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Relevant bibliographies by topics / Factor stochastic volatility

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Academic literature on the topic 'Factor stochastic volatility'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Factor stochastic volatility"

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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2

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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3

Escobar, Marcos, Sebastian Ferrando, and Alexey Rubtsov. "Optimal investment under multi-factor stochastic volatility." Quantitative Finance 17, no. 2 (2016): 241–60. http://dx.doi.org/10.1080/14697688.2016.1202440.

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Philipov, Alexander, and Mark E. Glickman. "Factor Multivariate Stochastic Volatility via Wishart Processes." Econometric Reviews 25, no. 2-3 (2006): 311–34. http://dx.doi.org/10.1080/07474930600713366.

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So, Mike K. P., and C. Y. Choi. "A threshold factor multivariate stochastic volatility model." Journal of Forecasting 28, no. 8 (2009): 712–35. http://dx.doi.org/10.1002/for.1123.

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Laurini, Márcio Poletti, and Roberto Baltieri Mauad. "A common jump factor stochastic volatility model." Finance Research Letters 12 (February 2015): 2–10. http://dx.doi.org/10.1016/j.frl.2014.12.009.

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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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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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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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Tauchen, George. "Stochastic Volatility in General Equilibrium." Quarterly Journal of Finance 01, no. 04 (2011): 707–31. http://dx.doi.org/10.1142/s2010139211000237.

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The connections between stock market volatility and returns are studied within the context of a general equilibrium framework. The framework rules out a priori any purely statistical relationship between volatility and returns by imposing uncorrelated innovations. The main model generates a two-factor structure for stock market volatility along with time-varying risk premiums on consumption and volatility risk. It also generates endogenously a dynamic leverage effect (volatility asymmetry), the sign of which depends upon the magnitudes of the risk aversion and the intertemporal elasticity of s

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Dissertations / Theses on the topic "Factor stochastic volatility"

1

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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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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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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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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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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Ladkau, Marcel. "Stochastic volatility Libor modeling and efficient algorithms for optimal stopping problems." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17559.

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Die vorliegende Arbeit beschäftigt sich mit verschiedenen Aspekten der Finanzmathematik. Ein erweitertes Libor Markt Modell wird betrachtet, welches genug Flexibilität bietet, um akkurat an Caplets und Swaptions zu kalibrieren. Weiterhin wird die Bewertung komplexerer Finanzderivate, zum Beispiel durch Simulation, behandelt. In hohen Dimensionen können solche Simulationen sehr zeitaufwendig sein. Es werden mögliche Verbesserungen bezüglich der Komplexität aufgezeigt, z.B. durch Faktorreduktion. Zusätzlich wird das sogenannte Andersen-Simulationsschema von einer auf mehrere Dimensionen erwe

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Crespo, Cuaresma Jesus, Florian Huber, and Luca Onorante. "The macroeconomic effects of international uncertainty shocks." WU Vienna University of Economics and Business, 2017. http://epub.wu.ac.at/5462/1/wp245.pdf.

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We propose a large-scale Bayesian VAR model with factor stochastic volatility to investigate the macroeconomic consequences of international uncertainty shocks on the G7 countries. The factor structure enables us to identify an international uncertainty shock by assuming that it is the factor most correlated with forecast errors related to equity markets and permits fast sampling of the model. Our findings suggest that the estimated uncertainty factor is strongly related to global equity price volatility, closely tracking other prominent measures commonly adopted to assess global uncertainty.

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Moura, Rodolfo Chiabai. "Spillovers and jumps in global markets: a comparative analysis." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/96/96131/tde-02082018-160351/.

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We analyze the relation between volatility spillovers and jumps in financial markets. For this, we compared the volatility spillover index proposed by Diebold and Yilmaz (2009) with a global volatility component, estimated through a multivariate stochastic volatility model with jumps in the mean and in the conditional volatility. This model allows a direct dating of events that alter the global volatility structure, based on a permanent/transitory decomposition in the structure of returns and volatilities, and also the estimation of market risk measures. We conclude that the multivariate stoch

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Books on the topic "Factor stochastic volatility"

1

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

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2

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

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3

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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4

Santis, Giorgio De. Volatility bounds for stochastic discount factors: Tests and implications from international financial markets. 1993.

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Book chapters on the topic "Factor stochastic volatility"

1

Benth, Fred Espen. "Stochastic Volatility and Dependency in Energy Markets: Multi-Factor Modelling." In Lecture Notes in Mathematics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00413-6_2.

