HEDIBERT FREITAS LOPES

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Agora exibindo 1 - 5 de 5
  • Artigo Científico
    Stochastic Volatility Models with Skewness Selection
    (2024) Martins, Igor; HEDIBERT FREITAS LOPES
    This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks’ mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.
  • Artigo Científico
    Walk on the Wild Side: Temporarily Unstable Paths and Multiplicative Sunspots
    (2019) Ascari, Guido; Bonomolo, Paolo; HEDIBERT FREITAS LOPES
    We propose a generalization of the rational expectations framework to allow for temporarily unstable paths. Our approach introduces multiplicative sunspot shocks and it yields drifting parameters and stochastic volatility. Then, we provide an econometric strategy to estimate this generalized model on the data. The methodology allows the data to choose between different possible alternatives: determinacy, indeterminacy, and temporary instability. We apply our methodology to US inflation dynamics in the 1970s through the lens of a simple New Keynesian model. When temporarily unstable paths are allowed, the data unambiguously select them to explain the stagflation period in the 1970s.
  • Artigo Científico
    On the Long-Run Volatility of Stocks
    (2018) Carvalho, Carlos M.; HEDIBERT FREITAS LOPES; McCulloch, Robert E.
    In this article, we investigate whether or not the volatility per period of stocks is lower over longer horizons.Taking the perspective of an investor, we evaluate the predictive variance of k-period returns under differentmodel and prior specifications. We adopt the state-space framework of Pástor and Stambaugh to model thedynamics of expected returns and evaluate the effects of prior elicitation in the resulting volatility estimates.Part of the developments includes an extension that incorporates time-varying volatilities and covariancesin a constrained prior information set-up. Our conclusion for the U.S. market, under plausible prior specifi-cations, is that stocks are less volatile in the long run. Model assessment exercises demonstrate the modelsand priors supporting our main conclusions are in accordance with the data. To assess the generality of theresults, we extend our analysis to a number of international equity indices. Supplementary materials for thisarticle are available online.
  • Artigo Científico
    Particle learning for Bayesian semi-parametric stochasticvolatility model
    (2019) Virbickaitė, Audronė; HEDIBERT FREITAS LOPES; Ausín, M. Concepción; Galeano, Pedro
    This article designs a Sequential Monte Carlo (SMC) algorithm for estimation of Bayesian semi-parametric Stochastic Volatility model for financial data. In particular, it makes use of one of the most recent particle filters called Particle Learning (PL). SMC methods are especially well suited for state-space models and can be seen as a cost-efficient alternative to Markov Chain Monte Carlo (MCMC), since they allow for online type inference. The posterior distributions are updated as new data is observed, which is exceedingly costly using MCMC. Also, PL allows for consistent online model comparison using sequential predictive log Bayes factors. A simulated data is used in order to compare the posterior outputs for the PL and MCMC schemes, which are shown to be almost identical. Finally, a short real data application is included.
  • Artigo Científico
    Deep learning models for inflation forecasting
    (2023) Theoharidis, Alexandre Fernandes; DIOGO ABRY GUILLEN; HEDIBERT FREITAS LOPES; Hosszejni, Darjus
    We propose a hybrid deep learning model that merges Variational Autoencoders and Convolutional LSTM Networks (VAE-ConvLSTM) to forecast inflation. Using a public macroeconomic database that comprises 134 monthly US time series from January 1978 to December 2019, the proposed model is compared against several popular econometric and machine learning benchmarks, including Ridge regression, LASSO regression, Random Forests, Bayesian methods, VECM, and multilayer perceptron. We find that VAE-ConvLSTM outperforms the competing models in terms of consistency and out-of-sample performance. The robustness of such conclusion is ensured via cross-validation and Monte-Carlo simulations using different training, validation, and test samples. Our results suggest that macroeconomic forecasting could take advantage of deep learning models when tackling nonlinearities and nonstationarity, potentially delivering superior performance in comparison to traditional econometric approaches based on linear, stationary models.