Relevant bibliographies by topics / Factor stochastic volatility models

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Academic literature on the topic 'Factor stochastic volatility models'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Factor stochastic volatility models"

1

da Silva, Afonso Gonçalves, and Peter M. Robinson. "FRACTIONAL COINTEGRATION IN STOCHASTIC VOLATILITY MODELS." Econometric Theory 24, no. 5 (2008): 1207–53. http://dx.doi.org/10.1017/s0266466608080481.

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Asset returns are frequently assumed to be determined by one or more common factors. We consider a bivariate factor model where the unobservable common factor and idiosyncratic errors are stationary and serially uncorrelated but have strong dependence in higher moments. Stochastic volatility models for the latent variables are employed, in view of their direct application to asset pricing models. Assuming that the underlying persistence is higher in the factor than in the errors, a fractional cointegrating relationship can be recovered by suitable transformation of the data. We propose a narro
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Nakajima, Jouchi. "Skew selection for factor stochastic volatility models." Journal of Applied Statistics 47, no. 4 (2019): 582–601. http://dx.doi.org/10.1080/02664763.2019.1646227.

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PFANTE, OLIVER, and NILS BERTSCHINGER. "VOLATILITY INFERENCE AND RETURN DEPENDENCIES IN STOCHASTIC VOLATILITY MODELS." International Journal of Theoretical and Applied Finance 22, no. 03 (2019): 1950013. http://dx.doi.org/10.1142/s0219024919500134.

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Stochastic volatility models describe stock returns [Formula: see text] as driven by an unobserved process capturing the random dynamics of volatility [Formula: see text]. The present paper quantifies how much information about volatility [Formula: see text] and future stock returns can be inferred from past returns in stochastic volatility models in terms of Shannon’s mutual information. In particular, we show that across a wide class of stochastic volatility models, including a two-factor model, returns observed on the scale of seconds would be needed to obtain reliable volatility estimates.
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Gunawan, David, Robert Kohn, and David Nott. "Variational Bayes approximation of factor stochastic volatility models." International Journal of Forecasting 37, no. 4 (2021): 1355–75. http://dx.doi.org/10.1016/j.ijforecast.2021.05.001.

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Kouritzin, Michael A. "Microstructure Models with Short-Term Inertia and Stochastic Volatility." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/323475.

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Partially observed microstructure models, containing stochastic volatility, dynamic trading noise, and short-term inertia, are introduced to address the following questions: (1) Do the observed prices exhibit statistically significant inertia? (2) Is stochastic volatility (SV) still evident in the presence of dynamical trading noise? (3) If stochastic volatility and trading noise are present, which SV model matches the observed price data best? Bayes factor methods are used to answer these questions with real data and this allows us to consider volatility models with very different structures.
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Nardari, Federico, and John T. Scruggs. "Bayesian Analysis of Linear Factor Models with Latent Factors, Multivariate Stochastic Volatility, and APT Pricing Restrictions." Journal of Financial and Quantitative Analysis 42, no. 4 (2007): 857–91. http://dx.doi.org/10.1017/s0022109000003422.

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AbstractWe analyze a new class of linear factor models in which the factors are latent and the covariance matrix of excess returns follows a multivariate stochastic volatility process. We evaluate cross-sectional restrictions suggested by the arbitrage pricing theory (APT), compare competing stochastic volatility specifications for the covariance matrix, and test for the number of factors. We also examine whether return predictability can be attributed to time-varying factor risk premia. Analysis of these models is feasible due to recent advances in Bayesian Markov chain Monte Carlo (MCMC) met
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Deng, Guohe. "Option Pricing under Two-Factor Stochastic Volatility Jump-Diffusion Model." Complexity 2020 (September 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/1960121.

