Analyse complexe - Université de Montréal

Analyse complexe Cours et exercices corrig es Andr e Giroux D epartement de math ematiques et statistique Universit e de Montr eal 2013

Analysis of Financial Time Series

2. Linear Time Series Analysis and Its Applications 24 2.1 Stationarity, 25 2.2 Correlation and Autocorrelation Function, 25 2.3 White Noise and Linear Time Series, 31 2.4 Simple Autoregressive Models, 32 2.4.1 Properties of AR Models, 33 2.4.2 Identifying AR Models in Practice, 40 2.4.3 Goodness of Fit, 46 2.4.4 Forecasting, 47 vii

  Series, Time, Financial, Time series, Forecasting, Financial time series

3. Demnand Forecasting

Quantitative Methods of Forecasting 1.Causal –There is a causal relationship between the variable to be forecast and another variable or a series of variables. (Demand is based on the policy, e.g. cement, and build material. 2.Time series –The variable to be forecast has behaved according to a specific pattern in the past

  Series, Time, Time series, Forecasting

(Examination Scheme) (Written Examination)

analysis, Interest & time value of money, Basic methodology of engineering economics, cost and benefit analysis, risk analysis, investment decisions, demand analysis and sales forecasting, 6.2 Basic knowledge of trial balance & Balance Sheet, income statements, revenue and capital expenditure, budgeting and capitalization,

  Seal, Time, Forecasting, Sales forecasting

03 Time series with trend and seasonality components

Determining if a time series has a seasonal component Some examples of more pronounced seasonality: g = 1 Time z 0 40 80 120-4-2 0 2 4 0 5 10 15 20-0.5 0.0 0.5 1.0 Lag ACF Series z g = 0.83 Time z 0 40 80 120-4 4 0 5 10 15 20-0.5 1.0 Lag ACF Series z g = 0.5 Time z 0 40 80 120-3-1 1 2 3 0 5 10 15 20-0.5 0.0 0.5 1.0 Lag ACF Series z g = 0.25 ...

  Series, Time, Time series

Applying fundamental & technical analysis in stock investing

• Time periods measure include: Most Recent Quarter (MRQ) and Trailing 12 Months (TTM) ... Trend identification is the primary tool in price forecasting ...

  Time, Forecasting

FORECASTING

Forecasting •What is Forecasting? • Determining Future Events Based on Historical Facts and Data •Some Thoughts on Forecasts • Forecasts Tend to Be Wrong! • Forecasts Can Be Biased! (Marketing, Sales, etc.) • Forecasts Tend to Be Better for Near Future •So, Why Forecast? • Better to Have “Educated Guess” About Future Than to Not Forecast At All!

  Seal, Forecasting

Understanding Interest Rate Swap Math & Pricing

dealer’s pricing and sales con ventions, the relevant indices needed to determine pric­ ing, formulas for and examples of pricing, and a review of variables that have an affect on market and termination pricing of an existing swap. 1 . Basic Interest Rate Swap Mechanics . An interest . rate swap is a . contractual arrangement be­

  Seal