Package 'emhawkes'

Expected Inter-Arrival Time

Description

Computes the conditional expected time until the next event.

Usage

expected_tau(
  object,
  rambda_component,
  type = 1,
  mu = NULL,
  beta = NULL,
  tol = .Machine$double.eps^0.25,
  max_upper = Inf,
  subdivisions = 400L
)

Arguments

object	An object of class hspec.
rambda_component	Rambda component.
type	Process dimension index (default is 1).
mu	Optional mu value (overrides object@mu if provided).
beta	Optional beta value (overrides object@beta if provided).
tol	Relative tolerance for numerical integration.
max_upper	Upper integration limit.
subdivisions	Number of subdivisions for numerical integration.

Value

Expected value of next inter-arrival time.

---

Perform Maximum Likelihood Estimation

Description

This is a generic function named hfit designed for estimating the parameters of the exponential Hawkes model. It is implemented as an S4 method for two main reasons:

Usage

hfit(
  object,
  inter_arrival = NULL,
  type = NULL,
  mark = NULL,
  N = NULL,
  Nc = NULL,
  lambda_component0 = NULL,
  N0 = NULL,
  mylogLik = NULL,
  reduced = TRUE,
  grad = NULL,
  hess = NULL,
  constraint = NULL,
  method = "BFGS",
  verbose = FALSE,
  ...
)

## S4 method for signature 'hspec'
hfit(
  object,
  inter_arrival = NULL,
  type = NULL,
  mark = NULL,
  N = NULL,
  Nc = NULL,
  lambda_component0 = NULL,
  N0 = NULL,
  mylogLik = NULL,
  reduced = TRUE,
  grad = NULL,
  hess = NULL,
  constraint = NULL,
  method = "BFGS",
  verbose = FALSE,
  ...
)

Arguments

object	An hspec-class object containing the parameter values.
inter_arrival	A vector of inter-arrival times for events across all dimensions, starting with zero.
type	A vector indicating the dimensions, represented by numbers like 1, 2, 3, etc., starting with zero.
mark	A vector of mark (jump) sizes, starting with zero.
N	A matrix representing counting processes.
Nc	A matrix of counting processes weighted by mark sizes.
lambda_component0	Initial values for the lambda component $\lambda_{ij}$. Can be a numeric value or a matrix. Must have the same number of rows and columns as alpha or beta in object.
N0	Initial values for the counting processes matrix N.
mylogLik	A user-defined log-likelihood function, which must accept an object argument consistent with object.
reduced	Logical; if TRUE, performs reduced estimation.
grad	A gradient matrix for the likelihood function. Refer to maxLik for more details.
hess	A Hessian matrix for the likelihood function. Refer to maxLik for more details.
constraint	Constraint matrices. Refer to maxLik for more details.
method	The optimization method to be used. Refer to maxLik for more details.
verbose	Logical; if TRUE, prints the progress of the estimation process.
...	Additional parameters for optimization. Refer to maxLik for more details.

Details

Model Representation: To represent the structure of the model as an hspec object. The multivariate marked Hawkes model has numerous variations, and using an S4 class allows for a flexible and structured approach.

Optimization Initialization: To provide a starting point for numerical optimization. The parameter values assigned to the hspec slots serve as initial values for the optimization process.

This function utilizes the maxLik package for optimization.

Value

maxLik object

See Also

hspec-class, hsim,hspec-method

Examples

# example 1
mu <- c(0.1, 0.1)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=100)
summary(hfit(h, inter_arrival=res$inter_arrival, type=res$type))


# example 2

mu <- matrix(c(0.08, 0.08, 0.05, 0.05), nrow = 4)
alpha <- function(param = c(alpha11 = 0, alpha12 = 0.4, alpha33 = 0.5, alpha34 = 0.3)){
  matrix(c(param["alpha11"], param["alpha12"], 0, 0,
           param["alpha12"], param["alpha11"], 0, 0,
           0, 0, param["alpha33"], param["alpha34"],
           0, 0, param["alpha34"], param["alpha33"]), nrow = 4, byrow = TRUE)
}
beta <- matrix(c(rep(0.6, 8), rep(1.2, 8)), nrow = 4, byrow = TRUE)

impact <- function(param = c(alpha1n=0, alpha1w=0.2, alpha2n=0.001, alpha2w=0.1),
                   n=n, N=N, ...){

