (aka, parameters)

```
library(tidyverse)
library(scales)
library(ggthemes)
# library(lmtest)
x_mu <- .01; x_sig <- .1
y_mu <- .03; y_sig <- .2
rho <- 0.72
months <- 60
#set.seed(59)
set.seed(158)
# 60 rows of random standard normals
returns <- tibble(index = 1:months)
returns$x1 <- rnorm(months)
returns$y1 <- rnorm(months)
# make y2 correlated with y1; adjust location/scale
returns1 <- returns %>% mutate(
y2 = rho*x1 + sqrt(1 - rho^2)*y1,
r_x = x_mu + x1 * x_sig,
r_y = y_mu + y2 * y_sig
)
x_sd <- sd(returns1$r_x)
y_sd <- sd(returns1$r_y)
rho_xy <- cor(returns1$r_x, returns1$r_y)
beta_yx <- rho_xy * y_sd / x_sd
x_mu_act <- mean(returns1$r_x)
y_mu_act <- mean(returns1$r_y)
sprintf("sample rho is %.4f. The standard deviation of Portfolio returns is ", rho_xy)
```

`[1] "sample rho is 0.7078. The standard deviation of Portfolio returns is "`

`[1] "Portfolio standard deviation is 22.84%"`

`[1] "Benchmark standard deviation is 9.79%"`

`[1] "Portfolio average excess return is 2.61%"`

`[1] "Benchmark average excess return is -0.37%"`

```
sprintf("Beta(P,B) is %.3f", beta_yx)
```

`[1] "Beta(P,B) is 1.652"`

```
Call:
lm(formula = r_y ~ r_x, data = returns1)
Residuals:
Min 1Q Median 3Q Max
-0.30810 -0.11231 -0.01385 0.09922 0.42134
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03231 0.02102 1.537 0.13
r_x 1.65219 0.21649 7.632 2.54e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1627 on 58 degrees of freedom
Multiple R-squared: 0.501, Adjusted R-squared: 0.4924
F-statistic: 58.24 on 1 and 58 DF, p-value: 2.543e-10
```

```
returns1 %>% ggplot(aes(x = r_x, y = r_y)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "forestgreen", linetype = "longdash", size = 1.5) +
ggtitle("Investment fund versus benchmark: Excess returns, n = 60 months") +
xlab("Benchmark's excess returns") +
ylab("Portfolio's excess returns") +
scale_x_continuous(labels = percent_format(accuracy = 1)) +
scale_y_continuous(labels = percent_format(accuracy = 1)) +
theme_light() +
theme(
axis.title.y = element_text(size = 14),
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 14, margin = margin(b = 10)),
axis.text.y = element_text(size = 14, margin = margin(l = 10))
)
```

```
plot(returns1_lm)
```

```
new.df <- data.frame(r_x = c(x_mu_act, 0, seq(from = 0.01, to = 0.1, by = .01)))
new.df$predicted_y <- predict(returns1_lm, new.df)
new.df
```

```
r_x predicted_y
1 -0.003740684 0.02613168
2 0.000000000 0.03231199
3 0.010000000 0.04883388
4 0.020000000 0.06535577
5 0.030000000 0.08187766
6 0.040000000 0.09839955
7 0.050000000 0.11492143
8 0.060000000 0.13144332
9 0.070000000 0.14796521
10 0.080000000 0.16448710
11 0.090000000 0.18100899
12 0.100000000 0.19753088
```

```
intercept_predict <- (0 - x_mu_act)*beta_yx + y_mu_act
intercept_predict_round <- (0 - round(x_mu_act,4))*beta_yx + round(y_mu_act,4)
intercept_predict
```

`[1] 0.03231199`

```
intercept_predict_round
```

`[1] 0.0322131`