Package 'etrm' reference manual

Title:	Energy Trading and Risk Management
Description:	Provides a collection of functions to perform core tasks within Energy Trading and Risk Management (ETRM). Calculation of maximum smoothness forward price curves for electricity and natural gas contracts with flow delivery, as presented in F. E. Benth, S. Koekebakker, and F. Ollmar (2007) <doi:10.3905/jod.2007.694791> and F. E. Benth, J. S. Benth, and S. Koekebakker (2008) <doi:10.1142/6811>. Portfolio insurance trading strategies for price risk management in the forward market, see F. Black (1976) <doi:10.1016/0304-405X(76)90024-6>, T. Bjork (2009) <https://EconPapers.repec.org/RePEc:oxp:obooks:9780199574742>, F. Black and R. W. Jones (1987) <doi:10.3905/jpm.1987.409131> and H. E. Leland (1980) <http://www.jstor.org/stable/2327419>.
Authors:	Anders D. Sleire
Maintainer:	Anders D. Sleire <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.2
Built:	2025-02-09 03:44:38 UTC
Source:	https://github.com/sleire/etrm

etrm: Energy Trading and Risk Management

Description

Tools for energy market risk management (forward curves and trading strategies)

Author(s)

References

F. E. Benth, S. Koekkebakker, and F. Ollmar. Extracting and applying smooth forward curves from average-based commodity contracts with seasonal variation.The Journal of Derivatives, 15(1):52–66,2007b. https://doi.org/10.3905/jod.2007.694791

F. E. Benth, J. S. Benth, and S. Koekebakker. Stochastic modelling of electricity and related markets,volume 11. World Scientific, 2008. https://doi.org/10.1142/6811

F. Black. The pricing of commodity contracts.Journal of financial economics, 3(1):167–179, 1976. https://doi.org/10.1016/0304-405X(76)90024-6

T. Bjork. Arbitrage Theory in Continuous Time. Oxford University Press, 3 edition, 2009. https://EconPapers.repec.org/RePEc:oxp:obooks:9780199574742

F. Black and R. W. Jones. Simplifying portfolio insurance. The Journal of Portfolio Management, 14(1):48–51, 1987. https://doi.org/10.3905/jpm.1987.409131

H. E. Leland. Who should buy portfolio insurance? The Journal of Finance, 35(2):581–594, 1980. http://www.jstor.org/stable/2327419

Constant Proportion Portfolio Insurance (CPPI)

Description

Implements CPPI strategy for commodity price risk management

Usage

cppi(q, tdate, f, tper, rper, tcost = 0, int = TRUE)
cppi(q, tdate, f, tper, rper, tcost = 0, int = TRUE)

Arguments

`q`	numeric value for quantity to be hedged, either positive (net buyer) or negative (net seller)
`tdate`	date vector with trading days
`f`	numeric futures price vector
`tper`	numeric target price markup/down to the price on the first trading day
`rper`	numeric risk factor as a percentage of the price on the first trading day
`tcost`	numeric transaction costs pr unit
`int`	TRUE/FALSE integer restriction on tradable volume

Value

instance of the CPPI class

Examples

# CPPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

# implement cppi strategy for buyer
cppi_b <- cppi(q = 10,
tdate = tr_dates,
f = f_gbm,
tper = 0.1,
rper = 0.1,
tcost = 0,
int = TRUE)

# implement cppi strategy for seller
cppi_s <- cppi(q = - 10,
tdate = tr_dates,
f = f_gbm,
tper = - 0.1,
rper = 0.1,
tcost = 0,
int = TRUE)

# CPPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

# implement cppi strategy for buyer
cppi_b <- cppi(q = 10,
tdate = tr_dates,
f = f_gbm,
tper = 0.1,
rper = 0.1,
tcost = 0,
int = TRUE)

# implement cppi strategy for seller
cppi_s <- cppi(q = - 10,
tdate = tr_dates,
f = f_gbm,
tper = - 0.1,
rper = 0.1,
tcost = 0,
int = TRUE)

