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Books on the topic 'Factor stochastic volatility models'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Hafner, Reinhold. Stochastic implied volatility: A factor-based model. Springer, 2004.

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2

Chabi-Yo, Fousseni. The stochastic discount factor: Extending the volatility bound and a new approach to portfolio selection with higher-order moments. Bank of Canada, 2005.

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3

Stochastic volatility modeling. CRC Press, 2016.

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4

Mulligan, Casey B. Robust aggregate implications of stochastic discount factor volatility. National Bureau of Economic Research, 2004.

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5

Sandmann, G. Maximum likelihood estimation of stochastic volatility models. London School of Economics, Financial Markets Group, 1996.

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6

Aït-Sahalia, Yacine. Maximum likelihood estimation of stochastic volatility models. National Bureau of Economic Research, 2004.

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7

Melino, Angelo. Pricing foreign currency options with stochastic volatility. Dept. of Economics; Institute for Policy Analysis, University of Toronto, 1988.

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8

Trolle, Anders B. Unspanned stochastic volatility and the pricing of commodity derivatives. National Bureau of Economic Research, 2006.

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9

Krichene, Noureddine. Modeling stochastic volatility with application to stock returns. International Monetary Fund, African Department, 2003.

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10

Ferson, Wayne E. Stochastic discount factor bounds with conditioning information. National Bureau of Economic Research, 2002.

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11

Alizadeh, Sassan. High- and low-frequency exchange rate volatility dynamics: Range-based estimation of stochastic volatility models. National Bureau of Economic Research, 2001.

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12

Kuo, I.-Doun. Implied volatility functions for one-factor and two-factor Heath, Jarrow, and Morton models. Manchester Business School, Phd, 2002.

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13

Burnside, Craig. Identification and inference in linear stochastic discount factor models. National Bureau of Economic Research, 2010.

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14

Javaheri, Alireza. Inside volatility filtering: The secrets of skewness. John Wiley & Sons, Inc., 2015.

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15

Bates, David S. Jumps and stochastic volatility: Exchange rate processes implicit in PHLX Deutschemark options. National Bureau of Economic Research, 1993.

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16

Dufresne, Pierre Collin. Can interest rate volatility be extracted from the cross section of bond yields?: An investigation of unspanned stochastic volatility. National Bureau of Economic Research, 2004.

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17

Nunes, João Pedro Vidal. Exponential-affine diffusion term structure models: Dimension, time-homogeneity, and stochastic volatility. typescript, 2000.

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18

Dufresne, Pierre Collin. Can interest rate volatility be extracted from the cross section of bond yields? an investigation of unspanned stochastic volatility. National Bureau of Economic Research, 2004.

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19

Trolle, Anders B. A general stochastic volatility model for the pricing and forecasting of interest rate derivatives. National Bureau of Economic Research, 2006.

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20

Brock, William A. A dynamic structural model for stock return volatility and trading volume. National Bureau of Economic Research, 1995.

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21

Javaheri, Alireza. Inside Volatility Arbitrage. John Wiley & Sons, Ltd., 2006.

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22

Empirical studies on volatility in international stock markets. Kluwer Academic, 2003.

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23

Antonio, Mele, ed. Stochastic volatility in financial markets: Crossing the bridge to continuous time. Kluwer Academic Publishers, 2000.

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24

Engle, R. F. Hedging options in a GARCH environment: Testing the term structure of stochastic volatility models. National Bureau of Economic Research, 1994.

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25

Fornari, Fabio. A simple approach to the estimation of continuous time CEV stochastic volatility models of the short-term rate. Banca d'Italia, 2001.

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26

S, Madheswaran, ed. Technological progress, scale effect, and total factor productivity growth in Indian cement industry: Panel estimation of stochastic production frontier. Institute for Social and Economic Change, 2009.

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27

Neil, Shephard, ed. Stochastic volatility: Selected readings. Oxford University Press, 2005.

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28

Fornari, Fabio, and Antonio Mele. Stochastic Volatility in Financial Markets. Springer, 2012.

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29

Tsiakas, Ilias. bayesian empirical applications of generalized stochastic volatility models. 2001.

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30

Bao, Yun, Carl Chiarella, and Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.

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This chapter proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. It proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method that demonstrates the ability to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemen

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31

Option Valuation Under Stochastic Volatility: With Mathematica Code. Finance Press, 2000.

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32

Shephard, Neil. Stochastic Volatility: Selected Readings (Advanced Texts in Econometrics). Oxford University Press, USA, 2005.

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33

Shephard, Neil. Stochastic Volatility: Selected Readings (Advanced Texts in Econometrics). Oxford University Press, USA, 2005.

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34

Pricing Of Bond Options Unspanned Stochastic Volatility And Random Field Models. Springer, 2008.

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35

Javaheri, Alireza. Inside Volatility Filtering: Secrets of the Skew. Wiley & Sons, Incorporated, John, 2015.

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36

Javaheri, Alireza. Inside Volatility Filtering: Secrets of the Skew. Wiley & Sons, Incorporated, John, 2015.

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37

Javaheri, Alireza. Inside Volatility Arbitrage: The Secrets of Skewness. Wiley & Sons, Incorporated, John, 2011.

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38

Inside Volatility Arbitrage : The Secrets of Skewness. Wiley, 2005.

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39

Fornari, Fabio, and Antonio Mele. Stochastic Volatility in Financial Markets: Crossing the Bridge to Continuous Time (Dynamic Modeling and Econometrics in Economics and Finance). Springer, 2000.

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40

Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data fo

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41

Back, Kerry E. Forwards, Futures, and More Option Pricing. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0017.

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Forward measures are defined. Forward and futures contracts are explained. The spot‐forward parity formula is derived. A forward price is a martingale under the forward measure. A futures price is a martingale under a risk neutral probability. Forward prices equal futures prices when interest rates are nonrandom. The expectations hypothesis is explained. The option pricing formulas of Margabe (exchange options), Black (options on forwards), and Merton (random interest rates) are derived. Implied volatilities and local volatility models are explained. Heston’s stochastic volatility model is der

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42

Back, Kerry E. Asset Pricing and Portfolio Choice Theory. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.001.0001.

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This book is intended as a textbook for asset pricing theory courses at the Ph.D. or Masters in Quantitative Finance level and as a reference for financial researchers. The first two parts of the book explain portfolio choice and asset pricing theory in single‐period, discrete‐time, and continuous‐time models. For valuation, the focus throughout is on stochastic discount factors and their properties. Traditional factor models, including the CAPM, are related to or derived from stochastic discount factors. A chapter on stochastic calculus provides the needed tools for analyzing continuous‐time

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43

Ferraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.

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This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as li

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44

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Nonlinear Time Series Analysis with R. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.001.0001.

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In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis

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45

Björk, Tomas. Arbitrage Theory in Continuous Time. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198851615.001.0001.

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The fourth edition of this textbook on pricing and hedging of financial derivatives, now also including dynamic equilibrium theory, continues to combine sound mathematical principles with economic applications. Concentrating on the probabilistic theory of continuous time arbitrage pricing of financial derivatives, including stochastic optimal control theory and optimal stopping theory, the book is designed for graduate students in economics and mathematics, and combines the necessary mathematical background with a solid economic focus. It includes a solved example for every new technique prese

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