Kategorien
Finance Research

Option valuation using Black-Scholes

Financial options have an intrinsic and a time value. The intrinsic value for a call option is simply the spot (S) minus the strike price (X). The time value of the call option can be derived using the Black-Scholes formula. The resulting price of the option minus the intrinsic value of the option results in the time value of the option.

The following graph illustrates the intrinsic value (red line), the price of the option (grey line) and the time value of the option (dark grey area).

Using the following code you can replicate the figure:

spot <- seq(1,100,by=1)
strike <- 50
riskfree <- 0
time <- 1
standarddev <- 0.2

d1 <- (log(spot/strike)+(0+standarddev^2/2)*1)/(standarddev*time)
d2 <- d1-standarddev*time^(1/2)

value.call <- pnorm(d1,0,1)*spot-pnorm(d2,0,1)*strike*exp(-riskfree*time)

inner.value <- spot-strike
inner.value <- pmax(inner.value,0)

require(ggplot2)

ggplot()+
geom_line(aes(spot,value.call))+
geom_line(aes(spot,inner.value),colour="red")+ geom_ribbon(aes(spot,ymin=value.call,ymax=inner.value),fill="darkgrey")

Kategorien
Finance Research

Efficient frontier in Finance

In every finance class, one of the first topics students are confronted with is the efficient frontier. Even though the intuition behind the efficient frontier might be easy to grasp, I show you that it can also be very easy to derive the efficient frontier – even using multiple assets.

Using the following code you can replicate the image:


stocks <- c("TSLA","AAPL", "FB")

require(PerformanceAnalytics)
require(quantmod)

getSymbols(stocks)

x<- dailyReturn(TSLA)
y <- dailyReturn(AAPL)
z <- dailyReturn(FB)


g <- as.data.frame(cbind(x,y,z))
a <-as.data.frame(cov(g[2000:2620,]))


library(data.table)
library(scales)
library(ggplot2)

link <- "https://raw.githubusercontent.com/DavZim/Efficient_Frontier/master/data/fin_data.csv"
dt <- data.table(read.csv(link))
dt[, date := as.Date(date)]

# create indexed values
dt[, idx_price := price/price[1], by = ticker]

# plot the indexed values
ggplot(dt, aes(x = date, y = idx_price, color = ticker)) +
geom_line() +
# Miscellaneous Formatting
theme_bw() + ggtitle("Price Developments") +
xlab("Date") + ylab("Pricen(Indexed 2000 = 1)") +
scale_color_discrete(name = "Company")


# calculate the arithmetic returns
dt[, ret := price / shift(price, 1) - 1, by = ticker]

# summary table
# take only non-na values
tab <- dt[!is.na(ret), .(ticker, ret)]

# calculate the expected returns (historical mean of returns) and volatility (standard deviation of returns)
tab <- tab[, .(er = round(mean(ret), 4),
sd = round(sd(ret), 4)),
by = "ticker"]


ggplot(tab, aes(x = sd, y = er, color = ticker)) +
geom_point(size = 5) +
# Miscellaneous Formatting
theme_bw() + ggtitle("Risk-Return Tradeoff") +
xlab("Volatility") + ylab("Expected Returns") +
scale_y_continuous(label = percent, limits = c(0, 0.03)) +
scale_x_continuous(label = percent, limits = c(0, 0.1))

link <- "https://raw.githubusercontent.com/DavZim/Efficient_Frontier/master/data/mult_assets.csv"
df <- data.table(read.csv(link))

# calculate the necessary values:
# I) expected returns for the two assets
er_x <- mean(df$x)
er_y <- mean(df$y)

# II) risk (standard deviation) as a risk measure
sd_x <- sd(df$x)
sd_y <- sd(df$y)

# III) covariance
cov_xy <- cov(df$x, df$y)

# create 1000 portfolio weights (omegas)
x_weights <- seq(from = 0, to = 1, length.out = 1000)

# create a data.table that contains the weights for the two assets
two_assets <- data.table(wx = x_weights,
wy = 1 - x_weights)

# calculate the expected returns and standard deviations for the 1000 possible portfolios
two_assets[, ':=' (er_p = wx * er_x + wy * er_y,
sd_p = sqrt(wx^2 * sd_x^2 +
wy^2 * sd_y^2 +
2 * wx * (1 - wx) * cov_xy))]

