Open Access
Subscription Access
Open Access
Subscription Access
Explicit Time Discretization Programming Approach to Risk Modelling
Subscribe/Renew Journal
In this paper we formulate an explicit time discretization model for modeling risk by establishing an initial value problem as a function of time. The model is proved stable and the scaled-stability regions can encapsulated the volatile macroeconomic condition pertaining to financial risk. The model is extended to multistage schemes where we test for convergence under higherorder difference equations. Further, for addressing advection problems we have used Runge-Kutta method to propose a multistep model and have shown its stability patterns against general and absolute stability conditions. The paper also provides second-order and forth-order algorithm for computational programming of the models in practice. We conclude by stating that explicit time discretization models are stable and adequate for changing business environment.
Keywords
Explicit Time Discretization, Runge-kutta Method, Algorithms, Computational Programming, Risk Modeling
Subscription
Login to verify subscription
User
Font Size
Information
- A. Harvey, Econometric Analysis of Time Series, Cambridge, MA: MIT Press, (1993).
- A. Bensoussan, M. Crouhy, D. Galai, Stochastic equity volatility related to the leverage effect. I. Equity volatility behaviour, Applied Mathematical Finance, 1(1994)63-85.
- B. Jorgensen, S. Lundbye-Christensen, X.K. Song, and L. Sun., State Space Model for Multivariate Longitudinal Count Data, http://citeseer.nj.nec.com/ rgensen98state.html, (1998).
- C. Cameron and P. Trivedi, Regression Analysis of Count Data, Cambridge: Cambridge University Press, (1998).
- Czado, C. Czado and P. Song, State Space Models for Longitudinal Observations with Binary and Binomial Responses, http://citeseer.nj.nec.com/czado01state. html, (2001).
- D.R. Coc, and E.J. Snell. Analysis of Binary Data, 2nd ed. London: Chapman and Hall, (1989).
- D. R. Cox, and P.A.W. Lewis. The Statistical Analysis of Series of Events, New York: John Wiley and Sons, (1996).
- E.G. Bonney, Logistic Regression for Dependent Binary Observations, Biometrics, 43(1987)951-973.
- E.I. Altman andV. Kishore, Almost everything you wanted to know about recoveries on defaulted bonds, Financial Analysts Journal, (1996).
- Gary King, A Seemingly Unrelated Poisson Regression Model, Sociological Methods and Research, 17(1989)235-255.
- H. Goldstein, Multilevel Statistical Models, Kendall’sLibrary of Statistics 3, Internet Edition, www.arnoldpublishers.com/support/goldstein.htm, (1999).
- Leisch, F. Leisch, A. Weingessel, E. Dimitriadou, Competitive Learning for Binary Valued Data, International Conference on Artificial Neural Networks, Skovde, Sweden, (1998).
- L.R. Muenz and L.V. Rubinstein, Markov Models for Covariate Dependence of Binary Sequences, Biometrics, 41(1985)91-101.
- L.J. Fogel, A.J. Owens, and M.J. Walsh, Artificial Intelligence through Simulated Evolution, New York: John Wiley and Sons, (1966).
- Patrick T. Brandt and John T. Williams, Dynamic Modeling for Persistent Event Count Time Series, Political Analysis, www.polsci.indiana.edu, (1998).
- Patrick T. Brandt and John T. Williams, A Linear Poisson Autoregressive Model: the Poisson AR(p) Model, Political Analysis, www.polsci.indiana.edu, (2000).
- P.A.W. Lewis, Simple Models for Positive-Valued and Discrete-Valued Time Series with ARMA Correlation Structure, Multivariate Analysis, (1980) 151-166.
- Rob J. Hyndman, Nonparametric Additive Regression Models for Binary Time Series, Australasian Meeting of the Econometric Society, University of Technology, Sydney, July (1999).
- Robert C. Jung, and Andrew R. Tremayne, Testing Serial Dependence in Time Series Models of Counts Against Some INARMA Alternatives, www.uni tuebingen.de/uni/wwo/-jung.htm, (2000).
- RiskMetrics Technical Document, New York: J.P. Morgan/ Reuters, (1996).
- S. Chib and E. Greenberg, Analysis of Multivariate Probit Models, Biometrica, 85(1998)347-361.
- T.W. Anderson, and L.A. Goodman, Statistical Inference about Markov Chains, The Annals of Mathematical Statistics, 28(1957)89-110.
- V.C. Carey , S.L. Zeger, and Diggle, P.J. Diggle, Modeling Multivariate Binary Data with Alternating Logistic Regressions, Biometrika, 80(1993)517-526.
- W. Hardle, and R. Chen, Nonparametric Time Series Analysis: A Selective Review with Examples, http:// citeseer.nj. nec.com/228910.html, (1994).
Abstract Views: 282
PDF Views: 0