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Empirical evidence shows that single-factor stochastic volatility models are not flexible enough to account for the stochastic behavior of the skew, and certain financial assets may exhibit jumps in returns and volatility. This paper introduces a two-factor stochastic volatility jump-diffusion model in which two variance processes with jumps drive the underlying stock price and then considers the valuation on European style option. We derive a semianalytical formula for European vanilla option and develop a fast and accurate numerical algorithm for the computation of the option prices using th
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Doz, Catherine, and Eric Renault. "Factor Stochastic Volatility in Mean Models: A GMM Approach." Econometric Reviews 25, no. 2-3 (2006): 275–309. http://dx.doi.org/10.1080/07474930600713325.

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Kastner, Gregor, Sylvia Frühwirth-Schnatter, and Hedibert Freitas Lopes. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models." Journal of Computational and Graphical Statistics 26, no. 4 (2017): 905–17. http://dx.doi.org/10.1080/10618600.2017.1322091.

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ISSAKA, AZIZ. "VALUATION, HEDGING, AND BOUNDS OF SWAPS UNDER MULTI-FACTOR BNS-TYPE STOCHASTIC VOLATILITY MODELS." Annals of Financial Economics 15, no. 02 (2020): 2050007. http://dx.doi.org/10.1142/s2010495220500074.

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In this paper, we consider price weighted-volatility swap and price weighted-variance swap. The underlying asset considered in this paper is assumed to follow a general stochastic differential equation and exhibits stochastic volatility. We obtain analytical pricing formulas for the weighted-variance swap and approximate expression for the weighted-volatility swap. Nice bounds for the arbitrage-free variance swap price are also found. The proposed pricing formulas are easy to implement in real time and can be applied efficiently for practical applications. We consider the problem of hedging vo
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Dissertations / Theses on the topic "Factor stochastic volatility models"

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Kastner, Gregor, Sylvia Frühwirth-Schnatter, and Hedibert Freitas Lopes. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4875/1/research_report_updated.pdf.

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We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almo
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Hafner, Reinhold. "Stochastic implied volatility : a factor-based model /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0813/2004109369-d.html.

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Häfner, Reinhold. "Stochastic implied volatility : a factor-based model /." Berlin ; New York : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0813/2004109369-d.html.

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Ahy, Nathaniel, and Mikael Sierra. "Implied Volatility Surface Approximation under a Two-Factor Stochastic Volatility Model." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-40039.

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Due to recent research disproving old claims in financial mathematics such as constant volatility in option prices, new approaches have been incurred to analyze the implied volatility, namely stochastic volatility models. The use of stochastic volatility in option pricing is a relatively new and unexplored field of research with a lot of unknowns, where new answers are of great interest to anyone practicing valuation of derivative instruments such as options. With both single and two-factor stochastic volatility models containing various correlation structures with respect to the asset price and
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Lee, Hyoung Il. "Stochastic volatility models with persistent latent factors: theory and its applications to asset prices." Texas A&M University, 2008. http://hdl.handle.net/1969.1/86017.

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We consider the stochastic volatility model with smooth transition and persistent la- tent factors. We argue that this model has advantages over the conventional stochastic model for the persistent volatility factor. Though the linear filtering is widely used in the state space model, the simulation result, as well as theory, shows that it does not work in our model. So we apply the density-based filtering method; in particular, we develop two methods to get solutions. One is the conventional approach using the Maximum Likelihood estimation and the other is the Bayesian approach using Gibbs sa
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Backwell, Alexander. "Term structure models with unspanned factors and unspanned stochastic volatility." Doctoral thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29460.

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Certain models of the term structure of interest rates exhibit unspanned stochastic volatility (USV). A model has this property if it involves a source of stochastic variation — called an unspanned factor — that does not affect the model’s interest rates directly, but does affect the extent to which future interests are liable to change (that is, interest-rate volatility). This thesis is concerned with these models, from a variety of perspectives. Firstly, the theoretical foundation of the USV property is addressed. Formal definitions of unspanned factors and USV are developed, generalising on
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Rafiou, AS. "Foreign Exchange Option Valuation under Stochastic Volatility." University of the Western Cape, 2009. http://hdl.handle.net/11394/7777.