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2

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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3

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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4

Charpin, J. P. F., and M. Cummins. "Fast Fourier Transform Option Pricing: Efficient Approximation Methods Under Multi-Factor Stochastic Volatility and Jumps." In Topics in Numerical Methods for Finance. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3433-7_7.

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5

Drobetz, Wolfgang. "Volatility bounds for stochastic discount factors on global financial markets." In Global Stock Markets. Deutscher Universitätsverlag, 2000. http://dx.doi.org/10.1007/978-3-663-08529-4_6.

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6

"An example of one-factor dynamics: the Heston model." In Stochastic Volatility Modeling. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19649-8.

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7

Diebold, Francis X., and Glenn D. Rudebusch. "Extensions." In Yield Curve Modeling and Forecasting. Princeton University Press, 2013. http://dx.doi.org/10.23943/princeton/9780691146805.003.0004.

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This chapter highlights aspects of the vibrant ongoing research program associated with the ideas developed in earlier chapters. It begins with a collage-style sketch of work involving Bayesian analysis, functional form for factor loadings, term structures of credit spreads, and nonlinearities. It then discusses in greater detail the incorporation of more than three yield factors. Next, it treats stochastic volatility in both dynamic Nelson–Siegel model (DNS) and arbitrage-free DNS (AFNS) environments, with some attention to the issue of unspanned stochastic volatility. Finally, it discusses the incorporation of macroeconomic fundamentals in their relation to bond yields. It also introduces aspects of modeling real versus nominal yields in DNS/AFNS environments, a theme treated in detail in Chapter 5.

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8

Davasligil Atmaca, Verda, and Burcu Mestav. "Bayesian Analysis of Additive Factor Volatility Models with Heavy-Tailed Distributions with Specific Reference to S&P 500 and SSEC Indices1." In Linear and Non-Linear Financial Econometrics -Theory and Practice [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93685.

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The distribution of the financial return series is unsuitable for normal distribution. The distribution of financial series is heavier than the normal distribution. In addition, parameter estimates obtained in the presence of outliers are unreliable. Therefore, models that allow heavy-tailed distribution should be preferred for modelling high kurtosis. Accordingly, univariate and multivariate stochastic volatility models, which allow heavy-tailed distribution, have been proposed to model time-varying volatility. One of the multivariate stochastic volatility (MSVOL) model structures is factor-MSVOL model. The aim of this study is to investigate the convenience of Bayesian estimation of additive factor-MSVOL (AFactor-MSVOL) models with normal, heavy-tailed Student-t and Slash distributions via financial return series. In this study, AFactor-MSVOL models that allow normal, Student-t, and Slash heavy-tailed distributions were estimated in the analysis of return series of S&P 500 and SSEC indices. The normal, Student-t, and Slash distributions were assigned to the error distributions as the prior distributions and full conditional distributions were obtained by using Gibbs sampling. Model comparisons were made by using DIC. Student-t and Slash distributions were shown as alternatives of normal AFactor-MSVOL model.

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Mushunje, Leonard, Maxwell Mashasha, and Edina Chandiwana. "Estimating Short-Term Returns with Volatilities for High Frequency Stock Trades in Emerging Economies Using Gaussian Processes (GPs)." In Investment Strategies in Emerging New Trends in Finance. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96486.

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Fundamental theorem behind financial markets is that stock prices are intrinsically complex and stochastic in nature. One of the complexities is the volatilities associated with stock prices. Price volatility is often detrimental to the return economics and thus investors should factor it in when making investment decisions, choices, and temporal or permanent moves. It is therefore crucial to make necessary and regular stock price volatility forecasts for the safety and economics of investors’ returns. These forecasts should be accurate and not misleading. Different traditional models and methods such as ARCH, GARCH have been intuitively implemented to make such forecasts, however they fail to effectively capture the short-term volatility forecasts. In this paper we investigate and implement a combination of numeric and probabilistic models towards short-term volatility and return forecasting for high frequency trades. The essence is that: one-day-ahead volatility forecasts were made with Gaussian Processes (GPs) applied to the outputs of a numerical market prediction (NMP) model. Firstly, the stock price data from NMP was corrected by a GP. Since it not easy to set price limits in a market due to its free nature, and randomness of the prices, a censored GP was used to model the relationship between the corrected stock prices and returns. To validate the proposed approach, forecasting errors were evaluated using the implied and estimated data.

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Conference papers on the topic "Factor stochastic volatility"

1

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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Reports on the topic "Factor stochastic volatility"

1

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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