Package SMFI5. February 19, 2015
|
|
|
- Paul Quentin Tate
- 6 years ago
- Views:
Transcription
1 Type Package Package SMFI5 February 19, 2015 Title R functions and data from Chapter 5 of 'Statistical Methods for Financial Engineering' Version 1.0 Date Author Maintainer <bruno.remillard@hec.ca> R functions and data from Chapter 5 of 'Statistical Methods for Financial Engineering', by, CRC Press, (2013). License GPL (>= 2) Depends ggplot2, reshape, corpcor URL NeedsCompilation no Repository CRAN Date/Publication :56:20 R topics documented: bond.cir bond.vasicek data.cir data.vasicek est.cir est.feller est.ou est.vasicek get.cir.param get.vasicek.param LogLikCIR LogLikFeller
2 2 bond.cir LogLikOU LogLikVasicek num.jacobian sim.cir sim.n.chi sim.vasicek Index 19 bond.cir Simulates the values and yields of zero-coupon bonds when the spot rate is modeled by a Feller process. Simulates the values and yields of zero-coupon bonds when the (annualized ) spot rate (in percent) is modeled by a Feller process satisfying dr = alpha(beta-r)dt + sigma sqrt(r) dw, with market price of risk q = q1/sqrt(r) +q2 sqrt(r). The maturities are 1,3,6 and 12 months. bond.cir(alpha, beta, sigma, q1, q2, r0, n, maturities, days = 360) alpha beta sigma q1 q2 r0 n maturities days Mean-reversion parameter. Long term mean. Volatility parameter. Market prime of risk parameter. Market prime of risk parameter. Initial rate value. Number of periods. Maturities in years (row vector). Days in a year convention (360 default). P R tau Bond values. Annual rate for the bond. Maturities in years.
3 bond.vasicek 3 out = bond.cir(0.5,2.55,0.365,0.3,0,3.55,1080,c(1/12, 3/12, 6/12, 1),365) bond.vasicek Simulates the values and yields of zero-coupon bonds when the spot rate is modeled by a Ornstein-Uhlenbeck process Simulates the values and yields of zero-coupon bonds when the (annualized ) spot rate (in percent) is modeled by a Ornstein-Uhlenbeck process satisfying dr <- alpha(beta-r)dt + sigma dw, with market price of risk q(r) <- q1+q2 r. The maturities are 1,3,6 and 12 months. bond.vasicek(alpha, beta, sigma, q1, q2, r0, n, maturities, days = 360) alpha beta sigma q1 q2 r0 n maturities days Mean-reversion parameter. Long term mean. Volatility parameter. Market prime of risk parameter. Market prime of risk parameter. Initial rate value. Number of periods. Maturities in years (row vector). Days in a year convention (360 default). P R tau Bond values. Annual rate for the bond. Maturities in years.
4 4 data.cir out = bond.vasicek(0.5,2.55,0.365,0.3,0,3.55,1080,c(1/12, 3/12, 6/12, 1),365); data.cir Yields and maturities simulated from the CIR model. Yields and maturities simulated from the CIR model, wth parameters alpha = 0.5, beta = 2.55, sigma = 0.365, q1 = 0.3, q2 = 0, h = 1/360. The maturities are 1,3,6, and 12 months. data(data.cir) Format The format is: c(r,tau) = [1:1440, 1:2] Source The program bond.cir was used to simulate these data. data(data.cir) ## maybe str(data.cir) ; plot(data.cir)...
5 data.vasicek 5 data.vasicek Yields and maturities simulated from the Vasicek model. Yields and maturities simulated from the Vasicek model, wth parameters alpha = 0.5, beta = 2.55, sigma = 0.365, q1 = 0.3, q2 = 0, h = 1/360. The maturities are 1,3,6, and 12 months. data(data.vasicek) Format The format is: c(r,tau) = [1:1440, 1:2] Source The program bond.vasicek was used to simulate these data. data(data.vasicek) ## maybe str(data.vasicek) ; plot(data.vasicek)... est.cir Estimates the parameters of the CIR model. Estimates the parameters of the CIR model dr = alpha(beta-r)dt + sigma sqrt(r) dw with market price of risk q(r) = q1/sqrt(r) +q2 sqrt(r). The time scale is in years and the units are percentages. est.cir(data, method = "Hessian", days = 360, significancelevel = 0.95)
6 6 est.feller data method c(r,tau) (n x 2), with R: annual bonds yields in percentage, and tau: maturities in years. Hessian (default), num. days Number of days per year (default: 360). significancelevel 95%(default). theta error rimp Parameters (alpha, beta, sigma, q1,q2) of the model. Estimation errors for the given confidence level. Implied spot rate. data(data.cir) out = est.cir(data.cir,method= num ) est.feller Estimates the parameters of the Feller process. Estimates the parameters of the Feller process dr = alpha(beta-r)dt + sigma sqrt(r) dw The time scale is in years and the units are percentages. est.feller(data, method = "Hessian", days = 360, significancelevel = 0.95)
7 est.ou 7 data method annual bonds yields in percentage; Hessian (default), num ; days number of days per year (default: 360); significancelevel (95% default). param error parameters (alpha, beta, sigma) of the model; estimation errors for the given confidence level. data(data.cir) out = est.feller(data.cir[,1]) #The first colum contains returns. est.ou Estimates the parameters of the Ornstein-Uhlenbeck process.~~ Estimates the parameters of the Ornstein-Uhlenbeck process dr = alpha(beta-r)dt + sigma dw. est.ou(data, method = "Hessian", days = 360, significancelevel = 0.95) data method annual bonds yields in percentage; Hessian (default), num ; days number of days per year (default: 360); significancelevel (95% default).
