Reporter: You-Cheng Luo 2011/01/04 Spikes of the Electricity.

Slides:



Advertisements
Similar presentations
Chapter 3 Properties of Random Variables
Advertisements

Chapter 4: Basic Estimation Techniques
Algorithm for Fast Statistical Timing Analysis Jakob Salzmann, Frank Sill, Dirk Timmermann SOC 2007, Nov ‘07, Tampere, Finland Institute of Applied Microelectronics.
Copula Regression By Rahul A. Parsa Drake University &
Chapter 3 Brownian Motion 報告者:何俊儒.
The Simple Linear Regression Model: Specification and Estimation
Understanding the Accuracy of Assembly Variation Analysis Methods ADCATS 2000 Robert Cvetko June 2000.
HMM-BASED PATTERN DETECTION. Outline  Markov Process  Hidden Markov Models Elements Basic Problems Evaluation Optimization Training Implementation 2-D.
Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics, 9e Managerial Economics Thomas Maurice.
Part 4 b Forward-Backward Algorithm & Viterbi Algorithm CSE717, SPRING 2008 CUBS, Univ at Buffalo.
Descriptive statistics Experiment  Data  Sample Statistics Experiment  Data  Sample Statistics Sample mean Sample mean Sample variance Sample variance.
Environmentally Conscious Design & Manufacturing (ME592) Date: May 5, 2000 Slide:1 Environmentally Conscious Design & Manufacturing Class 25: Probability.
Simultaneous Forecasting of Non-stationary conditional Mean & Variance Speaker: Andrey Torzhkov September 25 th, 2006.
Chapter 3 homework Numbers 6, 7, 12 Review session: Monday 6:30-7:30 Thomas 324.
Topics: Regression Simple Linear Regression: one dependent variable and one independent variable Multiple Regression: one dependent variable and two or.
Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices Jonathan Stroud, Wharton, U. Pennsylvania Stern-Wharton Conference on.
Aaker, Kumar, Day Seventh Edition Instructor’s Presentation Slides
Computer vision: models, learning and inference
The CGMYmodel Finance seminar by Mari Hodnekvam supervised by Prof.Korn.
1 Assessment of Imprecise Reliability Using Efficient Probabilistic Reanalysis Farizal Efstratios Nikolaidis SAE 2007 World Congress.
FRANCIS A. LONGSTAFF and ASHLEY W. WANG AUGUST 2004 Reporter: You-cheng Luo.
Ewa Lukasik - Jakub Lawik - Juan Mojica - Xiaodong Xu.
Advanced Risk Management I Lecture 6 Non-linear portfolios.
Random Sampling, Point Estimation and Maximum Likelihood.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 4 Curve Fitting.
Elements of Financial Risk Management Second Edition © 2012 by Peter Christoffersen 1 Distributions and Copulas for Integrated Risk Management Elements.
Book Review: ‘Energy Derivatives: Pricing and Risk Management’ by Clewlow and Strickland, 2000 Chapter 3: Volatility Estimation in Energy Markets Anatoliy.
1 G Lect 8b G Lecture 8b Correlation: quantifying linear association between random variables Example: Okazaki’s inferences from a survey.
Applied Quantitative Analysis and Practices LECTURE#23 By Dr. Osman Sadiq Paracha.
Hedging with Forward & Futures Risk Management Prof. Ali Nejadmalayeri, Dr N a.k.a. “Dr N”
Simulation of the matrix Bingham-von Mises- Fisher distribution, with applications to multivariate and relational data Discussion led by Chunping Wang.
Generalized Linear Models All the regression models treated so far have common structure. This structure can be split up into two parts: The random part:
CHAPTER 3 Model Fitting. Introduction Possible tasks when analyzing a collection of data points: Fitting a selected model type or types to the data Choosing.
Managerial Economics Demand Estimation & Forecasting.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
Spatial Analysis & Geostatistics Methods of Interpolation Linear interpolation using an equation to compute z at any point on a triangle.
Mixture of Gaussians This is a probability distribution for random variables or N-D vectors such as… –intensity of an object in a gray scale image –color.
CS Statistical Machine learning Lecture 24
The Simple Linear Regression Model: Specification and Estimation ECON 4550 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s.
1 Ka-fu Wong University of Hong Kong EViews Commands that are useful for Assignment #2.
INTRODUCTORY MATHEMATICAL ANALYSIS For Business, Economics, and the Life and Social Sciences  2011 Pearson Education, Inc. Chapter 16 Continuous Random.
Sampling and estimation Petter Mostad
Estimation of covariance matrix under informative sampling Julia Aru University of Tartu and Statistics Estonia Tartu, June 25-29, 2007.
Statistics 3502/6304 Prof. Eric A. Suess Chapter 4.
Investigating a Physically-Based Signal Power Model for Robust Low Power Wireless Link Simulation Tal Rusak, Philip Levis MSWIM 2008.
S TOCHASTIC M ODELS L ECTURE 4 P ART II B ROWNIAN M OTIONS Nan Chen MSc Program in Financial Engineering The Chinese University of Hong Kong (Shenzhen)
Review of Statistical Terms Population Sample Parameter Statistic.
The role of market impact and investor behavior on fund flows Yoni and Doyne 9/2/09.
The Unscented Particle Filter 2000/09/29 이 시은. Introduction Filtering –estimate the states(parameters or hidden variable) as a set of observations becomes.
Chi Square Test for Goodness of Fit Determining if our sample fits the way it should be.
Cycle Detection and Removal in Electricity Markets “Lunch at Lab” Presentation Matt Lyle Department of mathematics&statistics University of Calgary, Alberta.
MEGN 537 – Probabilistic Biomechanics Ch.5 – Determining Distributions and Parameters from Observed Data Anthony J Petrella, PhD.
Statistics 350 Lecture 2. Today Last Day: Section Today: Section 1.6 Homework #1: Chapter 1 Problems (page 33-38): 2, 5, 6, 7, 22, 26, 33, 34,
Multiple Regression Reference: Chapter 18 of Statistics for Management and Economics, 7 th Edition, Gerald Keller. 1.
Forecasting. Model with indicator variables The choice of a forecasting technique depends on the components identified in the time series. The techniques.
Chapter 4: Basic Estimation Techniques
Estimating the Value of a Parameter Using Confidence Intervals
Chapter 4 Basic Estimation Techniques
Basic Estimation Techniques
Ch3: Model Building through Regression
Basic Estimation Techniques
CHAPTER 29: Multiple Regression*
Linear Regression.
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Sampling Distribution
Sampling Distribution
Confidence Intervals Chapter 10 Section 1.
Regression in the 21st Century
Regression Assumptions
Regression Assumptions
Presentation transcript:

