Applied quantitative methods for trading and investment
著者
書誌事項
Applied quantitative methods for trading and investment
(Wiley finance series)
Wiley, c2003
大学図書館所蔵 全7件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
This book provides a manual on quantitative financial analysis. Focusing on advanced methods for modelling financial markets in the context of practical financial applications, it will cover data, software and techniques that will enable the reader to implement and interpret quantitative methodologies, specifically for trading and investment.
Includes contributions from an international team of academics and quantitative asset managers from Morgan Stanley, Barclays Global Investors, ABN AMRO and Credit Suisse First Boston.
Fills the gap for a book on applied quantitative investment & trading models
Provides details of how to combine various models to manage and trade a portfolio
目次
About the Contributors. Preface.
1 Applications of Advanced Regression Analysis for Trading and Investment (Christian L. Dunis and Mark Williams).
Abstract.
1.1 Introduction.
1.2 Literature review.
1.3 The exchange rate and related financial data.
1.4 Benchmark models: theory and methodology.
1.5 Neural network models: theory and methodology.
1.6 Forecasting accuracy and trading simulation.
1.7 Concluding remarks.
2 Using Cointegration to Hedge and Trade International Equities (A. Neil Burgess).
Abstract.
2.1 Introduction.
2.2 Time series modelling and cointegration.
2.3 Implicit hedging of unknown common risk factors.
2.4 Relative value and statistical arbitrage.
2.5 Illustration of cointegration in a controlled simulation.
2.6 Application to international equities.
2.7 Discussion and conclusions.
3 Modelling the Term Structure of Interest Rates: An Application of Gaussian Affine Models to the German Yield Curve (Nuno Cassola and Jorge Barros Luis).
Abstract.
3.1 Introduction.
3.2 Background issues on asset pricing.
3.3 Duffie-Kan affine models of the term structure.
3.4 A forward rate test of the expectations theory.
3.5 Identification.
3.6 Econometric methodology and applications.
3.7 Estimation results.
3.8 Conclusions.
4 Forecasting and Trading Currency Volatility: An Application of Recurrent Neural Regression and Model Combination (Christian L. Dunis and Xuehuan Huang).
Abstract.
4.1 Introduction.
4.2 The exchange rate and volatility data.
4.3 The GARCH (1,1) benchmark volatility forecasts.
4.4 The neural network volatility forecasts.
4.5 Model combinations and forecasting accuracy.
4.6 Foreign exchange volatility trading models.
4.7 Concluding remarks and further work.
5 Implementing Neural Networks, Classification Trees, and Rule Induction Classification Techniques: An Application to Credit Risk (George T. Albanis).
Abstract.
5.1 Introduction.
5.2 Data description.
5.3 Neural networks for classification in Excel.
5.4 Classification tree in Excel.
5.5 See5 classifier.
5.6 Conclusions.
6 Switching Regime Volatility: An Empirical Evaluation (Bruno B. Roche and Michael Rockinger).
Abstract.
6.1 Introduction.
6.2 The model.
6.3 Maximum likelihood estimation.
6.4 An application to foreign exchange rates.
6.5 Conclusion.
7 Quantitative Equity Investment Management with Time-Varying Factor Sensitivities (Yves Bentz).
Abstract.
7.1 Introduction.
7.2 Factor sensitivities defined.
7.3 OLS to estimate factor sensitivities: a simple, popular but inaccurate method.
7.4 WLS to estimate factor sensitivities: a better but still sub-optimal method.
7.5 The stochastic parameter regression model and the Kalman filter: the best way to estimate factor sensitivities.
7.6 Conclusion.
8 Stochastic Volatility Models: A Survey with Applications to Option Pricing and Value at Risk (Monica Billio and Domenico Sartore).
Abstract.
8.1 Introduction.
8.2 Models of changing volatility.
8.3 Stochastic volatility models.
8.4 Estimation.
8.5 Extensions of SV models.
8.6 Multivariate models.
8.7 Empirical applications.
8.8 Concluding remarks.
9 Portfolio Analysis Using Excel (Jason Laws).
Abstract.
9.1 Introduction.
9.2 The simple Markovitz model.
9.3 The matrix approach to portfolio risk.
9.4 Matrix algebra in Excel when the number of assets increases.
9.5 Alternative optimisation targets.
9.6 Conclusion.
10 Applied Volatility and Correlation Modelling Using Excel (Frederick Bourgoin).
Abstract.
10.1 Introduction.
10.2 The Basics.
10.3 Univariate models.
10.4 Multivariate models.
10.5 Conclusion.
11 Optimal Allocation of Trend-Following Rules: An Application Case of Theoretical Results (Pierre Lequeux).
Abstract.
11.1 Introduction.
11.2 Data.
11.3 Moving averages and their statistical properties.
11.4 Trading rule equivalence.
11.5 Expected transactions cost under assumption of random walk.
11.6 Theoretical correlation of linear forecasters.
11.7 Expected volatility of MA.
11.8 Expected return of linear forecasters.
11.9 An applied example.
11.10 Final remarks.
References.
12 Portfolio Management and Information from Over-the-Counter Currency Options (Jorge Barros Luis).
Abstract.
12.1 Introduction.
12.2 The valuation of currency options spreads.
12.3 RND estimation using option spreads.
12.4 Measures of correlation and option prices.
12.5 Indicators of credibility of an exchange rate band.
12.6 Empirical applications.
12.7 Conclusions.
13 Filling Analysis for Missing Data: An Application to Weather Risk Management (Christian L. Dunis and Vassilios Karalis).
Abstract.
13.1 Introduction.
13.2 Weather data and weather derivatives.
13.3 Alternative filling methods for missing data.
13.4 Empirical results.
13.5 Concluding remarks.
Index.
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