An S4 class for the CPPI hedging strategy

Description

An S4 class for the CPPI hedging strategy

Slots

RiskFactor: The risk factor (cushion) used in the CPPI model

Dynamic Proportion Portfolio Insurance (DPPI)

Description

Implements DPPI strategy for commodity price risk management

Usage

dppi(q, tdate, f, tper, rper, tcost = 0, int = TRUE)
dppi(q, tdate, f, tper, rper, tcost = 0, int = TRUE)

Arguments

`q`	numeric value for quantity to be hedged, either positive (net buyer) or negative (net seller)
`tdate`	date vector with trading days
`f`	numeric futures price vector
`tper`	numeric target price factor, markup/down to the price on the first trading day
`rper`	numeric risk factor as a percentage of the price on the first trading day
`tcost`	numeric transaction costs pr unit
`int`	TRUE/FALSE integer restriction on tradable volume

Value

instance of the DPPI class

Examples

# DPPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

# implement dppi strategy for buyer
dppi_b <- dppi(q = 10,
tdate = tr_dates,
f = f_gbm,
tper = 0.1,
rper = 0.1,
tcost = 0,
int = TRUE)

# implement dppi strategy for seller
dppi_s <- dppi(q = - 10,
tdate = tr_dates,
f = f_gbm,
tper = - 0.1,
rper = 0.1,
tcost = 0,
int = TRUE)

# DPPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

# implement dppi strategy for buyer
dppi_b <- dppi(q = 10,
tdate = tr_dates,
f = f_gbm,
tper = 0.1,
rper = 0.1,
tcost = 0,
int = TRUE)

# implement dppi strategy for seller
dppi_s <- dppi(q = - 10,
tdate = tr_dates,
f = f_gbm,
tper = - 0.1,
rper = 0.1,
tcost = 0,
int = TRUE)

An S4 class for the DPPI hedging strategy

Description

An S4 class for the DPPI hedging strategy

Slots

TargetPercent: A percentage of first trading day's market price used to set target price (cap or floor)
RiskFactor: The risk factor (cushion) used in the DPPI model

An S4 VIRTUAL parent class for the hedging strategy classes in etrm

Description

An S4 VIRTUAL parent class for the hedging strategy classes in etrm

Slots

Name: A string with the portfolio insurance strategy name
Volume: The quantity to be hedged
TargetPrice: The target price(s) for the portfolio (cap or floor)
TransCost: Transaction costs pr unit traded
TradeisInt: TUE/FALSE integer restriction on tradable volume, TRUE sets smallest transacted unit to 1
Results: Data frame with strategy results, daily values for market price, transactions, exposure, position, hedge and portfolio price

Maximum Smoothness Forward Curve (MSFC)

Description

Creates a smooth forward curve from futures prices for a flow delivery

Usage

msfc(tdate, include, contract, sdate, edate, f, prior = 0)
msfc(tdate, include, contract, sdate, edate, f, prior = 0)

Arguments

`tdate`	trading date
`include`	logical vector to determine if contracts should be included in calculation
`contract`	vector with contract names
`sdate`	date vector with contract delivery start dates
`edate`	date vector with contract delivery end dates
`f`	numeric vector with futures contract prices
`prior`	numeric vector with prior forward price curve

Value

instance of the MSFC class

Examples

# calculate forward curve for synthetic futures contracts, without prior

# date for curve calculation and contract information
tdate <- as.Date("2021-06-17")
include <- rep(TRUE, 10)
contract <- c("JUL-21", "AUG-21", "SEP-21", "OCT-21", "NOV-21", "DEC-21",
"Q1-22", "Q2-22", "Q3-22", "Q4-22")

sdate <- as.Date(c("2021-07-01", "2021-08-01", "2021-09-01", "2021-10-01",
"2021-11-01", "2021-12-01", "2022-01-01", "2022-04-01", "2022-07-01", "2022-10-01"))

edate <- as.Date(c("2021-07-30", "2021-08-31", "2021-09-30", "2021-10-31",
"2021-11-30", "2021-12-31", "2022-03-31", "2022-06-30", "2022-09-30", "2022-12-31"))

f <- c(32.55, 32.50, 32.50, 32.08, 36.88, 39.80, 39.40, 25.20, 21.15, 29.50)

fwd_curve <- msfc(tdate = tdate,
include = include,
contract = contract,
sdate = sdate,
edate = edate,
f = f)
# calculate forward curve for synthetic futures contracts, without prior