8 8 est.vasicek param error parameters (alpha, beta, sigma) of the model; estimation errors for the given confidence level. data(data.vasicek) out = est.ou(data.vasicek[,1]) #The first colum contains returns. est.vasicek Estimates the parameters of the Vasicek model. ~~ Estimates the parameters of the Vasicek model. dr = alpha(beta-r)dt + sigma dw, with market price of risk q(r) = q1+q2 r. The time scale is in years and the units are percentages. est.vasicek(data, method = "Hessian", days = 360, significancelevel = 0.95) data method c(r,tau) (n x 2), with R: annual bonds yields in percentage, and tau: maturities in years; Hessian (default), num ; days number of days per year (default: 360); significancelevel 95%(default) theta error rimp parameters (alpha, beta, sigma, q1,q2) of the model; estimation errors for the given confidence level; implied spot rate.
9 get.cir.param 9 data(data.vasicek) out = est.vasicek(data.vasicek) get.cir.param Computes the terms A and B for the price of a zero-coupon bond under the CIR model. Computes the terms A and B for the price of a zero-coupon bond under the CIR model. get.cir.param(param, tau, scalingfact = 1) param Parameters of the CIR model: alpha,beta,sigma,q1,q2. tau Vector of maturities. scalingfact Scaling factor (default =1). A B See formula in the book. See formula in the book.
10 10 get.vasicek.param params <- get.cir.param( c(0.3,2.55,0.365,0.3,0), 1) get.vasicek.param Computes the terms A and B for the price of a zero-coupon bond under the Vasicek model. Computes the terms A and B for the price of a zero-coupon bond under the Vasicek model. get.vasicek.param(param, tau, scalingfact = 1) param Parameters of the Vasicek model: alpha,beta,sigma,q1,q2. tau Vector of maturities. scalingfact Scaling factor (default =1). A B See formula in the book. See formula in the book. params <- get.vasicek.param( c(0.3,2.55,0.365,0.3,0), 1)
11 LogLikCIR 11 LogLikCIR Estimates the parameters of the CIR model. Loglikelihood for the CIR model dr = alpha(beta-r)dt + sigma sqrt(r) dw with market price of risk q(r) = q1/sqrt(r) +q2 sqrt(r). The time scale is in years and the units are percentages. LogLikCIR( theta, R, tau, days, n) theta R tau days n Vector of parameters: (alpha,beta,sigma,q1,q2). Observed returns. Maturities. Number of days in a year. Length of the time series. LL -1 x Log-likelihood (to be minimized).
12 12 LogLikFeller LogLikFeller Estimates the parameters of the Feller process. Loglikelihood for the CIR model dr = alpha(beta-r)dt + sigma sqrt(r) dw. The time scale is in years and the units are percentages. LogLikFeller( theta, R, days, n) theta R days n Vector of parameters: (alpha,beta,sigma,q1,q2). Observed returns. Number of days in a year. Length of the time series. LL -1 x Log-likelihood (to be minimized).
13 LogLikOU 13 LogLikOU Estimates the parameters of the Ornstein-Uhlenbeck process. Loglikelihood for the OU model dr = alpha(beta-r)dt + sigma dw. The time scale is in years and the units are percentages. LogLikOU( theta, R, days, n) theta R days n Vector of parameters: (alpha,beta,sigma,q1,q2). Observed returns. Number of days in a year. Length of the time series. LL -1 x Log-likelihood (to be minimized).
14 14 LogLikVasicek LogLikVasicek Estimates the parameters of the Vasicek model. Loglikelihood for the Vasicek model dr = alpha(beta-r)dt + sigma dw with market price of risk q(r) = q1 +q2 r. The time scale is in years and the units are percentages. LogLikVasicek( theta, R, tau, days, n) theta R tau days n Vector of parameters: (alpha,beta,sigma,q1,q2). Observed returns. Maturities. Number of days in a year. Length of the time series. LL -1 x Log-likelihood (to be minimized).