  Psi <- matrix(c(0, 0, param['alpha1w'], param['alpha1n'],
                  0, 0, param['alpha1n'], param['alpha1w'],
                  param['alpha2w'], param['alpha2n'], 0, 0,
                  param['alpha2n'], param['alpha2w'], 0, 0), nrow=4, byrow=TRUE)

  ind <- N[,"N1"][n] - N[,"N2"][n] > N[,"N3"][n] - N[,"N4"][n] + 0.5

  km <- matrix(c(!ind, !ind, !ind, !ind,
                 ind, ind, ind, ind,
                 ind, ind, ind, ind,
                 !ind, !ind, !ind, !ind), nrow = 4, byrow = TRUE)

  km * Psi
}
h <- new("hspec",
         mu = mu, alpha = alpha, beta = beta, impact = impact)
hr <- hsim(h, size=100)
plot(hr$arrival, hr$N[,'N1'] - hr$N[,'N2'], type='s')
lines(hr$N[,'N3'] - hr$N[,'N4'], type='s', col='red')
fit <- hfit(h, hr$inter_arrival, hr$type)
summary(fit)


# example 3

mu <- c(0.15, 0.15)
alpha <- matrix(c(0.75, 0.6, 0.6, 0.75), nrow=2, byrow=TRUE)
beta <- matrix(c(2.6, 2.6, 2.6, 2.6), nrow=2, byrow=TRUE)
rmark <- function(param = c(p=0.65), ...){
  rgeom(1, p=param[1]) + 1
}
impact <- function(param = c(eta1=0.2), alpha, n, mark, ...){
  ma <- matrix(rep(mark[n]-1, 4), nrow = 2)
  alpha * ma * matrix( rep(param["eta1"], 4), nrow=2)
}
h1 <- new("hspec", mu=mu, alpha=alpha, beta=beta,
          rmark = rmark,
          impact=impact)
res <- hsim(h1, size=100, lambda_component0 = matrix(rep(0.1,4), nrow=2))

fit <- hfit(h1,
            inter_arrival = res$inter_arrival,
            type = res$type,
            mark = res$mark,
            lambda_component0 = matrix(rep(0.1,4), nrow=2))
summary(fit)

# For more information, please see vignettes.

---

Realization of Hawkes Process

Description

hreal is a list containing the following components:

• hspec: An S4 object of class hspec-class that specifies the parameter values.

• inter_arrival: The time intervals between consecutive events.

• arrival: The cumulative sum of inter_arrival times.

• type: An integer representing the type of event.

• mark: The size of the mark, providing additional information associated with the event.

• N: A counting process that tracks the number of events.

• Nc: A counting process that tracks the number of events, weighted by mark.

• lambda: The left-continuous intensity process.

• lambda_component: The component of the intensity process, $\lambda_{ij}$, that excludes mu.

• rambda: The right-continuous intensity process.

• rambda_component: The right-continuous version of lambda_component.

Functions for printing hreal objects are provided.

Usage

## S3 method for class 'hreal'
print(x, n = 20, ...)

## S3 method for class 'hreal'
summary(object, n = 20, ...)

## S3 method for class 'hreal'
as.matrix(x, ...)

Arguments

x	An S3 object of class hreal.
n	The number of rows to display.
...	Additional arguments passed to or from other methods.
object	An S3 object of class hreal.

---

Simulate multivariate Hawkes process with exponential kernel.

Description

The method simulate multivariate Hawkes processes. The object hspec-class contains the parameter values such as mu, alpha, beta. The mark (jump) structure may or may not be included. It returns an object of class hreal which contains inter_arrival, arrival, type, mark, N, Nc, lambda, lambda_component, rambda, rambda_component.

