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1

Takahashi, Makoto, Yasuhiro Omori, and Toshiaki Watanabe. Stochastic Volatility and Realized Stochastic Volatility Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3.

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2

Hafner, Reinhold. Stochastic Implied Volatility. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17117-8.

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3

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

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4

Fornari, Fabio, and Antonio Mele. Stochastic Volatility in Financial Markets. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4533-0.

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5

Harvey, Andrew. The econometrics of stochastic volatility. London School of Economics Financial Markets Group, 1993.

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6

Bishwal, Jaya P. N. Parameter Estimation in Stochastic Volatility Models. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03861-7.

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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

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

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9

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

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10

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

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11

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

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12

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

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13

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

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14

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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15

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

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16

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

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17

Robinson, P. M. Nonlinear time series with long memory: A model for stochastic volatility. Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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18

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

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19

Chacko, George. Dynamic consumption and portfolio choice with stochastic volatility in incomplete markets. National Bureau of Economic Research, 1999.

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20

Robinson, Peter M. Nonlinear time series with long memory: A model for stochastic volatility. London School of Economics, Financial Markets Group, 1996.

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21

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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22

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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23

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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24

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

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25

Guichard, R. The pricing of foreign exchange options with stochastic volatility: A practical approach. Imperial College Management School, 1996.

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26

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

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27

Feinstein, Steven. The Hull and White implied volatility: A theoretical and empirical investigation of a volatility forecast implied by the Hull and White stochastic volatility option pricing model. Boston University, School of Management, 1992.

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28

A, Gershunov, Panorska A. K, Kozubowski Tomasz J. 1962-, California Energy Commission. Public Interest Energy Research., Scripps Institution of Oceanography, and University of Nevada Reno, eds. Quantifying volatility and the probability of daily precipitation extremes: PIER project report. California Energy Commission, 2007.

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29

Harvey, Andrew. Testing for a slowly changing level with a special reference to stochastic volatility. Suntory and Toyota International Centres for Economics and Related Disciplines, 1996.

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30

Engle, R. F. Index-option pricing with stochastic volatility and the value of accurate variance forecasts. National Bureau of Economic Research, 1993.

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31

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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32

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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33

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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34

Heston, Steven L. A closed-form solution for options with stochastic volatility, with application to bond and currency options. Yale University, School of Organization and Management, 1992.

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35

Heston, Steven L. A closed-form solution for options with stochastic volatility, with application to bond and currency options. Yale University, School of Organization and Management, 1992.

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36

Chevallier, Julien, Stéphane Goutte, David Guerreiro, Sophie Saglio, and Bilel Sanhaji, eds. Financial Mathematics, Volatility And Covariance Modelling: Volume 2. Routledge, 2019.

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37

Kim, Don H. Spanned stochastic volatility in bond markets: A reexamination of the relative pricing between bonds and bond options. Bank for International Settlements, 2007.

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38

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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39

Bergomi, Lorenzo. Stochastic Volatility Modeling. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19649.

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40

Stochastic volatility modeling. CRC Press, 2016.

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41

Bergomi, Lorenzo. Stochastic Volatility Modeling. Taylor & Francis Group, 2015.

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42

Bergomi, Lorenzo. Stochastic Volatility Modeling. Taylor & Francis Group, 2015.

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43

Shephard, Neil. Stochastic Volatility: Selected Readings. Oxford University Press, 2005.

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44

Stochastic Volatility in Financial Markets. Island Press, 2000.

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45

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

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46

Bishwal, Jaya P. N. Parameter Estimation in Stochastic Volatility Models. Springer International Publishing AG, 2022.

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47

Parameter Estimation in Stochastic Volatility Models. Springer International Publishing AG, 2023.

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48

Fouque, Jean-Pierre, George Papanicolaou, and K. Ronnie Sircar. Derivatives in Financial Markets with Stochastic Volatility. Cambridge University Press, 2000.

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49

Cao, Quanwei. Pricing foreign currency options with stochastic volatility. 1993.

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50

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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Abstract (sommario):
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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