# lastly plot the values
ggplot() +
geom_point(data = two_assets, aes(x = sd_p, y = er_p, color = wx)) +
geom_point(data = data.table(sd = c(sd_x, sd_y), mean = c(er_x, er_y)),
aes(x = sd, y = mean), color = "red", size = 3, shape = 18) +
# Miscellaneous Formatting
theme_bw() + ggtitle("Possible Portfolios with Two Risky Assets") +
xlab("Volatility") + ylab("Expected Returns") +
scale_y_continuous(label = percent, limits = c(0, max(two_assets$er_p) * 1.2)) +
scale_x_continuous(label = percent, limits = c(0, max(two_assets$sd_p) * 1.2)) +
scale_color_continuous(name = expression(omega[x]), labels = percent)

##### Three assets ###################

# load the data
link <- "https://raw.githubusercontent.com/DavZim/Efficient_Frontier/master/data/mult_assets.csv"
df <- data.table(read.csv(link))

# calculate the necessary values:
# I) expected returns for the two assets
er_x <- mean(df$x)
er_y <- mean(df$y)
er_z <- mean(df$z)

# II) risk (standard deviation) as a risk measure
sd_x <- sd(df$x)
sd_y <- sd(df$y)
sd_z <- sd(df$z)

# III) covariance
cov_xy <- cov(df$x, df$y)
cov_xz <- cov(df$x, df$z)
cov_yz <- cov(df$y, df$z)

# create portfolio weights (omegas)
x_weights <- seq(from = 0, to = 1, length.out = 1000)

# create a data.table that contains the weights for the three assets
three_assets <- data.table(wx = rep(x_weights, each = length(x_weights)),
wy = rep(x_weights, length(x_weights)))

three_assets[, wz := 1 - wx - wy]


# calculate the expected returns and standard deviations for the 1000 possible portfolios
three_assets[, ':=' (er_p = wx * er_x + wy * er_y + wz * er_z,
sd_p = sqrt(wx^2 * sd_x^2 +
wy^2 * sd_y^2 +
wz^2 * sd_z^2 +
2 * wx * wy * cov_xy +
2 * wx * wz * cov_xz +
2 * wy * wz * cov_yz))]

# take out cases where we have negative weights (shortselling)
three_assets <- three_assets[wx >= 0 & wy >= 0 & wz >= 0]
three_assets

# lastly plot the values
ggplot() +
geom_point(data = three_assets, aes(x = sd_p, y = er_p, color = wx - wz)) +
geom_point(data = data.table(sd = c(sd_x, sd_y, sd_z), mean = c(er_x, er_y, er_z)),
aes(x = sd, y = mean), color = "red", size = 3, shape = 18) +
# Miscellaneous Formatting
theme_bw() + ggtitle("Possible Portfolios with Three Risky Assets") +
xlab("Volatility") + ylab("Expected Returns") +
scale_y_continuous(label = percent, limits = c(0, max(three_assets$er_p) * 1.2)) +
scale_x_continuous(label = percent, limits = c(0, max(three_assets$sd_p) * 1.2)) +
scale_color_gradientn(colors = c("red", "blue", "yellow"),
name = expression(omega[x] - omega[z]), labels = percent)

Kategorien
Finance Research

Create your own badman logo in R

This was a fun project to replicate the badman logo using a mathematical function (badman function).

Using the following code you can replicate the image:

require(ggplot2)
require(dplyr)

f1 <- function(x) {
y1 <- 3*sqrt(1-(x/7)^2)
y2 <- -3*sqrt(1-(x/7)^2)
y <- c(y1,y2)
d <- data.frame(x=x,y=y)
d <- d[d$y > -3*sqrt(33)/7,]
return(d)
}

x1 <- c(seq(3, 7, 0.001), seq(-7, -3, 0.001))
d1 <- f1(x1)
p1 <- ggplot(d1,aes(x,y)) + geom_point(color="green")

x2 <- seq(-4,4, 0.001)
y2 <- abs(x2/2)-(3*sqrt(33)-7)*x2^2/112-3 + sqrt(1-(abs(abs(x2)-2)-1)^2)