Usage

hsim(
  object,
  size = 100,
  lambda_component0 = NULL,
  N0 = NULL,
  Nc0 = NULL,
  verbose = FALSE,
  ...
)

## S4 method for signature 'hspec'
hsim(
  object,
  size = 100,
  lambda_component0 = NULL,
  N0 = NULL,
  Nc0 = NULL,
  verbose = FALSE,
  ...
)

Arguments

object	hspec-class. S4 object that specifies the parameter values.
size	Number of observations.
lambda_component0	Initial values for the lambda component $\lambda_{ij}$. Can be a numeric value or a matrix. Must have the same number of rows and columns as alpha or beta in object.
N0	Starting values of N with default value 0.
Nc0	Starting values of Nc with default value 0.
verbose	Logical. If TRUE, print progress messages during the simulation. Default is FALSE.
...	Further arguments passed to or from other methods.

Value

hreal S3-object, summary of the Hawkes process realization.

Examples

# example 1

mu <- 1; alpha <- 1; beta <- 2
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
hsim(h, size=100)


# example 2
mu <- matrix(c(0.1, 0.1), nrow=2)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=100)
print(res)

---

An S4 Class Representing an Exponential Marked Hawkes Model

Description

This class defines a marked Hawkes model with an exponential kernel. The intensity of the ground process is expressed as:

$\lambda(t) = \mu + \int_{(-\infty,t)\times E} ( \alpha + g(u, z) ) e^{-\beta (t-u)} M(du \times dz).$

For more details, refer to the vignettes.

Details

$\mu$ is base intensity, typically a constant vector or a function.

$\alpha$ is a constant matrix representing the impact on intensities after events, stored in the alpha slot.

$\beta$ is a constant matrix for exponential decay rates, stored in the beta slot.

$z$ represents the mark and can be generated by rmark slot.

$g$ is represented by eta when it is linear function of $z$, and by impact when it is a genenral function.

mu, alpha and beta are required slots for every exponential Hawkes model. rmark and impact are additional slots.

Slots

mu

A numeric value, matrix, or function. If numeric, it is automatically converted to a matrix.

alpha

A numeric value, matrix, or function. If numeric, it is automatically converted to a matrix, representing the exciting term.

beta

A numeric value, matrix, or function. If numeric, it is automatically converted to a matrix, representing the exponential decay.

eta

A numeric value, matrix, or function. If numeric, it is automatically converted to a matrix, representing the impact of an additional mark.

impact

A function describing the after-effects of the mark on $\lambda$, with the first argument always being param.

dimens

The dimension of the model.

rmark

A function that generates marks for the counting process, used in simulations.

dmark

A density function for the mark, used in estimation.

type_col_map

A mapping between type and column number of the kernel used in multi-kernel models.

rresidual

A function for generating residuals, analogous to the R random number generator function, specifically for the discrete Hawkes model.

dresidual

A density function for the residual.

presidual

A distribution function for the residual.

qresidual

A quantile function for the residual.

Examples

MU <- matrix(c(0.2), nrow = 2)
ALPHA <- matrix(c(0.75, 0.92, 0.92, 0.75), nrow = 2, byrow=TRUE)
BETA <- matrix(c(2.25, 2.25, 2.25, 2.25), nrow = 2, byrow=TRUE)
mhspec2 <- new("hspec", mu=MU, alpha=ALPHA, beta=BETA)
mhspec2

---

Compute Hawkes volatility

Description

This function computes Hawkes volatility. Only works for bi-variate Hawkes process.

Usage

hvol(
  object,
  horizon = 1,
  inter_arrival = NULL,
  type = NULL,
  mark = NULL,
  dependence = FALSE,
  lambda_component0 = NULL,
  ...
)

## S4 method for signature 'hspec'
hvol(
  object,
  horizon = 1,
  inter_arrival = NULL,
  type = NULL,
  mark = NULL,
  dependence = FALSE,
  lambda_component0 = NULL,
  ...
)

Arguments

object	hspec-class
horizon	Time horizon for volatility.
inter_arrival	Inter-arrival times of events which includes inter-arrival for events that occur in all dimensions. Start with zero.
type	A vector of dimensions. Distinguished by numbers, 1, 2, 3, and so on. Start with zero.
mark	A vector of mark (jump) sizes. Start with zero.
dependence	Dependence between mark and previous sigma-algebra.
lambda_component0	A matrix of the starting values of lambda component.
...	Further arguments passed to or from other methods.

