Pub Date : 2000-03-26DOI: 10.1109/CIFER.2000.844604
Y. d'Halluin, P. Forsyth, K. Vetzal, G. Labahn
This work demonstrates that it is possible to obtain accurate values of callable bonds using a fully numerical approach, provided that the PDE is discretized appropriately. To facilitate comparisons with results reported by Buttler and Waldvogel (1996), we consider models with a single factor: the instantaneous risk free interest rate. We emphasize, however, that it is straightforward to extend the numerical methods described to cases where the Green's function cannot be determined analytically as well as to cases with time-dependent parameters (typically used to match current term structures of interest rates/interest rate volatilities), or multi-factor interest rate models.
{"title":"Numerical methods for pricing callable bonds","authors":"Y. d'Halluin, P. Forsyth, K. Vetzal, G. Labahn","doi":"10.1109/CIFER.2000.844604","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844604","url":null,"abstract":"This work demonstrates that it is possible to obtain accurate values of callable bonds using a fully numerical approach, provided that the PDE is discretized appropriately. To facilitate comparisons with results reported by Buttler and Waldvogel (1996), we consider models with a single factor: the instantaneous risk free interest rate. We emphasize, however, that it is straightforward to extend the numerical methods described to cases where the Green's function cannot be determined analytically as well as to cases with time-dependent parameters (typically used to match current term structures of interest rates/interest rate volatilities), or multi-factor interest rate models.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131350565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-03-26DOI: 10.1109/CIFER.2000.844601
K. Ng, G. Sheblé
Operations research tools vary significantly. In this paper, several operations research tools that can handle uncertainty are investigated. They include sensitivity analysis, parametric analysis, mean-variance analysis, stochastic linear programming, fuzzy linear programming, and value at risk (VaR). In addition, these tools are compared and contrasted based on their applicability, time, and technical requirements.
{"title":"Exploring risk management tools","authors":"K. Ng, G. Sheblé","doi":"10.1109/CIFER.2000.844601","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844601","url":null,"abstract":"Operations research tools vary significantly. In this paper, several operations research tools that can handle uncertainty are investigated. They include sensitivity analysis, parametric analysis, mean-variance analysis, stochastic linear programming, fuzzy linear programming, and value at risk (VaR). In addition, these tools are compared and contrasted based on their applicability, time, and technical requirements.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124588415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-03-26DOI: 10.1109/CIFER.2000.844588
K. Engemann, H. Miller, R. Yager
There is much imprecise information in financial environments; for example, in forecasting economic scenarios in order to assess the potential payoff of an investment or to optimize asset allocation. We provide a new methodology which may be used in situations in which the likelihood of a state of nature is given in the form of an interval probability. Our approach incorporates the behavioral disposition of the decision maker.
{"title":"Imprecise information and financial environments: an interval probability approach","authors":"K. Engemann, H. Miller, R. Yager","doi":"10.1109/CIFER.2000.844588","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844588","url":null,"abstract":"There is much imprecise information in financial environments; for example, in forecasting economic scenarios in order to assess the potential payoff of an investment or to optimize asset allocation. We provide a new methodology which may be used in situations in which the likelihood of a state of nature is given in the form of an interval probability. Our approach incorporates the behavioral disposition of the decision maker.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126417289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-03-26DOI: 10.1109/CIFER.2000.844610
S. Kou
Brownian motion and normal distribution have been widely used to study option pricing and the return of assets. Despite the successes of the Black-Scholes-Merton model based on Brownian motion and normal distribution, two puzzles which emerged from many empirical investigations, have had much attention recently: 1) the leptokurtic and asymmetric features; 2) the volatility smile. Much research has been conducted on modifying the Black-Scholes models to explain the two puzzles. To incorporate the leptokurtic and asymmetric features, a variety of models have been proposed. The article proposes a novel model which has three properties: 1) it has leptokurtic and asymmetric features, under which the return distribution of the assets has a higher peak and two heavier tails than the normal distribution, especially the left tail; 2) it leads to analytical solutions to many option pricing problems, including: call and put options, and options on futures; interest rate derivatives such as caplets, caps, and bond options; exotic options, such as perpetual American options, barrier and lookback options; 3) it can reproduce the "volatility smile".