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>Magister Scientiae - MSc<br>The case of pricing options under constant volatility has been common practise for decades. Yet market data proves that the volatility is a stochastic phenomenon, this is evident in longer duration instruments in which the volatility of underlying asset is dynamic and unpredictable. The methods of valuing options under stochastic volatility that have been extensively published focus mainly on stock markets and on options written on a single reference asset. This work probes the effect of valuing European call option written on a basket of currencies, under constant
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Hauzenberger, Niko, Maximilian Böck, Michael Pfarrhofer, Anna Stelzer, and Gregor Zens. "Implications of Macroeconomic Volatility in the Euro Area." 261, 2018. http://epub.wu.ac.at/6246/1/wp261.pdf.

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In this paper, we estimate a Bayesian vector autoregressive (VAR) model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area (EA). This allows us to incorporate uncertainty directly into the econometric framework and treat it as a latent quantity. Only a limited number of papers estimates impacts of uncertainty and macroeconomic consequences jointly, and most literature in this sphere is based on single countries. We analyze the special case of a shock restricted to the Euro area, whose countries are highly related by definition. Am
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Rios, Benavides Renato, and Chrysafis Bourelos. "Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44644.

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Stochastic volatility models have become essential for financial modelling and forecasting.The present thesis works with a two-factor stochastic volatility model that is reduced to four parameters. We start by making the case for the model that best fits data, use that modelto produce said parameters and then analyse the time series of these parameters. Suitable ARIMA models were then used to forecast the parameters and in turn, the implied volatilities.It was established that fitting the model for different groups of maturities produced better results. Moreover, we managed to reduce the forec
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Huber, Florian. "Dealing with heterogeneity in panel VARs using sparse finite mixtures." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6247/1/wp262.pdf.

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In this paper, we provide a parsimonious means of estimating panel VARs with stochastic volatility. We assume that coefficients associated with domestic lagged endogenous variables arise from a finite mixture of Gaussian distribution. Shrinkage on the cluster size is introduced through suitable priors on the component weights and cluster-relevant quantities are identified through novel normal-gamma shrinkage priors. To assess whether dynamic interdependencies between units are needed, we moreover impose shrinkage priors on the coefficients related to other countries' endogenous variables. Fi
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Books on the topic "Factor stochastic volatility models"

1

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

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

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

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

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

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

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

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

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Book chapters on the topic "Factor stochastic volatility models"

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Kastner, Gregor, Sylvia Frühwirth-Schnatter, and Hedibert F. Lopes. "Analysis of Exchange Rates via Multivariate Bayesian Factor Stochastic Volatility Models." In The Contribution of Young Researchers to Bayesian Statistics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02084-6_35.

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Ben Arous, Gérard, and Peter Laurence. "Second Order Expansion for Implied Volatility in Two Factor Local Stochastic Volatility Models and Applications to the Dynamic $$\lambda $$ -Sabr Model." In Large Deviations and Asymptotic Methods in Finance. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11605-1_4.

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Lutz, Björn. "Stochastic Volatility Models." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02909-7_4.

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Zhu, Jianwei. "Stochastic Volatility Models." In Applications of Fourier Transform to Smile Modeling. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01808-4_3.

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Shephard, Neil. "Stochastic Volatility Models." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2756-1.

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Shephard, Neil. "Stochastic Volatility Models." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2756.

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Ekstrand, Christian. "Stochastic Volatility Models." In Financial Derivatives Modeling. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22155-2_7.

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Bouchard, Bruno, and Jean-François Chassagneux. "Stochastic Volatility Models." In Universitext. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38990-5_8.

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Hilber, Norbert, Oleg Reichmann, Christoph Schwab, and Christoph Winter. "Stochastic Volatility Models." In Springer Finance. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35401-4_9.