15 num.jacobian 15 num.jacobian Compute the symmetric numerical first order derivatives of a multivariate function. Compute the symmetric numerical first order derivatives of a multivariate function. num.jacobian(fct_handle, x, prec) fct_handle x prec Name of a function returning a N x 1 vector. Point (d x 1) of evaluation at which the derivatives will be computed. Percentage of +\- around x (in fraction). J Derivatives (N x d) Appendix B of Statistical Methods for Financial Engineering, B. Remillard, CRC Press, (2013). data(data.cir) out = est.cir(data.cir,method= num )
16 16 sim.cir sim.cir Simulates the Feller process. Simulates the Feller process dr = alpha(beta-r)dt + sigma sqrt(r) dw. sim.cir(alpha, beta, sigma, r0, n, h) alpha beta sigma r0 n h Mean-reversion parameter. Long term mean. Volatility parameter. Initial rate value. Number of periods. Time between observations. r Simulated annual rate in percent. r = sim.cir(0.5,2.55,0.365,2.55,720,1/360)
17 sim.n.chi2 17 sim.n.chi2 Simulates a non-central chi-square variable. Simulates a non-central chi-square variable with parameters nu (degrees of freedom) and lambda (non-centrality). sim.n.chi2(nu, lambda) nu lambda Degrees of freedom. Non centrality parameter. x Generated random variable. x = sim.n.chi2(10,4.5)
18 18 sim.vasicek sim.vasicek Simulates the Ornstein-Uhlenbeck process. Simulates the Ornstein-Uhlenbeck process dr = alpha(beta-r)dt + sigma dw. sim.vasicek(alpha, beta, sigma, r0, n, h) alpha beta sigma r0 n h Mean-reversion parameter. Long term mean. Volatility parameter. Initial rate value. Number of periods. Time between observations. r Simulated annual rate in percent. r = sim.vasicek(0.5,2.55,0.365,2.55,360,1/360)
19 Index Topic Bond bond.cir, 2 bond.vasicek, 3 Topic CIR model bond.cir, 2 data.cir, 4 est.cir, 5 get.cir.param, 9 LogLikCIR, 11 Topic Coefficients for bond pricing get.cir.param, 9 get.vasicek.param, 10 Topic Feller process est.feller, 6 LogLikFeller, 12 sim.cir, 16 Topic Log-likelihood LogLikCIR, 11 LogLikFeller, 12 LogLikOU, 13 LogLikVasicek, 14 Topic MLE estimation est.cir, 5 est.feller, 6 est.ou, 7 est.vasicek, 8 Topic Non-central chi-square sim.n.chi2, 17 Topic Numerical derivative num.jacobian, 15 Topic Ornstein-Uhlenbeck process est.ou, 7 LogLikOU, 13 sim.vasicek, 18 Topic Simulation sim.cir, 16 sim.n.chi2, 17 sim.vasicek, 18 Topic Vasicek model bond.vasicek, 3 data.vasicek, 5 est.vasicek, 8 get.vasicek.param, 10 LogLikVasicek, 14 Topic datasets data.cir, 4 data.vasicek, 5 bond.cir, 2 bond.vasicek, 3 data.cir, 4 data.vasicek, 5 est.cir, 5 est.feller, 6 est.ou, 7 est.vasicek, 8 get.cir.param, 9 get.vasicek.param, 10 LogLikCIR, 11 LogLikFeller, 12 LogLikOU, 13 LogLikVasicek, 14 num.jacobian, 15 sim.cir, 16 sim.n.chi2, 17 sim.vasicek, 18 19
Package ESGtoolkit. February 19, 2015
Type Package Package ESGtoolkit February 19, 2015 Title Toolkit for the simulation of financial assets and interest rates models. Version 0.1 Date 2014-06-13 Author Jean-Charles Croix, Thierry Moudiki,
Package MixedPoisson
Type Package Title Mixed Poisson Models Version 2.0 Date 2016-11-24 Package MixedPoisson December 9, 2016 Author Alicja Wolny-Dominiak and Maintainer Alicja Wolny-Dominiak
Package ald. February 1, 2018
Type Package Title The Asymmetric Laplace Distribution Version 1.2 Date 2018-01-31 Package ald February 1, 2018 Author Christian E. Galarza and Victor H. Lachos
Package XNomial. December 24, 2015
Type Package Package XNomial December 24, 2015 Title Exact Goodness-of-Fit Test for Multinomial Data with Fixed Probabilities Version 1.0.4 Date 2015-12-22 Author Bill Engels Maintainer
Package MSMwRA. August 7, 2018
Type Package Package MSMwRA August 7, 2018 Title Multivariate Statistical Methods with R Applications Version 1.3 Date 2018-07-17 Author Hasan BULUT Maintainer Hasan BULUT Data
Package ELMSO. September 3, 2018
Type Package Package ELMSO September 3, 2018 Title Implementation of the Efficient Large-Scale Online Display Advertising Algorithm Version 1.0.0 Date 2018-8-31 Maintainer Courtney Paulson
Package LNIRT. R topics documented: November 14, 2018