---

Infer lambda process with given Hawkes model and realized path

Description

This method compute the inferred lambda process and returns it as hreal form. If we have realized path of Hawkes process and its parameter value, then we can compute the inferred lambda processes. Similarly with other method such as hfit, the input arguments are inter_arrival, type, mark, or equivalently, N and Nc.

Usage

infer_lambda(
  object,
  inter_arrival = NULL,
  type = NULL,
  mark = NULL,
  N = NULL,
  Nc = NULL,
  lambda_component0 = NULL,
  N0 = NULL,
  Nc0 = NULL,
  ...
)

## S4 method for signature 'hspec'
infer_lambda(
  object,
  inter_arrival = NULL,
  type = NULL,
  mark = NULL,
  N = NULL,
  Nc = NULL,
  lambda_component0 = NULL,
  N0 = NULL,
  Nc0 = NULL,
  ...
)

Arguments

object	hspec-class. This object includes the parameter values.
inter_arrival	inter-arrival times of events. This includes inter-arrival for events that occur in all dimensions. Start with zero.
type	a vector of dimensions. Distinguished by numbers, 1, 2, 3, and so on. Start with zero.
mark	a vector of mark (jump) sizes. Start with zero.
N	Hawkes process. If not provided, then generate using inter_arrival and type.
Nc	mark accumulated Hawkes process. If not provided, then generate using inter_arrival, type and mark.
lambda_component0	Initial values for the lambda component $\lambda_{ij}$. Can be a numeric value or a matrix. Must have the same number of rows and columns as alpha or beta in object.
N0	the initial values of N.
Nc0	the initial values of Nc.
...	further arguments passed to or from other methods.

Value

hreal S3-object, with inferred intensity.

Examples

mu <- c(0.1, 0.1)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=100)
summary(res)
res2 <- infer_lambda(h, res$inter_arrival, res$type)
summary(res2)

---

Compute the Log-Likelihood Function

Description

Calculates the log-likelihood for the Hawkes model.

Usage

## S4 method for signature 'hspec'
logLik(
  object,
  inter_arrival,
  type = NULL,
  mark = NULL,
  N = NULL,
  Nc = NULL,
  N0 = NULL,
  Nc0 = NULL,
  lambda_component0 = NULL,
  infer = FALSE,
  ...
)

Arguments

object	An hspec-class object containing parameter values for computing the log-likelihood.
inter_arrival	A vector of inter-arrival times for events across all dimensions, starting with zero.
type	A vector indicating the dimensions, represented by numbers (1, 2, 3, etc.), starting with zero.
mark	A vector of mark (jump) sizes, starting with zero.
N	A matrix representing counting processes.
Nc	A matrix of counting processes weighted by mark sizes.
N0	A matrix of initial values for N.
Nc0	A matrix of initial values for Nc.
lambda_component0	Initial values for the lambda component $\lambda_{ij}$. Can be a numeric value or a matrix. Must have the same number of rows and columns as alpha or beta in object.
infer	Logical
...	Additional arguments passed to or from other methods.

See Also

hspec-class, hfit,hspec-method

---

Compute residual process

Description

Using random time change, this function compute the residual process, which is the inter-arrival time of a standard Poisson process. Therefore, the return values should follow the exponential distribution with rate 1, if model and rambda are correctly specified.

Usage

residual_process(
  component,
  inter_arrival,
  type,
  rambda_component,
  mu,
  beta,
  dimens = NULL,
  mark = NULL,
  N = NULL,
  Nc = NULL,
  lambda_component0 = NULL,
  N0 = NULL,
  ...
)

Arguments

component	The component of type to get the residual process.
inter_arrival	Inter-arrival times of events. This includes inter-arrival for events that occur in all dimensions. Start with zero.
type	A vector of types distinguished by numbers, 1, 2, 3, and so on. Start with zero.
rambda_component	Right continuous version of lambda process.
mu	Numeric value or matrix or function. If numeric, automatically converted to matrix.
beta	Numeric value or matrix or function. If numeric, automatically converted to matrix, exponential decay.
dimens	Dimension of the model. If omitted, set to be the length of mu.
mark	A vector of realized mark (jump) sizes. Start with zero.
N	A matrix of counting processes.
Nc	A matrix of counting processes weighted by mark.
lambda_component0	The initial values of lambda component. Must have the same dimensional matrix with hspec.
N0	The initial value of N
...	Further arguments passed to or from other methods.

