A Bayesian Time Varying Approach to Risk Neutral Density Estimation

Casarin, Roberto; Molina, Germán; Horst, Enrique ter

doi:10.1111/rssa.12386

Cited by 4 publications

(3 citation statements)

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“…The model and inference proposed in this paper are novel in some respects. As such, the paper contributes to the literature on Bayesian semiparametrics and nonparametrics for time series analysis (e.g., see Taddy and Kottas, 2009;Jensen and Maheu, 2010;Griffin and Steel, 2011;Di Lucca et al, 2013;Bassetti, Casarin and Leisen, 2014;Casarin, Molina and ter Horst, 2019;Billio, Casarin and Rossini, 2019;Nieto-Barajas and Quintana, 2016;Griffin and Kalli, 2018). The paper also innovates the Bayesian nonparametric dynamic panel model in Hirano (2002) by introducing Markov-switching and GARCH dynamics.…”

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confidence: 88%

Bayesian nonparametric panel Markov-switching GARCH models

Casarin¹,

Costantini²,

Osuntuyi³

2020

Preprint

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This paper introduces a new model for panel data with Markov-switching GARCH effects. The model incorporates a series-specific hidden Markov chain process that drives the GARCH parameters. To cope with the high-dimensionality of the parameter space, the paper exploits the cross-sectional clustering of the series by first assuming a soft parameter pooling through a hierarchical prior distribution with two-step procedure, and then introducing clustering effects in the parameter space through a nonparametric prior distribution. The model and the proposed inference are evaluated through a simulation experiment. The results suggest that the inference is able to recover the true value of the parameters and the number of groups in each regime. An empirical application to 78 assets of the SP&100 index from 6 th January 2000 to 3 rd October 2020 is also carried out by using a two-regime Markov switching GARCH model. The findings shows the presence of 2 and 3 clusters among the constituents in the first and second regime, respectively.

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confidence: 88%

Bayesian nonparametric panel Markov-switching GARCH models

Casarin¹,

Costantini²,

Osuntuyi³

2020

Preprint

Self Cite

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“…As a result, Adrian and Franzoni [1], Mumtaz and Theodoridis [20]; Casarin et al [4]; and Pacifico [21], suggest that models without time-varying beta coefficients fail to capture investors' risk aversion characteristics and may lead to inaccurate estimates of risk dynamics. Similar work on GCC and less developed stock markets is lacking.…”

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confidence: 99%

Modeling and assessing systematic risk in stock markets in major oil exporting countries

Onour¹

2021

Econ. Consult.

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Introduction. This paper aims to assess time variability of beta coefficients (systematic risk) of Capital Asset Pricing Model (CAPM) using data from five key sectors in Saudi Arabia and Kuwait stock markets. Material and methods. To assess time – varying systematic risk we employed symmetric as well as asymmetric conditional volatility specifications to account for skewness and leptkurtosis of high frequency financial time series to better specify conditional higher moments. Results & discussions. The results of the paper support significant evidence of time-varying beta coefficients for all sectors included in the study, in particular the banking sector, and relatively with a lesser degree ,the food, and the service sectors in both countries. For the banking sector in Saudi Arabia, the beta coefficients variability during the sample period estimated between (0.18 to 22.1), and also for Kuwait stock market the beta coefficient of the banking sector variability estimated between (0.16 to 22.1). This result invalidates, at least in the context of the sample country’s banking sectors, the standard application of (CAPM) that assumes constant beta coefficients. Also indicated in the paper, time-varying beta estimates are consistent with a modified version of CAPM prediction that is portfolios with wider range of beta variations expected to yield higher return values and those with lower range of beta variations yield lower returns. Conclusion. In this new context, risk is no longer is a point estimate as implied by the standard CAPM model, but it is a range of values. Our findings also show the size and the range of beta variations are sensitive to skewness and fat tailedness that characterize asset returns distribution.

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“…An increasingly popular approach to this problem is to specify regression models with time-varying coefficients (TVC) and estimate the path of their variation (see e.g., Doan et al 1984;Cogley and Sargent 2001;Cogley et al 2010;D'Agostino et al 2013;Chan 2017) 1 . Although the estimation of TVC models has been facilitated by advancements in Markov Chain Monte Carlo (MCMC) methods (e.g., Carter andKohn 1994 andChib andGreenberg 1995, and, for recent applications, Mumtaz and Theodoridis 2018;Casarin et al 2019;Pacifico 2019), it often remains a complex task that requires a careful specification of priors and relies on computationally intensive numerical techniques. For a review and a discussion of the MCMC approach to the estimation of TVC models, see Petrova (2019).…”

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confidence: 99%

Bayesian Analysis of Coefficient Instability in Dynamic Regressions

Ciapanna

Taboga

2019

Econometrics

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This paper deals with instability in regression coefficients. We propose a Bayesian regression model with time-varying coefficients (TVC) that allows to jointly estimate the degree of instability and the time-path of the coefficients. Thanks to the computational tractability of the model and to the fact that it is fully automatic, we are able to run Monte Carlo experiments and analyze its finite-sample properties. We find that the estimation precision and the forecasting accuracy of the TVC model compare favorably to those of other methods commonly employed to deal with parameter instability. A distinguishing feature of the TVC model is its robustness to mis-specification: Its performance is also satisfactory when regression coefficients are stable or when they experience discrete structural breaks. As a demonstrative application, we used our TVC model to estimate the exposures of S&P 500 stocks to market-wide risk factors: We found that a vast majority of stocks had time-varying exposures and the TVC model helped to better forecast these exposures.

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A Bayesian Time Varying Approach to Risk Neutral Density Estimation

Cited by 4 publications

References 51 publications

Bayesian nonparametric panel Markov-switching GARCH models

Bayesian nonparametric panel Markov-switching GARCH models

Modeling and assessing systematic risk in stock markets in major oil exporting countries

Bayesian Analysis of Coefficient Instability in Dynamic Regressions

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