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Shephard, Neil. "Stochastic volatility models." In Macroeconometrics and Time Series Analysis. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_31.

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Conference papers on the topic "Factor stochastic volatility models"

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Simandl, Miroslav, and Tomas Soukup. "Gibbs sampler to stochastic volatility models." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076061.

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Nikolaev, Nikolay Y., and Evgueni Smirnov. "Analytical factor stochastic volatility modeling for portfolio allocation." In 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2012. http://dx.doi.org/10.1109/cifer.2012.6327808.

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Figà-Talamanca, Gianna, and Maria Letizia Guerra. "Fuzzy Option Value with Stochastic Volatility Models." In 2009 Ninth International Conference on Intelligent Systems Design and Applications. IEEE, 2009. http://dx.doi.org/10.1109/isda.2009.243.

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Yu, Jun, and Zhenlin Yang. "A class of nonlinear stochastic volatility models." In 9th Joint Conference on Information Sciences. Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.87.

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Wang, Ximei, Hang Zhang, and Yanlong Zhao. "Parameters estimations for continuous-time stochastic volatility models." In 2017 36th Chinese Control Conference (CCC). IEEE, 2017. http://dx.doi.org/10.23919/chicc.2017.8027703.

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Abdellah, Amal Ben, Pierre L'Ecuyer, and Florian Puchhammer. "Array-RQMC for Option Pricing Under Stochastic Volatility Models." In 2019 Winter Simulation Conference (WSC). IEEE, 2019. http://dx.doi.org/10.1109/wsc40007.2019.9004819.

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Wang, Liugen, Shanshan Ding, and Shenghong Li. "Option Pricing in Jump-Diffusion Models with Stochastic Volatility." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5304459.

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Remer, Ralf, and Reinhard Mahnke. "Application of stochastic volatility models to German DAX data." In Second International Symposium on Fluctuations and Noise, edited by Zoltan Gingl. SPIE, 2004. http://dx.doi.org/10.1117/12.544088.

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Hanson, Floyd B., and Guoqing Yan. "American Put Option Pricing for Stochastic-Volatility, Jump-Diffusion Models." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4283124.

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Zhai, Jia, and Yi Cao. "On the calibration of stochastic volatility models: A comparison study." In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2014. http://dx.doi.org/10.1109/cifer.2014.6924088.

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Reports on the topic "Factor stochastic volatility models"

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Mulligan, Casey. Robust Aggregate Implications of Stochastic Discount Factor Volatility. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10210.

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Ait-Sahalia, Yacine, and Robert Kimmel. Maximum Likelihood Estimation of Stochastic Volatility Models. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10579.

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Fernandez-Villaverde, Jesus, Pablo Guerrón-Quintana, and Juan Rubio-Ramírez. Estimating Dynamic Equilibrium Models with Stochastic Volatility. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w18399.

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Kristensen, Dennis, and Shin Kanaya. Estimation of stochastic volatility models by nonparametric filtering. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.0915.

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Hansen, Lars Peter, and Ravi Jagannathan. Assessing Specification Errors in Stochastic Discount Factor Models. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/t0153.

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Diebold, Francis, Frank Schorfheide, and Minchul Shin. Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22615.

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Alizadeh, Sassan, Michael Brandt, and Francis Diebold. High- and Low-Frequency Exchange Rate Volatility Dynamics: Range-Based Estimation of Stochastic Volatility Models. National Bureau of Economic Research, 2001. http://dx.doi.org/10.3386/w8162.

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Creal, Drew, and Jing Cynthia Wu. Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20115.

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Burnside, Craig. Identification and Inference in Linear Stochastic Discount Factor Models with Excess Returns. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w16634.

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Engle, Robert, and Joshua Rosenberg. Hedging Options in a GARCH Environment: Testing the Term Structure of Stochastic Volatility Models. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/w4958.

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