3,034 research outputs found
Extracting the Italian output gap: a Bayesian approach
During the last decades particular effort has been directed towards
understanding and predicting the relevant state of the business cycle with the
objective of decomposing permanent shocks from those having only a transitory
impact on real output. This trend--cycle decomposition has a relevant impact on
several economic and fiscal variables and constitutes by itself an important
indicator for policy purposes. This paper deals with trend--cycle decomposition
for the Italian economy having some interesting peculiarities which makes it
attractive to analyse from both a statistic and an historical perspective. We
propose an univariate model for the quarterly real GDP, subsequently extended
to include the price dynamics through a Phillips curve. This study considers a
series of the Italian quarterly real GDP recently released by OECD which
includes both the 1960s and the recent global financial crisis of 2007--2008.
Parameters estimate as well as the signal extraction are performed within the
Bayesian paradigm which effectively handles complex models where the parameters
enter the log--likelihood function in a strongly nonlinear way. A new Adaptive
Independent Metropolis--within--Gibbs sampler is then developed to efficiently
simulate the parameters of the unobserved cycle. Our results suggest that
inflation influences the Output Gap estimate, making the extracted Italian OG
an important indicator of inflation pressures on the real side of the economy,
as stated by the Phillips theory. Moreover, our estimate of the sequence of
peaks and troughs of the Output Gap is in line with the OECD official dating of
the Italian business cycle
Volatility forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1
Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information
Most asset returns exhibit high volatility and its persistence. Heuristically, this paper focuses on the role of surprising information in high volatility processes and indicates that dismissing surprising information may lead to considerable loss in forecast accuracy. In response, this paper considers the corresponding extension of the modified MDH to surprising information, and proposes a bivariate stochastic volatility model incorporating surprising information in the volatility equations (BSV-SI), which is also designed to capture the dynamics of returns and trading volume. Using the South Korea stock index and trading volume series, it turns out that performance of the onestep- ahead forecasts of the BSV-SI model is apparently superior to those of other competitive models.Volatility forecasting, Bivariate stochastic volatility model with surprising information, Modified mixture of distribution hypothesis, Realized volatility models, Markov Chain Monte Carlo (MCMC)
Volatility Forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
Volatility Forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3,4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
Volatility Forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
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