# date for curve calculation and contract information
tdate <- as.Date("2021-06-17")
include <- rep(TRUE, 10)
contract <- c("JUL-21", "AUG-21", "SEP-21", "OCT-21", "NOV-21", "DEC-21",
"Q1-22", "Q2-22", "Q3-22", "Q4-22")

sdate <- as.Date(c("2021-07-01", "2021-08-01", "2021-09-01", "2021-10-01",
"2021-11-01", "2021-12-01", "2022-01-01", "2022-04-01", "2022-07-01", "2022-10-01"))

edate <- as.Date(c("2021-07-30", "2021-08-31", "2021-09-30", "2021-10-31",
"2021-11-30", "2021-12-31", "2022-03-31", "2022-06-30", "2022-09-30", "2022-12-31"))

f <- c(32.55, 32.50, 32.50, 32.08, 36.88, 39.80, 39.40, 25.20, 21.15, 29.50)

fwd_curve <- msfc(tdate = tdate,
include = include,
contract = contract,
sdate = sdate,
edate = edate,
f = f)

An S4 class for the Maximum Smoothness Forward Curve (MSFC) in etrm

Description

An S4 class for the Maximum Smoothness Forward Curve (MSFC) in etrm

Slots

Name: A string with the acronym for Maximum Smoothness Forward Curve, "MSFC"
TradeDate: The trading date
BenchSheet: A data frame with futures contracts selected for calculation with MSFC computed prices
Polynomials: The number of polynomials in the MSFC spline
PriorFunc: A numeric vector with the prior function values
Results: A data frame with daily values for the calculated MSFC and contracts in "BenchSheet"
SplineCoef: List with coefficients for the polynomials in the MSFC spline
KnotPoints: Vector with spline knot points
CalcDat: Data frame extending "Results" with daily values for time vectors and polynomial coefficients used in calculation

Option Based Portfolio Insurance (OBPI)

Description

Implements OBPI strategy for commodity price risk management

Usage

obpi(
  q,
  tdate,
  f,
  k = f[1],
  vol,
  r = 0,
  tdays = 250,
  daysleft,
  tcost = 0,
  int = TRUE
)
obpi(
  q,
  tdate,
  f,
  k = f[1],
  vol,
  r = 0,
  tdays = 250,
  daysleft,
  tcost = 0,
  int = TRUE
)

Arguments

`q`	numeric value for quantity to be hedged, either positive (net buyer) or negative (net seller)
`tdate`	date vector with trading days
`f`	numeric futures price vector
`k`	numeric value for option strike price
`vol`	value for volatility
`r`	value for interest rate
`tdays`	integer assumed number of trading days per year
`daysleft`	integer with days left to option expiry
`tcost`	numeric transaction costs pr unit
`int`	TRUE/ FALSE integer restriction on tradable volume

Value

instance of the OBPI class

Examples

# OBPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

#implement obpi strategy for buyer
obpi_b <- obpi(q = 10,
tdate = tr_dates,
f = f_gbm,
k = f_gbm[1],
vol = 0.2,
r =  0,
tdays = 250,
daysleft = length(f_gbm),
tcost = 0,
int = TRUE)

# implement obpi strategy for seller
obpi_s <- obpi(q = - 10,
tdate = tr_dates,
f = f_gbm,
k = f_gbm[1],
vol = 0.2,
r =  0,
tdays = 250,
daysleft = length(f_gbm),
tcost = 0,
int = TRUE)

# OBPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

#implement obpi strategy for buyer
obpi_b <- obpi(q = 10,
tdate = tr_dates,
f = f_gbm,
k = f_gbm[1],
vol = 0.2,
r =  0,
tdays = 250,
daysleft = length(f_gbm),
tcost = 0,
int = TRUE)

# implement obpi strategy for seller
obpi_s <- obpi(q = - 10,
tdate = tr_dates,
f = f_gbm,
k = f_gbm[1],
vol = 0.2,
r =  0,
tdays = 250,
daysleft = length(f_gbm),
tcost = 0,
int = TRUE)

An S4 class for the OBPI hedging strategy

Description

An S4 class for the OBPI hedging strategy

Slots

StrikePrice: Strike price for the synthetic option hedging
AnnVol: Annualized volatility for the contract to be traded
InterestRate: Risk-free rate of interest
TradingDays: The number of trading days per year

S4 method for the plot generic for portfolio insurance strategy classes

Description

S4 method for the plot generic for portfolio insurance strategy classes

Usage

## S4 method for signature 'GenericStrat'
plot(
  x,
  y = NULL,
  title = "Strategy plot",
  xlab = "",
  ylab.1 = "Price",
  ylab.2 = "Hedge %",
  pcols = c("#F8766D", "steelblue3", "gray60", "gray80"),
  legend = "bottom"
)
## S4 method for signature 'GenericStrat'
plot(
  x,
  y = NULL,
  title = "Strategy plot",
  xlab = "",
  ylab.1 = "Price",
  ylab.2 = "Hedge %",
  pcols = c("#F8766D", "steelblue3", "gray60", "gray80"),
  legend = "bottom"
)

Arguments

`x`	instance of the strategy class created by the corresponding strategy function
`y`	NULL
`title`	plot title
`xlab`	label for x-axis
`ylab.1`	label for y-axis on price plot in top panel
`ylab.2`	label for y-axis on hedge plot in bottom panel
`pcols`	vector with four color codes for plot
`legend`	legend position in c("top", "bottom")

Value

a two-panel chart with daily values for (top panel) target price, market price and portfolio price and (bottom) portfolio hedge rate

S4 method for the plot generic for class "MSFC"

Description

S4 method for the plot generic for class "MSFC"

Usage

## S4 method for signature 'MSFC'
plot(
  x,
  y = NULL,
  plot.prior = FALSE,
  title = "",
  xlab = "",
  ylab = "Price",
  legend = "right"
)
## S4 method for signature 'MSFC'
plot(
  x,
  y = NULL,
  plot.prior = FALSE,
  title = "",
  xlab = "",
  ylab = "Price",
  legend = "right"
)

Arguments

`x`	instance of the MSFC class created by the msfc function
`y`	NULL
`plot.prior`	TRUE/FALSE for incuding prior function in plot
`title`	plot title
`xlab`	x-axis title
`ylab`	y-axis title
`legend`	position of legend, as implemented in ggplot2

Value

a chart with daily values for the forward curve and contracts used in calculation

Historical daily closing prices for 11 calendar year power futures contracts

Description

A synthetic dataset containing the closing prices and other attributes of 11 power futures contracts for calendar year delivery for 2006 - 2016.

Usage

powcal
powcal

Format

A data frame with 3253 rows and 12 columns:

Date: the trading date
CAL-06: the closing price for the 2006 futures contract
CAL-07: the closing price for the 2007 futures contract
CAL-08: the closing price for the 2008 futures contract
CAL-09: the closing price for the 2009 futures contract
CAL-10: the closing price for the 2010 futures contract
CAL-11: the closing price for the 2011 futures contract
CAL-12: the closing price for the 2012 futures contract
CAL-13: the closing price for the 2013 futures contract
CAL-14: the closing price for the 2014 futures contract
CAL-15: the closing price for the 2015 futures contract
CAL-16: the closing price for the 2016 futures contract

Closing prices for power futures contracts at trading date 2013-05-13

Description

A synthetic dataset containing the closing prices and other attributes of 38 power futures contracts.

