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Bayesian Analysis of Stochastic Volatility Models
, 1994
"... this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener alized ARCH ..."
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Cited by 548 (25 self)
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this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener alized ARCH (GARCH) models [see Bollerslev, Chou, and Kroner (1992) for a survey of ARCH modeling], both the mean and logvolatility equations have separate error terms. The ease of evaluating the ARCH likelihood function and the ability of the ARCH specification to accommodate the timevarying volatility found in many economic time series has fostered an explosion in the use of ARCH models. On the other hand, the likelihood function for stochastic volatility models is difficult to evaluate, and hence these models have had limited empirical application
Estimating the stochastic discount factor without a utility function.Mimeographed, Graduate School of Economics, Getulio Vargas Foundation
, 2008
"... Os artigos publicados são de inteira responsabilidade de seus autores. As opiniões neles emitidas não exprimem, necessariamente, o ponto de vista da Fundação Getulio Vargas. ..."
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Cited by 4 (2 self)
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Os artigos publicados são de inteira responsabilidade de seus autores. As opiniões neles emitidas não exprimem, necessariamente, o ponto de vista da Fundação Getulio Vargas.
MODÈLES DE PRIX
"... Introduction. Professors Scholes and Merton and I discovered the …rst problem with the formula early on. Although our search for the formula was an academic e¤ort, and our main goal was to …nd the truth, we did try to use it to make money. We applied the formula to some warrants and found some wher ..."
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Introduction. Professors Scholes and Merton and I discovered the …rst problem with the formula early on. Although our search for the formula was an academic e¤ort, and our main goal was to …nd the truth, we did try to use it to make money. We applied the formula to some warrants and found some where price seemed lower than value.
Correlation Timing in Asset Allocation with Bayesian Learning
, 2008
"... This paper assesses the relative economic value of volatility and correlation timing in the context of asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing a set of multivariate models based on Dynamic Conditional Correlatio ..."
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This paper assesses the relative economic value of volatility and correlation timing in the context of asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing a set of multivariate models based on Dynamic Conditional Correlation (DCC) model of Engle (2002). Our analysis takes a Bayesian approach in both estimation and asset allocation. We develop a new MCMC estimation algorithm for the DCC model, which is key for evaluating the optimal portfolio decision of a riskaverse investor in a Bayesian asset allocation framework with CRRA utility. The allocation strategies are designed to account for parameter uncertainty, Bayesian learning as well as model risk by constructing combined forecasts across a large set of volatility and correlation speci
cations using Bayesian Model Averaging. We
nd that in foreign exchange markets there is substantial economic value in timing correlations in addition to the economic value of volatility timing; with daily rebalancing, correlation timing can add up to 350 basis points per annum to the 500 basis points of volatility timing. This result is robust to reasonably high transaction costs as well as parameter uncertainty, alternative volatility speci
cations, diagonal correlation structure and asymmetric correlations.
c: AXA Investment Managers Preliminary and Incomplete
, 2007
"... This paper assesses the relative economic value of volatility and correlation risk in the context of multivariate dynamic asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing the multivariate Asymmetric Dynamic Conditional Co ..."
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This paper assesses the relative economic value of volatility and correlation risk in the context of multivariate dynamic asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing the multivariate Asymmetric Dynamic Conditional Correlation (ADCC) model of Cappiello, Engle and Sheppard (2006). Our statistical analysis develops a new Bayesian estimation algorithm for the ADCC model, provides a ranking of alternative model speci
cations in a way that accounts for parameter uncertainty, and constructs combined forecasts across a large set of correlation and volatility speci
cations using Bayesian Model Averaging. More importantly, we assess the economic value of volatility and correlation timing for the optimal portfolio decision of a risk averse investor in a dynamic meanvariance framework. We
nd that in foreign exchange markets there is substantial economic value in timing correlations in addition to the economic value of volatility timing; the former can add up to 350 basis points per annum to the 500 basis points of the latter. This result is robust to reasonably high transaction costs as well as alternative volatility speci
cations, diagonal correlation structure and asymmetric correlations.
Correlation Timing in Asset Allocation with Bayesian Learning
, 2008
"... This paper assesses the relative economic value of volatility and correlation timing in the context of asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing a set of multivariate models based on Dynamic Conditional Correlation ..."
Abstract
 Add to MetaCart
This paper assesses the relative economic value of volatility and correlation timing in the context of asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing a set of multivariate models based on Dynamic Conditional Correlation (DCC) model of Engle (2002). Our analysis takes a Bayesian approach in both estimation and asset allocation. We develop a new MCMC estimation algorithm for the DCC model, which is key for evaluating the optimal portfolio decision of a riskaverse investor in a Bayesian asset allocation framework with CRRA utility. The allocation strategies are designed to account for parameter uncertainty, Bayesian learning as well as model risk by constructing combined forecasts across a large set of volatility and correlation speci…cations using Bayesian Model Averaging. We …nd that in foreign exchange markets there is substantial economic value in timing correlations in addition to the economic value of volatility timing; with daily rebalancing, correlation timing can add up to 350 basis points per annum to the 500 basis points of volatility timing. This result is robust to reasonably high transaction costs as well as parameter uncertainty, alternative volatility speci…cations, diagonal correlation structure and asymmetric correlations.