F orecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization, and risk management are examples of implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Real...

In this paper we develop a novel quantile double AR model for modelling financial time series. This is done by specifying a generalized lambda distribution to the quantile function of the location-scale double autoregressive model developed in Ling (2004, 2007). Model parameter estimation uses MCMC Bayesian methods. A novel simulation technique is introduced for forecasting the conditional dist...

Investors’ search for successful forecasting models leads the data generating process for financial returns to change over time which means that individual return forecasting models can at best hope to uncover evidence of ‘local’ predictability. We illustrate this point on a suite of forecasting models used to predict US stock returns and propose an adaptive forecast combination approach. Most ...

To take into account different character of distinct segments of non-stationary financial time series the multi-agent system based forecasting algorithm is suggested. The primary goal of present paper is to introduce methodological findings that could help to reduce one step ahead forecasting error. In contrast to previous investigation [6], instead of single prediction rule we use a system of ...

The motivation of this study is the research of an originally engineering field, but with important implications and applications for economics, in general and finance, in particular. Forecasting financial time series is the goal of many studies that combine concepts from various disciplines, in terms of classical theories or of the latest approaches, forming a point of great interest. Financia...

We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. We call the model Dynamic Factor GARCH, as the information contained in large macroeconomic or financial datasets is captured by a few dynamic common factors, which we assume being conditionally heteroskedastic. After describing the estimation of the model, we present simulation res...

Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies (in U.S. dollars) are related to the stock price changes of these companies. The authors’ findings indicate, in the financial services context, nega...

This paper introduces a method based on various linear and nonlinear state space models that are used to extract global stochastic financial trends (GST) out of non-synchronous financial data. More specifically, these models are constructed to take advantage of the intraday arrival of closing information coming from different international markets to improve volatility description and forecasti...

The recently introduced class of simultaneous graphical dynamic linear models (SGDLMs) defines an ability to scale on-line Bayesian analysis and forecasting to higher-dimensional time series. This paper advances the methodology of SGDLMs, developing and embedding a novel, adaptive method of simultaneous predictor selection in forward filtering for on-line learning and forecasting. The advances ...

How do people form expectations about the future? We use amateur and expert investors' financial asset prices to study this question. Three experiments contrast rational assumption from neoclassical economics (investors forecast according theory) against two psychological theories of expectation formation—behaviorally informed understand empirical market anomalies expect these occur) narrative ...