Package LNIRT November 14, 2018 Type Package Title LogNormal Response Time Item Response Theory Models Version 0.3.5 Author Jean-Paul Fox, Konrad Klotzke, Rinke Klein Entink Maintainer Konrad Klotzke
OSU Interfraternity Council Chapter Conduct History
Semester Alpha Gamma Rho Alpha Sigma Phi OSU Interfraternity Council Chapter History Violation, Weapons, False Representation Failed IFC Inspection Function Registration Past 5 Academic Years Through Spring
Package smam. October 1, 2016
Type Package Title Statistical Modeling of Animal Movements Version 0.3-0 Date 2016-09-02 Package smam October 1, 2016 Author Jun Yan and Vladimir Pozdnyakov
Package dng. November 22, 2017
Version 0.1.1 Date 2017-11-22 Title Distributions and Gradients Type Package Author Feng Li, Jiayue Zeng Maintainer Jiayue Zeng Depends R (>= 3.0.0) Package dng November 22, 2017 Provides
CS 361: Probability & Statistics
March 12, 2018 CS 361: Probability & Statistics Inference Binomial likelihood: Example Suppose we have a coin with an unknown probability of heads. We flip the coin 10 times and observe 2 heads. What can
Package RcmdrPlugin.RiskDemo
Type Package Package RcmdrPlugin.RiskDemo October 3, 2018 Title R Commander Plug-in for Risk Demonstration Version 2.0 Date 2018-10-3 Author Arto Luoma Maintainer R Commander plug-in to demonstrate various
Package mle.tools. February 21, 2017
Type Package Package mle.tools February 21, 2017 Title Expected/Observed Fisher Information and Bias-Corrected Maximum Likelihood Estimate(s) Version 1.0.0 License GPL (>= 2) Date 2017-02-21 Author Josmar
Package finiteruinprob
Type Package Package finiteruinprob December 30, 2016 Title Computation of the Probability of Ruin Within a Finite Time Horizon Version 0.6 Date 2016-12-30 Maintainer Benjamin Baumgartner
Package ESG. February 19, 2015
Type Package Title ESG - A package for asset projection Version 0.1 Date 2013-01-13 Package ESG February 19, 2015 Author Jean-Charles Croix, Thierry Moudiki, Frédéric Planchet, Wassim Youssef Maintainer
Package quantileda. R topics documented: February 2, 2016
Type Package Title Quantile Classifier Version 1.1 Date 2016-02-02 Author Package quantileda February 2, 2016 Maintainer Cinzia Viroli Code for centroid, median and quantile classifiers.
Package SimCorMultRes
Package SimCorMultRes February 15, 2013 Type Package Title Simulates Correlated Multinomial Responses Version 1.0 Date 2012-11-12 Author Anestis Touloumis Maintainer Anestis Touloumis
Package xva. November 26, 2016
Type Package Package xva November 26, 2016 Title Calculates Credit Risk Valuation Adjustments Version 0.8.1 Date 2016-11-19 Author Tasos Grivas Maintainer Calculates a number of valuation adjustments including
Package scenario. February 17, 2016
Type Package Package scenario February 17, 2016 Title Construct Reduced Trees with Predefined Nodal Structures Version 1.0 Date 2016-02-15 URL https://github.com/swd-turner/scenario Uses the neural gas
Package eesim. June 3, 2017
Type Package Package eesim June 3, 2017 Title Simulate and Evaluate Time Series for Environmental Epidemiology Version 0.1.0 Date 2017-06-02 Provides functions to create simulated time series of environmental
Package gmediation. R topics documented: June 27, Type Package
Type Package Package gmediation June 27, 2017 Title Mediation Analysis for Multiple and Multi-Stage Mediators Version 0.1.1 Author Jang Ik Cho, Jeffrey Albert Maintainer Jang Ik Cho Description
Maximum Likelihood Estimation
Maximum Likelihood Estimation EPSY 905: Fundamentals of Multivariate Modeling Online Lecture #6 EPSY 905: Maximum Likelihood In This Lecture The basics of maximum likelihood estimation Ø The engine that
The Lmoments Package
The Lmoments Package April 12, 2006 Version 1.1-1 Date 2006-04-10 Title L-moments and quantile mixtures Author Juha Karvanen Maintainer Juha Karvanen Depends R Suggests lmomco The
Package YieldCurve. July 2, 2014
Package YieldCurve July 2, 2014 Type Package Title Modelling and estimation of the yield curve Version 4.1 Date 2013-01-19 Depends R (>= 2.10, xts Author Sergio Salvino Guirreri Maintainer Sergio Salvino
The method of Maximum Likelihood.