Reporter: You-Cheng Luo 2011/01/04 Spikes of the Electricity

Outline Review on the data ( lesale.html) lesale.html Review on Propose Methods Parameter Estimation Result Conclusion & Future Work

Review on the data The first data is the delivery date from 2009/01/05 to 2010/11/15 in the Ercotsouth which is a main trade hub in Texas. The second data is the delivery date form 2009/01/07 to 2010/11/10 in the PJM West which is a main trade hub in Pennsylvania. The prices are computed by WtdAvgPrice $/MWh, where the WtdAvgPrice is

Review on the Proposed Methods Geman and Roncoroni (2002) introduce a jump-reversion model for electricity spot prices, namely the representation of S(t), by:

Selection of the Structural Element Mean trend The first term may be viewed as a fixed cost linked to the production of power. The second one drives the long-run linear trend in the total production cost. The overall effect of the third and fourth terms is a periodic path displaying two maxima per year, of possibly different magnitudes.

Selection of the Structural Element A probability distribution for the jump size. We select a truncated version of an exponential density with parameter θ 3 : where ψ represents an upper bound for the absolute value of price changes.

Model Parameter Estimation The constant Brownian volatility over observation dates 0 = t 0 < t 1 <…< t n = t can be obtained as : where each summand represents the square of the continuous part of observed price variations (in a logarithmic scale) between consecutive days t i and t i+1

Model Parameter Estimation

Simulation Algorithm Where N is sample from a standard normal distribution and J is sample from p( ‧, θ 3 ) for some k=1, …,n

Result PJM Market

Result Ercotsouth

Conclusion and Future Work My simulation is not fitting well about the spikes of the data because of my experiences. Maybe we can try another models to fit the electricity prices, and then introduce the copula to figure out the dependency between other variables and electricity prices.

References U.S. Energy Information Administration Independent Statistic and Analysis esale.html Roncoroni-Geman(2002) ;The Journal of Business Understanding the Fine Structure of Electricity Prices oroni-Geman.pdf