Usage

powfutures130513
powfutures130513

Format

A data frame with 38 rows and 5 columns:

Include: boolean variable to determine if contract should be included in forward curve calculation
Contract: the name of the futures contract
Start: delivery start date for the futures contract
End: delivery start date for the futures contract
Closing: the futures contract closing price

Example priors at trading date 2015-05-13

Description

An example of two simple priors for forward market price to be used with powfutures130513

Usage

powpriors130513
powpriors130513

Format

A data frame with 3885 rows and 3 columns:

Date: vector of dates ranging from 2013-05-13 to final end date of contracts in powfutures130513
trig.prior: a simple smooth trigonometric prior describing power price seasonality
mod.prior: a trigonometric prior adjusted for typical calendar effects

S4 method for the show generic for portfolio insurance strategy classes

Description

S4 method for the show generic for portfolio insurance strategy classes

Usage

## S4 method for signature 'GenericStrat'
show(object)
## S4 method for signature 'GenericStrat'
show(object)

Arguments

object

instance of a strategy class

Value

a data frame with daily observations for market price, transactions, exposed volume, forward positions, hedge rate, target price and portfolio price

S4 method for the show generic for class "MSFC"

Description

S4 method for the show generic for class "MSFC"

Usage

## S4 method for signature 'MSFC'
show(object)
## S4 method for signature 'MSFC'
show(object)

Arguments

object

instance of the MSFC class

Value

data frame with daily values for forward curve and forward contracts used in calculation

Step Hedge Portfolio Insurance (SHPI)

Description

Implements SHPI strategy for commodity price risk management

Usage

shpi(q, tdate, f, daysleft, tper, tcost = 0, int = TRUE)
shpi(q, tdate, f, daysleft, tper, tcost = 0, int = TRUE)

Arguments

`q`	numeric value for quantity to be hedged, either positive (net buyer) or negative (net seller)
`tdate`	date vector with trading days
`f`	numeric futures price vector
`daysleft`	integer with days left to contract expiry
`tper`	numeric target price markup/down to the price on the first trading day
`tcost`	numeric transaction costs pr unit
`int`	TRUE/FALSE integer restriction on tradable volume

Value

instance of the SHPI class

Examples

# SHPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

# implement step-hedge strategy for buyer
shpi_b <- shpi(q = 10,
tdate = tr_dates,
f = f_gbm,
daysleft = length(tr_dates),
tper = 0.1,
tcost = 0,
int = TRUE)

# implement step-hedge strategy for seller
shpi_s <- shpi(q = - 10,
tdate = tr_dates,
f = f_gbm,
daysleft = length(tr_dates),
tper = - 0.1,
tcost = 0,
int = TRUE)

# SHPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

# implement step-hedge strategy for buyer
shpi_b <- shpi(q = 10,
tdate = tr_dates,
f = f_gbm,
daysleft = length(tr_dates),
tper = 0.1,
tcost = 0,
int = TRUE)

# implement step-hedge strategy for seller
shpi_s <- shpi(q = - 10,
tdate = tr_dates,
f = f_gbm,
daysleft = length(tr_dates),
tper = - 0.1,
tcost = 0,
int = TRUE)

An S4 class for the SHPI hedging strategy

Description

An S4 class for the SHPI hedging strategy

Stop Loss Portfolio Insurance (SLPI)

Description

Implements SLPI strategy for commodity price risk management

Usage

slpi(q, tdate, f, tper, tcost = 0, int = TRUE)
slpi(q, tdate, f, tper, tcost = 0, int = TRUE)

Arguments

`q`	numeric value for quantity to be hedged, either positive (net buyer) or negative (net seller)
`tdate`	date vector with trading days
`f`	numeric futures price vector
`tper`	numeric target price markup/down to the price on the first trading day
`tcost`	numeric transaction costs pr unit
`int`	TRUE/FALSE integer restriction on tradable volume