#only work with ggplot2 <= 0.8.9
#p2 <- p1 + geom_point(aes(x=x2,y=y2), color="yellow")

# in ggplot2 0.9.0, should be:
d2 <- data.frame(x2=x2, y2=y2)
p2 <- p1 + geom_point(data=d2, aes(x=x2,y=y2), color="green")


x3 <- c(seq(0.75,1,0.001), seq(-1,-0.75,0.001))
y3 <- 9-8*abs(x3)
#p3 <- p2+geom_point(aes(x=x3,y=y3), color="green")
d3 <- data.frame(x3=x3, y3=y3)
p3 <- p2+geom_point(data=d3, aes(x=x3,y=y3), color="green")


x4 <- c(seq(0.5,0.75,0.001), seq(-0.75,-0.5,0.001))
y4 <- 3*abs(x4)+0.75
#p4 <- p3+geom_point(aes(x=x4,y=y4), color="steelblue")
d4 <- data.frame(x4=x4,y4=y4)
p4 <- p3+geom_line(data=d4, aes(x=x4,y=y4), color="green")


x5 <- seq(-0.5,0.5,0.001)
y5 <- rep(2.25,length(x5))
#p5 <- p4+geom_point(aes(x=x5,y=y5))
d5 <- data.frame(x5=x5,y5=y5)
p5 <- p4+geom_line(data=d5, aes(x=x5,y=y5),color="green")

x6 <- c(seq(-3,-1,0.001), seq(1,3,0.001))
y6 <- 6 * sqrt(10)/7 +
(1.5 - 0.5 * abs(x6)) * sqrt(abs(abs(x6)-1)/(abs(x6)-1)) -
6 * sqrt(10) * sqrt(4-(abs(x6)-1)^2)/14
#p6 <- p5+geom_point(aes(x=x6,y=y6), colour="blue")
d6 <- data.frame(x6=x6,y6=y6)
p6 <- p5+geom_line(data=d6,aes(x=x6,y=y6), colour="green")

p <- p6

theme_black<- function (base_size = 16, base_family = ""){
theme_minimal() %+replace%
theme( line = element_line(colour = "black", size = 0.5, linetype = 1,
lineend = "butt"),
rect = element_rect(fill = "black",
colour = "black", size = 0.5, linetype = 1),
plot.background = element_rect(colour = 'black', fill = 'black'),
plot.title = element_text(size = rel(1.2)),
panel.border = element_rect(fill = NA, colour = "black"),axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank()
)
}


p+theme_black()

Kategorien
Finance Research

Heteroskedasticity-robust standard errors in R

Writing a financial paper is often associated with an OLS regression model. One major issue can be heteroskedasticity: the variance of the error terms vary. I show you how you can detect heteroskedasticity and how to implement robust standard errors in R.

Step 1: Implement a regression model in R

model1 <- lm(dist ~ speed, data=cars) # initial model using car data

Step 2: Detect heteroskedasticity using the Breush Pagan Test. When the p-value is below the 10% we can reject the null hypothesis that the variance of the residuals is constant. 

require(lmtest)  # load the package
require(vars)

The p-value is below the critical value of 10%.

lmtest::bptest(model1)
	studentized Breusch-Pagan test

data: model1
BP = 3.2149, df = 1, p-value = 0.07297

Step 3: Use Newey-West to correct the standard errors

model2 <- lm(dist~speed,data=cars)
model2$coefficients <- unclass(coeftest(model2, vcov. = NeweyWest))

Step 4: Compare the results

summary(model1)
Call:
lm(formula = dist ~ speed, data = cars)

Residuals:
Min 1Q Median 3Q Max
-29.069 -9.525 -2.272 9.215 43.201

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -17.5791 6.7584 -2.601 0.0123 *
speed 3.9324 0.4155 9.464 1.49e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.38 on 48 degrees of freedom
Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438
F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12
model2
Call:
lm(formula = dist ~ speed, data = cars)

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.758e+01 7.018e+00 -2.505e+00 1.570e-02
speed 3.932e+00 5.509e-01 7.138e+00 4.526e-09

Notice that the estimated coefficients are the same but the standard errors of the two models differ! Hence, this affects the statistical significance.