'Financial time series' presentation slideshows

Neural networks

1.07k views • 91 slides

Chapter 2. Unobserved Component models

Chapter 2. Unobserved Component models. Esther Ruiz 2006-2007 PhD Program in Business Administration and Quantitative Analysis Financial Econometrics. 2.1 Description and properties.

1.58k views • 62 slides

STAT 497 LECTURE NOTES 5

STAT 497 LECTURE NOTES 5. UNIT ROOT TESTS. UNIT ROOTS IN TIME SERIES MODELS. Shock is usually used to describe an unexpected change in a variable or in the value of the error terms at a particular time period. When we have a stationary system, effect of a shock will die out gradually.

794 views • 61 slides

A Generalized Model for Financial Time Series Representation and Prediction

A Generalized Model for Financial Time Series Representation and Prediction. Author: Depei Bao Presenter: Liao Shu Acknowledgement: Some figures in this presentation are obtained from the paper. Outline of the Presentation. Introduction

548 views • 39 slides

Applications of RQA to Financial Time Series

Applications of RQA to Financial Time Series. F. Strozzi 1 , J. M. Zald í var 2 and J. P. Zbilut 3. 1 Quantitative Methods Institute, Carlo Cattaneo University, Castellanza (VA), Italy 2 European Commission, DG Joint Research Centre, IES, Ispra (VA), Italy

519 views • 35 slides

Introduction

Introduction. 1. Introduction to Energy Trading Exchange and OTC trading Traded Contracts Settlement Fundamentals 2. Energy Contracts Time Series Specifics 3. Energy Trading: Financial and Risk Management. (. ). Power Energy : Financial and Risk Management.

459 views • 34 slides

Time-series data analysis

Time-series data analysis. Why deal with sequential data?. Because all data is sequential ? All data items arrive in the data store in some order Examples transaction data documents and words In some (or many) cases the order does not matter In many cases the order is of interest.

373 views • 28 slides

Speaker : Kozlov Alexander A.

Financial Academy under the Government of the Russian Federation. Report. Singular spectrum analysis for a forecasting of financial time series. Speaker : Kozlov Alexander A. Moscow - 2009. Content list:. Introduction to nonlinear dynamics approach

377 views • 23 slides

Ch8 Time Series Modeling

Ch8 Time Series Modeling. 1. Autoregressive Integrated Moving Average Model (ARIMA Model), popularly known as the Box-Jenkins Methodology 2. Vector Autoregression Model (VAR Model)

610 views • 22 slides

Persistence and nonlinearities in Economics and Finance

329 views • 22 slides

Master of Science in Financial Engineering

Master of Science in Financial Engineering. Program Overview. Session Outline. Introduction (FE Faculty and Staff) What exactly is financial engineering? An Exercise in Expectations Program Structure Dual Degree Options Program Administration Registration Questions and Issues Bloomberg

422 views • 18 slides

Alejandro Fonseca EGADE Business School, Campus Monterrey afonseca@itesm.mx

Alejandro Fonseca EGADE Business School, Campus Monterrey afonseca@itesm.mx Roberto J. Santillan -Salgado EGADE Business School, Campus Monterrey roberto.santillan@itesm.mx. Increasing role of foreign exchange in corporate decision making has become a popular topic in modern economies.

323 views • 14 slides

Stat 131 Time Series Analysis: Introductory Examples

260 views • 13 slides

KYIV SCHOOL OF ECONOMICS Financial Econometrics (2nd part): Introduction to Financial Time Series

KYIV SCHOOL OF ECONOMICS Financial Econometrics (2nd part): Introduction to Financial Time Series May 2011 Instructor: Maksym Obrizan Lecture notes III. # 2. In this lecture: Review the basic ideas behind Value at Risk (VaR) calculations based on various time series models: 1. RiskMetrics

254 views • 10 slides

Financial Time Series I/Methods of Statistical Prediction

74 views • 7 slides