Maximum Likelihood The method of Maximum Likelihood. In developing the least squares estimator - no mention of probabilities. Minimize the distance between the predicted linear regression and the observed
Fitting parametric distributions using R: the fitdistrplus package
Fitting parametric distributions using R: the fitdistrplus package M. L. Delignette-Muller - CNRS UMR 5558 R. Pouillot J.-B. Denis - INRA MIAJ user! 2009,10/07/2009 Background Specifying the probability
Package cbinom. June 10, 2018
Package cbinom June 10, 2018 Type Package Title Continuous Analog of a Binomial Distribution Version 1.1 Date 2018-06-09 Author Dan Dalthorp Maintainer Dan Dalthorp Description Implementation
Jaime Frade Dr. Niu Interest rate modeling
Interest rate modeling Abstract In this paper, three models were used to forecast short term interest rates for the 3 month LIBOR. Each of the models, regression time series, GARCH, and Cox, Ingersoll,
Package QRank. January 12, 2017
Type Package Package QRank January 12, 2017 Title A Novel Quantile Regression Approach for eqtl Discovery Version 1.0 Date 2016-12-25 Author Xiaoyu Song Maintainer Xiaoyu Song
mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs Fernihough, A. mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs Document Version: Publisher's PDF, also known
Package MultiSkew. June 24, 2017
Type Package Package MultiSkew June 24, 2017 Title Measures, Tests and Removes Multivariate Skewness Version 1.1.1 Date 2017-06-13 Author Cinzia Franceschini, Nicola Loperfido Maintainer Cinzia Franceschini
Background. opportunities. the transformation. probability. at the lower. data come
The T Chart in Minitab Statisti cal Software Background The T chart is a control chart used to monitor the amount of time between adverse events, where time is measured on a continuous scale. The T chart
Package EMT. February 19, 2015
Type Package Package EMT February 19, 2015 Title Exact Multinomial Test: Goodness-of-Fit Test for Discrete Multivariate data Version 1.1 Date 2013-01-27 Author Uwe Menzel Maintainer Uwe Menzel
Package PortRisk. R topics documented: November 1, Type Package Title Portfolio Risk Analysis Version Date
Type Package Title Portfolio Risk Analysis Version 1.1.0 Date 2015-10-31 Package PortRisk November 1, 2015 Risk Attribution of a portfolio with Volatility Risk Analysis. License GPL-2 GPL-3 Depends R (>=
Package jrvfinance. R topics documented: August 29, 2016
Package jrvfinance August 29, 2016 Title Basic Finance; NPV/IRR/Annuities/Bond-Pricing; Black Scholes Version 1.03 Implements the basic financial analysis functions similar to (but not identical to) what
Package GenOrd. September 12, 2015
Package GenOrd September 12, 2015 Type Package Title Simulation of Discrete Random Variables with Given Correlation Matrix and Marginal Distributions Version 1.4.0 Date 2015-09-11 Author Alessandro Barbiero,
Package tailloss. August 29, 2016
Package tailloss August 29, 2016 Title Estimate the Probability in the Upper Tail of the Aggregate Loss Distribution Set of tools to estimate the probability in the upper tail of the aggregate loss distribution
Package GCPM. December 30, 2016
Type Package Title Generalized Credit Portfolio Model Version 1.2.2 Date 2016-12-29 Author Kevin Jakob Package GCPM December 30, 2016 Maintainer Kevin Jakob Analyze the
Package cumstats. R topics documented: January 16, 2017
Type Package Title Cumulative Descriptive Statistics Version 1.0 Date 2017-01-13 Author Arturo Erdely and Ian Castillo Package cumstats January 16, 2017 Maintainer Arturo Erdely
Package PairTrading. February 15, 2013
Package PairTrading February 15, 2013 Type Package Title classical pair trading based on cointegration in finance Version 1.1 Date 2012-03-24 Author Maintainer Shinichi Takayanagi
Risk Analysis. å To change Benchmark tickers:
Property Sheet will appear. The Return/Statistics page will be displayed. 2. Use the five boxes in the Benchmark section of this page to enter or change the tickers that will appear on the Performance
The actuar Package. March 24, bstraub... 1 hachemeister... 3 panjer... 4 rearrangepf... 5 simpf Index 8. Buhlmann-Straub Credibility Model
The actuar Package March 24, 2006 Type Package Title Actuarial functions Version 0.1-3 Date 2006-02-16 Author Vincent Goulet, Sébastien Auclair Maintainer Vincent Goulet
Package stable. February 6, 2017
Version 1.1.2 Package stable February 6, 2017 Title Probability Functions and Generalized Regression Models for Stable Distributions Depends R (>= 1.4), rmutil Description Density, distribution, quantile
