In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Can volume predict Bitcoin returns and volatility? A quantiles-based approach

A recent strand in the literature emphasizes the role of news-based economic policy uncertainty (EPU) and equity market uncertainty (EMU) as drivers of oil price movements. Against this backdrop, this paper uses a kth-order nonparametric quantile causality test, to analyse whether EPU and EMU predict stock returns and volatility. Based on daily data covering the period of 2 January 1986 to 8 December 2014, we find that, for oil returns, EPU and EMU have strong predictive power over the entire distribution barring regions around the median, but for volatility, the predictability virtually covers the entire distribution, with some exceptions in the tails. In other words, predictability based on measures of uncertainty is asymmetric over the distribution of oil returns and its volatility.

/pdf/the-role-of-news-based-uncertainty-indices-in-predicting-oil-bwa23nihfc.pdf

The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method

How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques

https://www.up.ac.za/media/shared/61/WP/wp_2015_92.zp75835.pdf

Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test

https://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/148009/1/j.jeconom.2011.05.010.pdf

A consistent nonparametric test for nonlinear causality—Specification in time series regression

We propose a nonparametric test for Granger-type causality. A conceptually similar work will be Hidalgo (2000), who introduced a nonparametric Granger causality test in the frequency domain for weakly stationary linear processes. He is mainly concerned with the test under long range dependent observations, and it does not have a power against some alternatives of series with nonlinear dynamics. We construct a test statistic based on moment conditions allowing for nonlinear dependence. The test has a nontrivial power against T1/2-local alternatives where T is sample size. Its null asymptotic distribution is not normal, but we can easily calculate the critical region by simularion.

A Consistent Nonparametric Test for Causality

We investigate the seasonal unit root properties of monthly industrial production series for 16 OECD countries within the context of a structural time series model. A basic version of this model assumes that there are 11 such seasonal unit roots. We propose to use model selection criteria (AIC and BIC) to examine if one or more of these are in fact stationary. We generally find that when these criteria indicate that a smaller number of seasonal unit roots can be assumed and hence that some seasonal roots are stationary, the corresponding model also gives more accurate one-step-ahead forecasts. Copyright © 2004 John Wiley & Sons, Ltd.

Do seasonal unit roots matter for forecasting monthly industrial production

Forecasting stock price in level is very difficult because such a series is often described as an integrated process. In spite of that, a linear combination of two stock prices can be stationary around a fixed mean. Then taking both long and short position can lead to profit. This article aims to characterize the long-short strategies based on cointegration by investigating their risk-return properties.

A characterization of long-short trading strategies based on cointegration

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological evolution of the topic in the set of articles using a dynamic topic model. After calculating a topic score, we develop time series models that incorporate the score to estimate and forecast realized volatility. The results of our empirical analysis suggest that the proposed models can contribute to improving forecasting accuracy.

Yoshinori Kawasaki

Papers

A consistent nonparametric test for nonlinear causality—Specification in time series regression

A Consistent Nonparametric Test for Causality

Do seasonal unit roots matter for forecasting monthly industrial production

A characterization of long-short trading strategies based on cointegration

Forecasting Financial Market Volatility Using a Dynamic Topic Model