Examples

mu <- c(0.1, 0.1)
alpha <- matrix(c(0.2, 0.1, 0.1, 0.2), nrow=2, byrow=TRUE)
beta <- matrix(c(0.9, 0.9, 0.9, 0.9), nrow=2, byrow=TRUE)
h <- new("hspec", mu=mu, alpha=alpha, beta=beta)
res <- hsim(h, size=1000)
rp <- residual_process(component = 1, res$inter_arrival, res$type, res$rambda_component, mu, beta)

---

Set Residual Distribution Functions for Hawkes Model Specification

Description

Sets residual distribution functions (density, CDF, quantile, and random generation) for a Hawkes model specification object with fixed parameters.

Usage

set_residual(
  object,
  param,
  dresidual = NULL,
  presidual = NULL,
  qresidual = NULL,
  rresidual = NULL,
  ...
)

## S4 method for signature 'hspec'
set_residual(
  object,
  param,
  dresidual = NULL,
  presidual = NULL,
  qresidual = NULL,
  rresidual = NULL
)

Arguments

object	An object of class hspec (Hawkes model specification)
param	A named numeric vector of parameters for the residual distribution
dresidual	Density function of the residual distribution (optional)
presidual	Cumulative distribution function (CDF) of the residual distribution (optional)
qresidual	Quantile function of the residual distribution (optional)
rresidual	Random generation function of the residual distribution (optional)
...	Additional arguments for future extensions

Details

This method allows setting residual distribution functions for a flexible model.

The param argument in these functions defaults to the parameters provided during setup and is used for estimation.

Value

An updated hspec object with residual functions set

Examples

## Not run: 
# Create basic Hawkes specification
hspec_obj <- new("hspec",
                 mu = matrix(0.1, nrow = 1),
                 alpha = matrix(0.5, nrow = 1),
                 beta = matrix(1.0, nrow = 1))

# Set residual distribution parameters
params <- c(a = 0.5, ell = 1.0)

# Apply residual functions
hspec_obj <- set_residual(
  hspec_obj,
  param = params,
  dresidual = dtzexp,
  presidual = ptzexp,
  qresidual = qtzexp,
  rresidual = rtzexp
)

# Check resulting functions
hspec_obj@dresidual
hspec_obj@rresidual

## End(Not run)

---

Trapezoid + Exponential Distribution

Description

These functions implement a custom distribution combining a trapezoidal section (0 < x < a) and an exponential tail ($x \geq a$). The distribution is parameterized by:

• a: transition point between trapezoid and exponential

• ell: rate parameter for the exponential tail

Usage

dtzexp(x, a, ell)

ptzexp(q, a, ell)

qtzexp(p, a, ell)

rtzexp(n, a, ell)

Arguments

x, q	vector of quantiles
a	location parameter for transition (must be > 0)
ell	rate parameter for exponential decay (must be > 0)
p	vector of probabilities
n	number of observations

Details

Density, distribution function, quantile function and random generation for a custom trapezoid + exponential distribution.

The trapezoid+exponential distribution has the probability density function:

$f(x) = \begin{cases} 0 & \text{if } x \leq 0 \\ \frac{(p\ell - c)}{a} x + c & \text{if } 0 < x < a \\ p\ell e^{-\ell (x - a)} & \text{if } x \geq a \end{cases}$

where:

$p = \frac{\ell - \frac{a\ell}{3}}{\frac{a^2\ell^2}{6} + \frac{2a\ell}{3} + 1}$

$c = \frac{2 - 2p - p\ell a}{a}$

The trapezoid+exponential distribution has the following characteristics:

• Support on $[0, \infty)$

• Continuous probability distribution

• Linear density from 0 to a

• Exponential decay for x > a

Value

• dtzexp gives the density

• ptzexp gives the distribution function

• qtzexp gives the quantile function

• rtzexp generates random deviates

---