Package semsfa. April 21, 2018
Type Package Package semsfa April 21, 2018 Title Semiparametric Estimation of Stochastic Frontier Models Version 1.1 Date 2018-04-18 Author Giancarlo Ferrara and Francesco Vidoli Maintainer Giancarlo Ferrara
Ornstein-Uhlenbeck Theory
Beatrice Byukusenge Department of Technomathematics Lappeenranta University of technology January 31, 2012 Definition of a stochastic process Let (Ω,F,P) be a probability space. A stochastic process is
Package multiassetoptions
Package multiassetoptions February 20, 2015 Type Package Title Finite Difference Method for Multi-Asset Option Valuation Version 0.1-1 Date 2015-01-31 Author Maintainer Michael Eichenberger
Supplementary material for the paper Identifiability and bias reduction in the skew-probit model for a binary response
Supplementary material for the paper Identifiability and bias reduction in the skew-probit model for a binary response DongHyuk Lee and Samiran Sinha Department of Statistics, Texas A&M University, College
Package ratesci. April 21, 2017
Type Package Package ratesci April 21, 2017 Title Confidence Intervals for Comparisons of Binomial or Poisson Rates Version 0.2-0 Date 2017-04-21 Author Pete Laud [aut, cre] Maintainer Pete Laud
Calibration of Ornstein-Uhlenbeck Mean Reverting Process
Calibration of Ornstein-Uhlenbeck Mean Reverting Process Description The model is used for calibrating an Ornstein-Uhlenbeck (OU) process with mean reverting drift. The process can be considered to be
Modeling the volatility of FTSE All Share Index Returns
MPRA Munich Personal RePEc Archive Modeling the volatility of FTSE All Share Index Returns Bayraci, Selcuk University of Exeter, Yeditepe University 27. April 2007 Online at http://mpra.ub.uni-muenchen.de/28095/
Package pglm. November 2, 2017
Version 0.2-1 Date 2017-10-29 Title Panel Generalized Linear Models Depends R (>= 2.10), maxlik, plm Imports statmod Suggests lmtest, car Package pglm November 2, 2017 Estimation of panel models for glm-like
3 ˆθ B = X 1 + X 2 + X 3. 7 a) Find the Bias, Variance and MSE of each estimator. Which estimator is the best according
STAT 345 Spring 2018 Homework 9 - Point Estimation Name: Please adhere to the homework rules as given in the Syllabus. 1. Mean Squared Error. Suppose that X 1, X 2 and X 3 are independent random variables
Package FADA. May 20, 2016
Type Package Package FADA May 20, 2016 Title Variable Selection for Supervised Classification in High Dimension Version 1.3.2 Date 2016-05-12 Author Emeline Perthame (INRIA, Grenoble, France), Chloe Friguet
Package xva. January 20, 2016
Type Package Package xva January 20, 2016 Title Calculates Credit Risk Valuation Adjustments Version 0.8 Date 2016-01-17 Author Tasos Grivas Maintainer Calculates a number of valuation adjustments including
Package RTDAmeritrade
Package RTDAmeritrade February 15, 2013 Version 0.0.1 Date 2011-06-4 Title RTDAmeritrade Author Theodore Van Rooy Maintainer Theodore Van Rooy Depends R (>= 2.9.1), xts, zoo,
User Guide of GARCH-MIDAS and DCC-MIDAS MATLAB Programs
User Guide of GARCH-MIDAS and DCC-MIDAS MATLAB Programs 1. Introduction The GARCH-MIDAS model decomposes the conditional variance into the short-run and long-run components. The former is a mean-reverting
Key Features Asset allocation, cash flow analysis, object-oriented portfolio optimization, and risk analysis
Financial Toolbox Analyze financial data and develop financial algorithms Financial Toolbox provides functions for mathematical modeling and statistical analysis of financial data. You can optimize portfolios
LESSON 7 INTERVAL ESTIMATION SAMIE L.S. LY
LESSON 7 INTERVAL ESTIMATION SAMIE L.S. LY 1 THIS WEEK S PLAN Part I: Theory + Practice ( Interval Estimation ) Part II: Theory + Practice ( Interval Estimation ) z-based Confidence Intervals for a Population
QQ PLOT Yunsi Wang, Tyler Steele, Eva Zhang Spring 2016
QQ PLOT INTERPRETATION: Quantiles: QQ PLOT Yunsi Wang, Tyler Steele, Eva Zhang Spring 2016 The quantiles are values dividing a probability distribution into equal intervals, with every interval having
Package BatchGetSymbols
Package BatchGetSymbols January 22, 2018 Title Downloads and Organizes Financial Data for Multiple Tickers Version 2.0 Makes it easy to download a large number of trade data from Yahoo or Google Finance.
Gov 2001: Section 5. I. A Normal Example II. Uncertainty. Gov Spring 2010
Gov 2001: Section 5 I. A Normal Example II. Uncertainty Gov 2001 Spring 2010 A roadmap We started by introducing the concept of likelihood in the simplest univariate context one observation, one variable.
