Stochastic volatility

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  • While the stochastic volatility (SV) generalization has been shown to improve the explanatory power over the Black-Scholes model, empirical implications of SV models on option pricing have not yet been adequately tested. The purpose of this paper is to first estimate a multivariate SV model using the efficient method of moments (EMM) technique from observations of underlying state variables and then investigate the respective effect of stochastic interest rates, systematic volatility and idiosyncratic volatility on option prices....

    pdf48p batoan 16-07-2009 170 45   Download

  • ADVANCED TEXTS IN ECONOMETRICS General Editors Manuel Arellano Guido Imbens Grayham E. Mizon Adrian Pagan Mark Watson Advisory Editor C. W. J. Granger.Other Advanced Texts in conometrics ARCH: Selected Readings Edited by Robert F. Engle Asymptotic Theory for Integrated Processes By H. Peter Boswijk Bayesian Inference in Dynamic Econometric Models By Luc Bauwens, Michel Lubrano, and Jean-Fran¸ois Richard c Co-tegration, Error Correction, and the Econometric Analysis of Non-Stationary Data By Anindya Banerjee, Juan J. ...

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  • Stochastic volatility (SV) is the main concept used in the fields of financial economics and mathematical finance to deal with time-varying volatility in financial markets. In this book I bring together some of the main papers which have influenced the field of the econometrics of stochastic volatility with the hope that this will allow students and scholars to place this literature in a wider context.

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  • It is a pleasure to edit the second volume of papers presented at the Mathematical Finance Seminar of New York University. These articles, written by some of the leading experts in financial modeling cover a variety of topics in this field. The volume is divided into three parts: (I) Estimation and Data-Driven Models, (II) Model Calibration and Option Volatility and (III) Pricing and Hedging. The papers in the section on "Estimation and Data-Driven Models" develop new econometric techniques for finance and, in some cases, apply them to derivatives.

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  • In this note we consider the filtering problem for financial volatility that is an Ornstein-Ulhenbeck process from point process observation. This problem is investigated for a Markov-Feller process of which the Ornstein-Ulhenbeck process is a particular case.

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  • Driven by the necessity to incorporate the observed stylized features of asset prices, continuous-time stochastic modeling has taken a predominant role in the financial literature over the past two decades. Most of the proposed models are particular cases of a stochastic volatility component driven by a Wiener process superposed with a pure-jump component accounting for the

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  • Continuous-time modeling in finance, though introduced by Louis Bachelier's 1900 thesis on the theory of speculation, really started with Merton's seminal work in the 1970s. Since then, the continuous-time paradigm has proved to be an immensely useful tool in finance and more generally economics. Continuous-time models are widely used to study issues that include the decision to optimally consume, save, and invest, portfolio choice under a variety of constraints, contingent claim pricing, capital accumulation, resource extraction, game theory, and more recently contract theory....

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  • Financial markets have undergone tremendous growth and dramatic changes in the past two decades, with the volume of daily trading in currency markets hitting over a trillion US dollars and hundreds of billions of dollars in bond and stock markets. Deregulation and globalization have led to large-scale capital flows; this has raised new problems for finance as well as has further spurred competition among banks and financial institutions.

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  • We build a three-factor term-structure of interest rates model and use it to price corporate bonds. The first two factors allow the risk-free term structure to shift and tilt. The third factor generates a stochastic credit-risk premium. To implement the model, we apply the Peterson and Stapleton (2002) diffusion approximation methodology. The method approximates a correlated and lagged-dependent lognormal diffusion processes. We then price options on credit-sensitive bonds.

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  • Our methodology is based on a dynamic stochastic general equilibrium (DSGE) calibrated model augmented with endogenous market structures in line with recent developments in the macroeconomic literature (see Etro, 2009, for a survey). This model is perturbed with a realistic structural change to the cost structure, with the purpose to study the short and long term reactions of the economy. Therefore, our methodology is based on a solid theoretical framework and provides results that can be easily replicated by economists. However, it has some limitations that we need to point out.

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  • Issuance of Stability Bonds under joint and several guarantees would a priori lead to a situation where the prohibition on bailing out would be breached. In such a situation, a Member State would indeed be held liable irrespective of its 'regular' contributing key, should another Member State be unable to honour its financial commitments. In this case, an amendment to the Treaty would be necessary.

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  • Web search engine: Markov chain theory Data Mining, Machine Learning: Data mining, Machine learning: Stochastic gradient, Markov chain Monte Carlo, Image processing: Markov random fields, Design of wireless communication systems: random matrix theory, Optimization of engineering processes: simulated annealing, genetic algorithms, Finance (option pricing, volatility models): Monte Carlo, dynamic models, Design of atomic bomb (Los Alamos): Markov chain Monte Carlo.

    pdf16p quangchien2205 30-03-2011 33 4   Download

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