Đang chuẩn bị liên kết để tải về tài liệu:
Learn Financial Modeling Markets Using Visual Basic NET_3

Tham khảo tài liệu 'learn financial modeling markets using visual basic net_3', công nghệ thông tin, hệ điều hành phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Control Structures 73 use in financial markets are based on the assumption of continuous time it is more intuitive to examine the entire time period rather than simply the ends. The most well known of the extreme value estimators have been proposed by Parkinson 1980 and Garman and Klass 1980 cited in Nelken 1997 Chap. 1 . The Parkinson s equation uses the intraperiod high and low thusly Op - 0.601 The Garman-Klass estimator which uses the intraperiod high and low as well as the open and close data has the form ƠGK Vln 2ln 2 - 1 ln CA 2 Li L W jV oJ Notice that these equations represent an estimate of the one-period historical volatility of the underlying symbol. You may notice however that neither of these models takes into account gaps either up or down from the previous day s close. Volatility that happens overnight will not be accounted for in either of these models. For this and other reasons there are dozens of derivatives of these two extreme value estimators currently in use. We will not examine any of them beyond the two standard models presented. These Parkinson and Garman-Klass models estimate past volatility. They do not forecast future volatility. Forecasting volatility is its own subject and is the topic of literally hundreds of research papers and books. The most popular models for forecasting volatility are the GARCH generalized autoregressive conditional heteroscedasticity family. Dozens of variations of GARCH models have been proposed for forecasting volatility based on the assumption that returns are generated by a random process with time-varying and meanreverting volatility Alexander 2001 p. 65 . That is in financial markets periods of low volatility tend to be followed by periods of low volatility but are interspersed with periods of high volatility. The most commonly referenced GARCH model for forecasting 74 Introduction to VB.NET variance is GARCH 1 1 s2 1 - 1 - a - b V ar2 b s2 5.1 and s j - V a b 7 -1 s2 1 - V 5.2 where a and b are .