Package Strategy. R topics documented: August 24, Type Package
Type Package Package Strategy August 24, 2017 Title Generic Framework to Analyze Trading Strategies Version 1.0.1 Date 2017-08-21 Author Julian Busch Maintainer Julian Busch Depends R (>=
P2.T5. Tuckman Chapter 9. Bionic Turtle FRM Video Tutorials. By: David Harper CFA, FRM, CIPM
P2.T5. Tuckman Chapter 9 Bionic Turtle FRM Video Tutorials By: David Harper CFA, FRM, CIPM Note: This tutorial is for paid members only. You know who you are. Anybody else is using an illegal copy and
Package uqr. April 18, 2017
Type Package Title Unconditional Quantile Regression Version 1.0.0 Date 2017-04-18 Package uqr April 18, 2017 Author Stefano Nembrini Maintainer Stefano Nembrini
Package conf. November 2, 2018
Type Package Package conf November 2, 2018 Title Visualization and Analysis of Statistical Measures of Confidence Version 1.4.0 Maintainer Christopher Weld Imports graphics, stats,
Derivatives Options on Bonds and Interest Rates. Professor André Farber Solvay Business School Université Libre de Bruxelles
Derivatives Options on Bonds and Interest Rates Professor André Farber Solvay Business School Université Libre de Bruxelles Caps Floors Swaption Options on IR futures Options on Government bond futures
To apply SP models we need to generate scenarios which represent the uncertainty IN A SENSIBLE WAY, taking into account
Scenario Generation To apply SP models we need to generate scenarios which represent the uncertainty IN A SENSIBLE WAY, taking into account the goal of the model and its structure, the available information,
Crashcourse Interest Rate Models
Crashcourse Interest Rate Models Stefan Gerhold August 30, 2006 Interest Rate Models Model the evolution of the yield curve Can be used for forecasting the future yield curve or for pricing interest rate
THE IMPACT OF BANKING RISKS ON THE CAPITAL OF COMMERCIAL BANKS IN LIBYA
THE IMPACT OF BANKING RISKS ON THE CAPITAL OF COMMERCIAL BANKS IN LIBYA Azeddin ARAB Kastamonu University, Turkey, Institute for Social Sciences, Department of Business Abstract: The objective of this
ESGtoolkit, tools for Economic Scenarios Generation
ESGtoolkit, tools for Economic Scenarios Generation Thierry Moudiki 13th June 2014 Contents 1 Overview 1 1.1 Context................................. 1 1.2 simdiff................................. 2 1.3
Package optimstrat. September 10, 2018
Type Package Title Choosing the Sample Strategy Version 1.1 Date 2018-09-04 Package optimstrat September 10, 2018 Author Edgar Bueno Maintainer Edgar Bueno
Mixed Logit or Random Parameter Logit Model
Mixed Logit or Random Parameter Logit Model Mixed Logit Model Very flexible model that can approximate any random utility model. This model when compared to standard logit model overcomes the Taste variation
A UNIFIED APPROACH FOR PROBABILITY DISTRIBUTION FITTING WITH FITDISTRPLUS
A UNIFIED APPROACH FOR PROBABILITY DISTRIBUTION FITTING WITH FITDISTRPLUS M-L. Delignette-Muller 1, C. Dutang 2,3 1 VetAgro Sud Campus Vétérinaire - Lyon 2 ISFA - Lyon, 3 AXA GRM - Paris, 1/15 12/08/2011
Package UnifQuantReg
Package UnifQuantReg May 13, 2014 Type Package Title Uniformly Adaptive-LASSO Quantile Regression Version 1.0 Date 2014-05-12 Author Limin Peng, Jinfeng Xu and Qi Zheng Maintainer Qi Zheng
Package PortfolioOptim
Package PortfolioOptim Title Small/Large Sample Portfolio Optimization Version 1.0.3 April 20, 2017 Description Two functions for financial portfolio optimization by linear programming are provided. One
Simulated Multivariate Random Effects Probit Models for Unbalanced Panels
Simulated Multivariate Random Effects Probit Models for Unbalanced Panels Alexander Plum 2013 German Stata Users Group Meeting June 7, 2013 Overview Introduction Random Effects Model Illustration Simulated
Interfraternity Council Budget January 1, December 31, 2018 Income Budget Actual IFC Dues
Interfraternity Council Budget January 1, 2018 - December 31, 2018 Income Budget Actual IFC Dues Chapter Dues Spring 2017 $12,000.00 $0.00 Colony Dues Spring 2017 $500.00 $0.00 Chapter Dues Fall 2017 $12,000.00
MFE/3F Questions Answer Key
MFE/3F Questions Download free full solutions from www.actuarialbrew.com, or purchase a hard copy from www.actexmadriver.com, or www.actuarialbookstore.com. Chapter 1 Put-Call Parity and Replication 1.01
MODELLING 1-MONTH EURIBOR INTEREST RATE BY USING DIFFERENTIAL EQUATIONS WITH UNCERTAINTY
Applied Mathematical and Computational Sciences Volume 7, Issue 3, 015, Pages 37-50 015 Mili Publications MODELLING 1-MONTH EURIBOR INTEREST RATE BY USING DIFFERENTIAL EQUATIONS WITH UNCERTAINTY J. C.
