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Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Duong, Diep; Swanson, Norman R. - 2013

Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility...

Persistent link: https://www.econbiz.de/10009771770

Bayesian forecasting of US growth using basic time varying parameter models and expectations data

Basturk, Nalan; Ceyhan, Pinar; Dijk, Herman K. van - 2014

Time varying patterns in US growth are analyzed using various univariate model structures, starting from a naive model structure where all features change every period to a model where the slow variation in the conditional mean and changes in the conditional variance are specified together with...

Persistent link: https://www.econbiz.de/10010399680

Forecasting Stock Returns Under Economic Constraints

Pettenuzzo, Davide - 2014

We propose a new approach to imposing economic constraints on forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two...

Persistent link: https://www.econbiz.de/10013064939

Forecasting Random Walks under Drift Instability

Pesaran, M. Hashem - 2011

This paper considers forecast averaging when the same model is used but estimation is carried out over different … estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or … more structural breaks. It is shown that compared to using forecasts based on a single estimation window, averaging over …

Persistent link: https://www.econbiz.de/10012714199

Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data

Shin, Minseok; Kim, Donggyu; Wang, Yazhen; Fan, Jianqing - 2021

estimation method for a high-dimensional VAR model. We apply the robust estimator to predicting large volatility matrices and … estimation and prediction methods. Using high-frequency trading data, we apply the proposed method to large volatility matrix …

Persistent link: https://www.econbiz.de/10013211439

Forecasting Random Walks Under Drift Instability

Pesaran, M. Hashem - 2011

Persistent link: https://www.econbiz.de/10012756639

High frequency vs. daily resolution : the economic value of forecasting volatility models 2nd ed

Lilla, Francesca - 2017

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...

Persistent link: https://www.econbiz.de/10011674479

High frequency vs. daily resolution : the economic value of forecasting volatility models

Lilla, Francesca - 2016

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

Persistent link: https://www.econbiz.de/10011730304

Changing risk-return profiles

Crump, Richard K.; Giannone, Domenico; Hundtofte, Sean - 2018

We show that realized volatility, especially the realized volatility of financial sector stock returns, has strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time....

Persistent link: https://www.econbiz.de/10011868395

Forecasting Indian Stock Market Index Using Singular Spectrum Analysis : A Comparison with ARIMA and Artificial Neural Network

Shukla, Suwarna - 2017

In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques...

Persistent link: https://www.econbiz.de/10012947889