Linear State Models for Volatility Estimation and Prediction

This report covers the important topic of stochastic volatility modelling with an emphasis on linear state models. The approach taken focuses on comparing models based on their ability to fit the data and their forecasting performance. To this end several parsimonious stochastic volatility models are estimated using realised volatility, a volatility proxy from high frequency stock price data. The results indicate that a hidden state space model performs the best among the realised volatility-based models under consideration. For the state space model different sampling intervals are compared based on in-sample prediction performance. The comparisons are partly based on the multi-period prediction results that are derived in this report

Measuring the risk of a nonlinear portfolio with fat tailed risk factors through probability conserving transformation

This paper presents a new heuristic for fast approximation of VaR (Value-at-Risk) and CVaR (conditional Value-at-Risk) for financial portfolios, where the net worth of a portfolio is a non-linear function of possibly non-Gaussian risk factors. The proposed method is based on mapping non-normal marginal distributions into normal distributions via a probability conserving transformation and then using a quadratic, i.e. Delta–Gamma, approximation for the portfolio value. The method is very general and can deal with a wide range of marginal distributions of risk factors, including non-parametric distributions. Its computational load is comparable with the Delta–Gamma–Normal method based on Fourier inversion. However, unlike the Delta–Gamma–Normal method, the proposed heuristic preserves the tail behaviour of the individual risk factors, which may be seen as a significant advantage. We demonstrate the utility of the new method with comprehensive numerical experiments on simulated as well as real financial data

Classical and Quantum Aspects of Gravitation and Cosmology

These are the proceedings of the XVIII Conference of the Indian Association for General Relativity and Gravitation (IAGRG) held at the Institute of Mathematical Sciences, Madras, INDIA during Feb. 15-17, 1996. The Conference was dedicated the late Prof. S. Chandrasekhar. The proceedings consists of 17 articles on: - Chandrasekhar's work (N. Panchapkesan); - Vaidya-Raychaudhuri Lecture (C.V. Vishveshwara) - Gravitational waves (B.R. Iyer, R. Balasubramanian) - Gravitational Collapse (T.P. Singh) - Accretion on black hole (S. Chakrabarti) - Cosmology (D. Munshi, S. Bharadwaj, G.S. Mohanty, P. Bhattacharjee); - Classical GR (S. Kar, D.C. Srivatsava) - Quantum aspects (J. Maharana, Saurya Das, P. Mitra, G. Date, N.D. Hari Dass) The body of THIS article contains ONLY the title, contents, foreword, organizing committees, preface, list of contributed talks and list of participants. The plenery talks are available at: http://www.imsc.ernet.in/physweb/Conf/ both as post-script files of individual articles and also as .uu source files. For further information please send e-mail to [email protected]: 12 pages, latex, needs psfig.tex macros. Latex the file run.tex. These Proceedings of the XVIII IAGRG Conference are available at http://www.imsc.ernet.in/physweb/Conf/ MINOR TYPO's in the ABSTRACT correcte

A new moment matching algorithm for sampling from partially specified symmetric distributions

A new algorithm is proposed for generating scenarios from a partially specified symmetric multivariate distribution. The algorithm generates samples which match the first two moments exactly and match the marginal fourth moments approximately, using a semidefinite programming procedure. The performance of the algorithm is illustrated by a numerical example

A new algorithm for latent state estimation in nonlinear time series models

We consider the problem of optimal state estimation for a wide class of nonlinear time series models. A modified sigma point filter is proposed, which uses a new procedure for generating sigma points. Unlike the existing sigma point generation methodologies in engineering where negative probability weights may occur, we develop an algorithm capable of generating sample points that always form a valid probability distribution while still allowing the user to sample using a random number generator. The effectiveness of the new filtering procedure is assessed through simulation examples

ICGC-2004 Conference Overview

This is a written, expanded version of the summary talk given at the conclusion of the ICGC-2004 held at Cochin. Brief introductory remarks are included to provide a slightly wider context to the theme talks.Comment: 14 pages, revtex4, no figure

Valuation of cash flows under random rates of interest: A linear algebraic approach

This paper reformulates the classical problem of cash flow valuation under stochastic discount factors into a system of linear equations with random perturbations. Using convergence results, a sequence of uniform approximations is developed. The new formulation leads to a general framework for deriving approximate statistics of cash flows for a broad class of models of stochastic interest rate process. We show applications of the proposed method by pricing default-free and defaultable cash flows. The methodology developed in this paper is applicable to a variety of uncertain cash flow analysis problems

Isolated Horizon, Killing Horizon and Event Horizon

We consider space-times which in addition to admitting an isolated horizon also admit Killing horizons with or without an event horizon. We show that an isolated horizon is a Killing horizon provided either (1) it admits a stationary neighbourhood or (2) it admits a neighbourhood with two independent, commuting Killing vectors. A Killing horizon is always an isolated horizon. For the case when an event horizon is definable, all conceivable relative locations of isolated horizon and event horizons are possible. Corresponding conditions are given.Comment: 14 pages, Latex, no figures. Some arguments tightened. To appear in Class. Quant. Gra

A linear algebraic method for pricing temporary life annuities and insurance policies

We recast the valuation of annuities and life insurance contracts under mortality and interest rates, both of which are stochastic, as a problem of solving a system of linear equations with random perturbations. A sequence of uniform approximations is developed which allows for fast and accurate computation of expected values. Our reformulation of the valuation problem provides a general framework which can be employed to find insurance premiums and annuity values covering a wide class of stochastic models for mortality and interest rate processes. The proposed approach provides a computationally efficient alternative to Monte Carlo based valuation in pricing mortality-linked contingent claims

Dressing Transformations and the Algebraic--Geometrical Solutions in the Conformal Affine sl(2)sl(2) Toda Model

It is shown that the algebraic--geometrical (or quasiperiodic) solutions of the Conformal Affine $sl(2)$ Toda model are generated from the vacuum via dressing transformations. This result generalizes the result of Babelon and Bernard which states that the $N$--soliton solutions are generated from the vacuum by dressing transformations.Comment: 12 pages, latex, no figure