The termstrc Package
The termstrc Package July 9, 2007 Type Package Title Term Structure and Credit Spread Estimation Version 1.0 Date 2006-12-15 Author Maintainer Robert Ferstl Depends R (>=
Exam 2 Spring 2015 Statistics for Applications 4/9/2015
18.443 Exam 2 Spring 2015 Statistics for Applications 4/9/2015 1. True or False (and state why). (a). The significance level of a statistical test is not equal to the probability that the null hypothesis
Package BatchGetSymbols
Package BatchGetSymbols November 25, 2018 Title Downloads and Organizes Financial Data for Multiple Tickers Version 2.3 Makes it easy to download a large number of trade data from Yahoo Finance. Date 2018-11-25
Package fmdates. January 5, 2018
Type Package Title Financial Market Date Calculations Version 0.1.4 Package fmdates January 5, 2018 Implements common date calculations relevant for specifying the economic nature of financial market contracts
MgtOp S 215 Chapter 8 Dr. Ahn
MgtOp S 215 Chapter 8 Dr. Ahn An estimator of a population parameter is a rule that tells us how to use the sample values,,, to estimate the parameter, and is a statistic. An estimate is the value obtained
Operational Risk Modeling
Operational Risk Modeling RMA Training (part 2) March 213 Presented by Nikolay Hovhannisyan Nikolay_hovhannisyan@mckinsey.com OH - 1 About the Speaker Senior Expert McKinsey & Co Implemented Operational
Joseph O. Marker Marker Actuarial Services, LLC and University of Michigan CLRS 2011 Meeting. J. Marker, LSMWP, CLRS 1
Joseph O. Marker Marker Actuarial Services, LLC and University of Michigan CLRS 2011 Meeting J. Marker, LSMWP, CLRS 1 Expected vs Actual Distribu3on Test distribu+ons of: Number of claims (frequency) Size
Stat 5303 (Oehlert): Power and Sample Size 1
Stat 5303 (Oehlert): Power and Sample Size 1 Cmd> # The Stat5303 package includes two functions of use here: power.anova.test() and sample.size.anova(). You won t be surprised to learn that the first computes
A NEW POINT ESTIMATOR FOR THE MEDIAN OF GAMMA DISTRIBUTION
Banneheka, B.M.S.G., Ekanayake, G.E.M.U.P.D. Viyodaya Journal of Science, 009. Vol 4. pp. 95-03 A NEW POINT ESTIMATOR FOR THE MEDIAN OF GAMMA DISTRIBUTION B.M.S.G. Banneheka Department of Statistics and
Lecture 2 - Calibration of interest rate models and optimization
- Calibration of interest rate models and optimization Elisabeth Larsson Uppsala University, Uppsala, Sweden March 2015 E. Larsson, March 2015 (1 : 23) Introduction to financial instruments Introduction
Parameter estimation in SDE:s
Lund University Faculty of Engineering Statistics in Finance Centre for Mathematical Sciences, Mathematical Statistics HT 2011 Parameter estimation in SDE:s This computer exercise concerns some estimation
THE UNIVERSITY OF TEXAS AT AUSTIN McCombs School of Business
THE UNIVERSIT OF TEXAS AT AUSTIN McCombs School of Business STA 37.5 Tom Shively SIMPLE EXPONENTIAL SMOOTHING MODELS The statistical model for simple exponential smoothing is t = M t- + ε t ε t iid N(0,
Tests for One Variance
Chapter 65 Introduction Occasionally, researchers are interested in the estimation of the variance (or standard deviation) rather than the mean. This module calculates the sample size and performs power
Visual fixations and the computation and comparison of value in simple choice SUPPLEMENTARY MATERIALS
Visual fixations and the computation and comparison of value in simple choice SUPPLEMENTARY MATERIALS Ian Krajbich 1 Carrie Armel 2 Antonio Rangel 1,3 1. Division of Humanities and Social Sciences, California
2. Copula Methods Background
1. Introduction Stock futures markets provide a channel for stock holders potentially transfer risks. Effectiveness of such a hedging strategy relies heavily on the accuracy of hedge ratio estimation.
Estimating HIV transmission rates with rcolgem
Estimating HIV transmission rates with rcolgem Erik M Volz December 5, 2014 This vignette will demonstrate how to use a coalescent models as described in [2] to estimate transmission rate parameters given
Statistical Models and Methods for Financial Markets
Tze Leung Lai/ Haipeng Xing Statistical Models and Methods for Financial Markets B 374756 4Q Springer Preface \ vii Part I Basic Statistical Methods and Financial Applications 1 Linear Regression Models
User s Guide for the Matlab Library Implementing Closed Form MLE for Diffusions
User s Guide for the Matlab Library Implementing Closed Form MLE for Diffusions Yacine Aït-Sahalia Department of Economics and Bendheim Center for Finance Princeton University and NBER This Version: July
Market Risk Analysis Volume II. Practical Financial Econometrics
Market Risk Analysis Volume II Practical Financial Econometrics Carol Alexander John Wiley & Sons, Ltd List of Figures List of Tables List of Examples Foreword Preface to Volume II xiii xvii xx xxii xxvi
A Model Calibration. 1 Earlier versions of this dataset have, for example, been used by Krohmer et al. (2009), Cumming et al.
A Model Calibration This appendix illustrates the model calibration. Where possible, baseline parameters are chosen in the following such that they correspond to an investment in an average buyout fund.
Phd Program in Transportation. Transport Demand Modeling. Session 11
Phd Program in Transportation Transport Demand Modeling João de Abreu e Silva Session 11 Binary and Ordered Choice Models Phd in Transportation / Transport Demand Modelling 1/26 Heterocedasticity Homoscedasticity