Value

instance of the SLPI class

Examples

# SLPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

# implement stop-loss strategy for buyer
slpi_b <- slpi(q = 10,
tdate = tr_dates,
f = f_gbm,
tper = 0.1,
tcost = 0,
int = TRUE)

# implement stop-loss strategy for seller
slpi_s <- slpi(q = - 10,
tdate = tr_dates,
f = f_gbm,
tper = - 0.1,
tcost = 0,
int = TRUE)

# SLPI for a buyer (seller), where stop loss is set 10% above (below) initial market price.

set.seed(5)
# GBM price process parameters
mu <- 0.2
sigma <- 0.1
S0 <- 100

# time
Y <- 2
N <- 500
delta <- Y/N
t <- seq (0, 1, length = N + 1)

# price process and date vector
W <- c(0, cumsum ( sqrt(delta) * rnorm (N)))
f_gbm <- S0 * exp(mu * t + sigma * W)
tr_dates <- seq(Sys.Date(), Sys.Date()+500, by = "day")

# implement stop-loss strategy for buyer
slpi_b <- slpi(q = 10,
tdate = tr_dates,
f = f_gbm,
tper = 0.1,
tcost = 0,
int = TRUE)

# implement stop-loss strategy for seller
slpi_s <- slpi(q = - 10,
tdate = tr_dates,
f = f_gbm,
tper = - 0.1,
tcost = 0,
int = TRUE)

An S4 class for the SLPI hedging strategy

Description

An S4 class for the SLPI hedging strategy

S4 method for the summary generic for portfolio insurance strategy classes

Description

S4 method for the summary generic for portfolio insurance strategy classes

Usage

## S4 method for signature 'GenericStrat'
summary(object)
## S4 method for signature 'GenericStrat'
summary(object)

Arguments

object

instance of a strategy class

Value

a list with five elements. 1) A string describing the type of portfolio insurance trading strategy and number of observations, 2) volume to be hedged, calculated churn rate (numer of times volume to be hedged has been traded) and 5) a data frame with summary statistics for achieved results

S4 method for the summary generic for class "MSFC"

Description

S4 method for the summary generic for class "MSFC"

Usage

## S4 method for signature 'MSFC'
summary(object)
## S4 method for signature 'MSFC'
summary(object)

Arguments

object

instance of the MSFC class

Value

a list with three elements. 1) A string describing length of forward curve, number of polynomials used in spline and trading date, 2) a vector with a sample of the prior used via head(prior) and 3) a data frame with all forward contracts used in the calculation along with computed forward curve prices

Package 'etrm'

Help Index

etrm: Energy Trading and Risk Management

Description

Author(s)

References

Constant Proportion Portfolio Insurance (CPPI)

Description

Usage

Arguments

Value

Examples

An S4 class for the CPPI hedging strategy

Description

Slots

Dynamic Proportion Portfolio Insurance (DPPI)

Description

Usage

Arguments

Value

Examples

An S4 class for the DPPI hedging strategy

Description

Slots

An S4 VIRTUAL parent class for the hedging strategy classes in etrm

Description

Slots

Maximum Smoothness Forward Curve (MSFC)

Description

Usage

Arguments

Value

Examples

An S4 class for the Maximum Smoothness Forward Curve (MSFC) in etrm

Description

Slots

Option Based Portfolio Insurance (OBPI)

Description

Usage

Arguments

Value

Examples

An S4 class for the OBPI hedging strategy

Description

Slots

S4 method for the plot generic for portfolio insurance strategy classes

Description

Usage

Arguments

Value

S4 method for the plot generic for class "MSFC"

Description

Usage

Arguments

Value

Historical daily closing prices for 11 calendar year power futures contracts

Description

Usage

Format

Closing prices for power futures contracts at trading date 2013-05-13

Description

Usage

Format

Example priors at trading date 2015-05-13

Description

Usage

Format

S4 method for the show generic for portfolio insurance strategy classes

Description

Usage

Arguments

Value

S4 method for the show generic for class "MSFC"

Description

Usage

Arguments

Value

Step Hedge Portfolio Insurance (SHPI)

Description

Usage