Isbn: 9780471974642 - nonlinear modelling of high frequency financial time series (financial economics and quantitative analysis series) (23 resultados)

Hardcover. Condición: Near Fine. A near fine, clean and tight hardcover copy. No writing, no ownership signatures. A very nice copy. = WE BOX AND SHIP ALL BOOKS WITH USPS TRACKING. = WE HAVE BEEN BUYING AND SELLING USED BOOKS FOR OVER 34 YEARS.

hardcover. Condición: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority.

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:9780471974642.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: new.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New. In English.

Condición: As New. Unread book in perfect condition.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. pp. 332.

Condición: New. This text focuses on the issue of non-linear modelling of high frequency financial data. Non-linearity refers to situations in which there is a high degree of apparent randomness to the way in which a particular financial measure, price, interest rate, or exchange rate moves with time. Editor(s): Dunis, Christian; Zhou, Bin. Series: Wiley series in financial economics & quantitative analysis. Num Pages: 332 pages, illustrations. BIC Classification: KFF; PBWH. Category: (P) Professional & Vocational. Dimension: 239 x 161 x 29. Weight in Grams: 672. . 1998. 1st Edition. Hardcover. . . . .…

Condición: New. pp. 332.

Gebunden. Condición: New. Nonlinear Modelling of High Frequency Financial Time Series Edited by Christian Dunis and Bin Zhou In the competitive and risky environment of today s financial markets, daily prices and models based upon low frequency price series data do not provide the l.

Condición: New. This text focuses on the issue of non-linear modelling of high frequency financial data. Non-linearity refers to situations in which there is a high degree of apparent randomness to the way in which a particular financial measure, price, interest rate, or exchange rate moves with time. Editor(s): Dunis, Christian; Zhou, Bin. Series: Wiley series in financial economics & quantitative analysis. Num Pages: 332 pages, illustrations. BIC Classification: KFF; PBWH. Category: (P) Professional & Vocational. Dimension: 239 x 161 x 29. Weight in Grams: 672. . 1998. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.…

Condición: New.

Buch. Condición: Neu. Neuware - Nonlinear Modelling of High Frequency Financial Time Series Edited by Christian Dunis and Bin Zhou In the competitive and risky environment of today s financial markets, daily prices and models based upon low frequency price series data do not provide the level of accuracy required by traders and a growing number of risk managers. To improve results, more and more researchers and practitioners are turning to high frequency data. Nonlinear Modelling of High Frequency Financial Time Series presents the latest developments and views of leading international researchers and market practitioners, in modelling high frequency data in finance. Combining both nonlinear modelling and intraday data for financial markets, the editors provide a fascinating foray into this extremely popular discipline. This book evolves around four major themes. The first introductory section focuses on high frequency financial data. The second part examines the exact nature of the time series considered: several linearity tests are presented and applied and their modelling implications assessed. The third and fourth parts are dedicated to modelling and forecasting these financial time series.…

Hardcover. Condición: Brand New. 1st edition. 332 pages. 9.50x6.50x1.00 inches. In Stock.

Condición: New.

Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 654.

Hardcover. Condición: new. Hardcover. Nonlinear Modelling of High Frequency Financial Time Series Edited by Christian Dunis and Bin Zhou In the competitive and risky environment of today's financial markets, daily prices and models based upon low frequency price series data do not provide the level of accuracy required by traders and a growing number of risk managers. To improve results, more and more researchers and practitioners are turning to high frequency data. Nonlinear Modelling of High Frequency Financial Time Series presents the latest developments and views of leading international researchers and market practitioners, in modelling high frequency data in finance. Combining both nonlinear modelling and intraday data for financial markets, the editors provide a fascinating foray into this extremely popular discipline. This book evolves around four major themes. The first introductory section focuses on high frequency financial data. The second part examines the exact nature of the time series considered: several linearity tests are presented and applied and their modelling implications assessed. The third and fourth parts are dedicated to modelling and forecasting these financial time series. This text focuses on the issue of non-linear modelling of high frequency financial data. Non-linearity refers to situations in which there is a high degree of apparent randomness to the way in which a particular financial measure, price, interest rate, or exchange rate moves with time. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condición: Brand New. 1st edition. 332 pages. 9.50x6.50x1.00 inches. In Stock. This item is printed on demand.