{"title":"A jump diffusion model for option pricing with three properties: leptokurtic feature, volatility smile, and analytical tractability","authors":"S. Kou","doi":"10.1109/CIFER.2000.844610","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844610","url":null,"abstract":"Brownian motion and normal distribution have been widely used to study option pricing and the return of assets. Despite the successes of the Black-Scholes-Merton model based on Brownian motion and normal distribution, two puzzles which emerged from many empirical investigations, have had much attention recently: 1) the leptokurtic and asymmetric features; 2) the volatility smile. Much research has been conducted on modifying the Black-Scholes models to explain the two puzzles. To incorporate the leptokurtic and asymmetric features, a variety of models have been proposed. The article proposes a novel model which has three properties: 1) it has leptokurtic and asymmetric features, under which the return distribution of the assets has a higher peak and two heavier tails than the normal distribution, especially the left tail; 2) it leads to analytical solutions to many option pricing problems, including: call and put options, and options on futures; interest rate derivatives such as caplets, caps, and bond options; exotic options, such as perpetual American options, barrier and lookback options; 3) it can reproduce the \"volatility smile\".","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"541 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133451941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1999-07-01DOI: 10.1109/CIFER.2000.844587
J. Rosenberg
This paper proposes and investigates a class of dynamic implied volatility function models (DIVF). This class of models separates the time-invariant implied volatility function from the stochastic state variables which drive changes in the individual implied volatilities. The dynamics of the state variables are modeled explicitly. This framework facilitates consistent pricing and hedging with time-variation in the IVF.
{"title":"Implied volatility functions: a reprise","authors":"J. Rosenberg","doi":"10.1109/CIFER.2000.844587","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844587","url":null,"abstract":"This paper proposes and investigates a class of dynamic implied volatility function models (DIVF). This class of models separates the time-invariant implied volatility function from the stochastic state variables which drive changes in the individual implied volatilities. The dynamics of the state variables are modeled explicitly. This framework facilitates consistent pricing and hedging with time-variation in the IVF.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122121099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CIFER.2000.844584
V. Cherkassky
In recent years, there has been an explosive growth of methods for estimating (learning) dependencies from data. The learning methods have been developed in the fields of statistics, neural networks, signal processing, fizzy systems etc. These methods have a common goal of estimating unknown dependencies from available (historical) data (samples). Estimated dependencies are then used for accurate prediction of future data (generalization). Hence this problem is known as Predictive Learning. Statistical Learning Theory (aka VC-theory or VapnikChervonenkis theory) has recently emerged as a general conceptual and mathematical framework for estimating (learning) dependencies from finite samples. Unfortunately, perhaps because of its mathematical rigor and complexity, this theory is not well known in the financial engineering community. Hence, the purpose of this tutorial is to discuss:
{"title":"Introduction to VC learning theory","authors":"V. Cherkassky","doi":"10.1109/CIFER.2000.844584","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844584","url":null,"abstract":"In recent years, there has been an explosive growth of methods for estimating (learning) dependencies from data. The learning methods have been developed in the fields of statistics, neural networks, signal processing, fizzy systems etc. These methods have a common goal of estimating unknown dependencies from available (historical) data (samples). Estimated dependencies are then used for accurate prediction of future data (generalization). Hence this problem is known as Predictive Learning. Statistical Learning Theory (aka VC-theory or VapnikChervonenkis theory) has recently emerged as a general conceptual and mathematical framework for estimating (learning) dependencies from finite samples. Unfortunately, perhaps because of its mathematical rigor and complexity, this theory is not well known in the financial engineering community. Hence, the purpose of this tutorial is to discuss:","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125000657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CIFER.2000.844616
Gianluca Bontempi, Edy Bertolissi, M. Birattari
This paper adopts the idea of regularity in the boundaries of financial time series in order to fit forecasting models which are able to outperform random walk predictions. In particular we propose the adoption of a local learning technique, called lazy learning, in order to perform model estimation and prediction in extreme conditions. The lazy learning method is proposed to return predictions in extreme conditions of trends of the Italian stock market index. The experiments show that in boundary conditions the technique is able to outperform a random predictor and to return a significant rate of accuracy.
