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Twitter mood predicts the stock market.

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

Features of Financial Returns Modelling Price Volatility Forecasting Standard Deviations The Accuracy of Autocorrelation Estimates Testing the Random Walk Hypothesis Forecasting Trends in Prices Evidence Against the Efficiency of Futures Markets Valuing Options Appendix: A Computer Program for Modelling Financial Time Series.

Modelling Financial Time Series

http://www.cirano.qc.ca/pdf/publication/2004s-62.pdf

Common and fundamental factors in stock returns of Canadian oil and gas companies

This textbook provides an introduction to econometrics through a grounding in probability theory and statistical inference. The emphasis is on the concepts and ideas underlying probability theory and statistical inference, and on motivating the learning of them both at a formal and an intuitive level. It encourages the mastering of fundamental concepts and theoretical perspectives which guide the formulation and solution of problems in econometric modelling. This makes it an ideal introduction to empirical econometric modelling and the more advanced econometric literature. It is recommended for use on courses giving students a thorough grounding in econometrics at undergraduate or graduate level.

Statistical Foundations of Econometric Modelling

The lead–lag relationship in both returns and volatilities between spot and futures markets has been investigated extensively in the financial economics literature. Only a limited number of such studies have appeared on forward markets, primarily due to the lack of easy access to empirical data. This paper uses a unique database in over-the-counter Forward Freight Agreements (FFA) to investigate the issue. The underlying commodity is non-storable, being that of a shipping service, with the additional feature of transactions costs being higher in the spot market in comparison to the forward market. These features have interesting implications for the markets. At the practical level, the better understanding of the mean and variance dynamics can improve risk management and budget planning decisions.

Market interactions in returns and volatilities between spot and forward shipping freight markets

This paper investigates the nature of seasonality (deterministic and/or stochastic) in dry bulk freight rates, and measures and compares it across freight rates of different vessel sizes (Capesize, Panamax and Handysize), contract duration (spot, 1-year and 3-year time charters) and market conditions (peaks and troughs). Although, there is no evidence of stochastic seasonality, deterministic seasonality in freight rates is found to be varying from −18.2% to 15.3% in individual months within a year. Spot rates for larger vessels exhibit higher seasonal fluctuations compared to smaller vessels, although differences in seasonal fluctuations between sectors are eliminated as the contract duration increases. Also, for each vessel size, the seasonality declines as the contract duration rises. Asymmetries in seasonal fluctuations in freight rates over different market conditions are attributed to the high and low elasticities of supply expected under the respective market conditions. The results have implications for tactical shipping operations such as timing of dry-docking, chartering strategies and switching between freight markets.

Seasonality patterns in dry bulk shipping spot and time charter freight rates

This paper examines volatility as a measure of risk in the dry-bulk ship market. In particular, it compares volatility estimates between time-charter and spot rates. Volatility is also compared between different sizes of dry-bulk vessels. It is found that time-charters are more volatile than spot rates, and small vessels are less risky than larger ones when spot rates are used. The policy implications for ship owners who are risk-averse are to utilise the spot market in preference to time charters, and to invest in smaller vessels rather than large ones.

Comparisons of Volatility in the Dry-Cargo Ship Sector. Spot versus Time-Charters, and Smaller Versus Larger Vessels

This paper examines the dynamics of conditional volatilities in the world dry-bulk market for second-hand ships. In particular, it models and compares volatility estimates between different size vessels using monthly data. The recently developed class of autoregressive conditional heteroskedasticity (ARCH) models are utilized for this purpose. It is found that broadly speaking prices of small vessels are less volatile than larger ones, and the nature of these volatilities vary across sizes. Panamax volatilities are mostly driven by old ‘news’, while new shocks are more important for Handysize and Capesize volatilities. Furthermore, conditional volatilities of Handysize and Panamax prices are positively related to interest rates and Capesize to time-charters.

The dynamics of time-varying volatilities in different size second-hand ship prices of the dry-cargo sector

This article investigates the unbiasedness hypothesis of futures prices in the freight futures market. Being the only market whose underlying asset is a service, it sets it apart from other markets investigated so far in the literature. Cointegration techniques, employed to examine this hypothesis, indicate that futures prices one and two months before maturity are unbiased forecasts of the realized spot prices, whereas a bias exists in the three-months futures prices. This mixed evidence is in agreement with studies in other markets and suggests that the acceptance or rejection of unbiasedness depends on the idiosyncrasies of the market under investigation and on the time to maturity of the contract. Despite the existence of a bias in the three-months prices, futures prices for all maturities are found to provide forecasts of the realized spot prices that are superior to forecasts generated from error correction, ARIMA, exponential smoothing, and random walk models. Hence it appears that users of the BIFFEX market receive accurate signals from the futures prices (regarding the future course of cash prices) and can use the information generated by these prices to guide their physical market decisions. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 353–376, 1999

Manolis G. Kavussanos

Papers

Market interactions in returns and volatilities between spot and forward shipping freight markets

Seasonality patterns in dry bulk shipping spot and time charter freight rates

Comparisons of Volatility in the Dry-Cargo Ship Sector. Spot versus Time-Charters, and Smaller Versus Larger Vessels

The dynamics of time-varying volatilities in different size second-hand ship prices of the dry-cargo sector

The forward pricing function of the shipping freight futures market