{"title":"Predicting stock markets in boundary conditions with local models","authors":"Gianluca Bontempi, Edy Bertolissi, M. Birattari","doi":"10.1109/CIFER.2000.844616","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844616","url":null,"abstract":"This paper adopts the idea of regularity in the boundaries of financial time series in order to fit forecasting models which are able to outperform random walk predictions. In particular we propose the adoption of a local learning technique, called lazy learning, in order to perform model estimation and prediction in extreme conditions. The lazy learning method is proposed to return predictions in extreme conditions of trends of the Italian stock market index. The experiments show that in boundary conditions the technique is able to outperform a random predictor and to return a significant rate of accuracy.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126570517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CIFER.2000.844585
V. Cherkassky, Filip Mulier, Anna B. Sheng
This paper describes a new approach for asset allocation and risk management called funds exchange. The funds exchange approach generically describes short-term trading of (broadly-based) mutual funds or indices based on statistical strategies aimed at achieving improved returns and, at the same time, reducing market risk (i.e., market exposure). Unlike many statistically-based trading and advisory systems trying to predict and benefit from the major (big) changes in the stock market, the funds exchange approach tries to capitalize on the short-term (daily) market volatility, i.e. small daily changes. This paper describes concepts and assumptions underlying this approach, and mathematical formulation of the funds exchange approach as a problem of predictive learning. Finally we show empirical evidence that the proposed approach can indeed provide improved returns and reduce market risk for SP 500 mutual funds.
{"title":"Funds exchange: an approach for risk and portfolio management","authors":"V. Cherkassky, Filip Mulier, Anna B. Sheng","doi":"10.1109/CIFER.2000.844585","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844585","url":null,"abstract":"This paper describes a new approach for asset allocation and risk management called funds exchange. The funds exchange approach generically describes short-term trading of (broadly-based) mutual funds or indices based on statistical strategies aimed at achieving improved returns and, at the same time, reducing market risk (i.e., market exposure). Unlike many statistically-based trading and advisory systems trying to predict and benefit from the major (big) changes in the stock market, the funds exchange approach tries to capitalize on the short-term (daily) market volatility, i.e. small daily changes. This paper describes concepts and assumptions underlying this approach, and mathematical formulation of the funds exchange approach as a problem of predictive learning. Finally we show empirical evidence that the proposed approach can indeed provide improved returns and reduce market risk for SP 500 mutual funds.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CIFER.2000.844623
G. A. Agasandian, F. Ereshko, A. Ereshko, I. Gasanov
Financial markets are not widely and deeply developed in the modern Russian economy. There are, however, several examples where information has been accumulated such that constructions of adequate stochastic or other models of uncertain factors are possible. Applications of the various approaches and methods of system analysis and operations research are therefore possible, useful and profitable. We investigated the market of Russian Government Bills (RGBs) from two points of view: the macroeconomics and RGB portfolio management by small investors. An approach to the elaboration of the rational politics for big producers in the real Russian economy that use barter and bill contracts is also described.
{"title":"The methods of operations research on Russian financial markets","authors":"G. A. Agasandian, F. Ereshko, A. Ereshko, I. Gasanov","doi":"10.1109/CIFER.2000.844623","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844623","url":null,"abstract":"Financial markets are not widely and deeply developed in the modern Russian economy. There are, however, several examples where information has been accumulated such that constructions of adequate stochastic or other models of uncertain factors are possible. Applications of the various approaches and methods of system analysis and operations research are therefore possible, useful and profitable. We investigated the market of Russian Government Bills (RGBs) from two points of view: the macroeconomics and RGB portfolio management by small investors. An approach to the elaboration of the rational politics for big producers in the real Russian economy that use barter and bill contracts is also described.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121632628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CIFER.2000.844609
P. Carr
We use various techniques to simplify the derivations of "greeks" of path-independent claims in the Black-Merton-Scholes model. We first interpret delta, gamma, speed, and other higher order spatial derivatives of these claims as the values of certain quantoed contingent claims. We then show that all partial derivatives of such claims can be represented in terms of these spatial derivatives. These observations permit the rapid deployment of high order Taylor series expansions, which we illustrate for European options.
{"title":"Deriving derivatives of derivative securities","authors":"P. Carr","doi":"10.1109/CIFER.2000.844609","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844609","url":null,"abstract":"We use various techniques to simplify the derivations of \"greeks\" of path-independent claims in the Black-Merton-Scholes model. We first interpret delta, gamma, speed, and other higher order spatial derivatives of these claims as the values of certain quantoed contingent claims. We then show that all partial derivatives of such claims can be represented in terms of these spatial derivatives. These observations permit the rapid deployment of high order Taylor series expansions, which we illustrate for European options.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134513245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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