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Special Issue of Quantitative Finance on the ‘23rd Forecasting Financial Markets Conference’
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Abstract
Forecasting Financial Markets (FFM) is an International Conference on Quantitative Finance, which has been held in May/June each year since 1994. Since its inception, the Conference has grown in scope and stature to become a key international meeting point for those interested in Quantitative Finance, with the participation of both market professionals and academics, from across the globe. The purpose of this special issue is to present a selection of papers that make significant contributions in terms of methodology, techniques and econometrics in the field of Quantitative Finance. This issue is dedicated to the memory of our friend, mentor, supervisor, colleague and FFM organizer, Christian Dunis, who sadly passed away in May 2017. The first paper by McGroarty, Dao and Urquhart, studies a large data-set of ultra high-frequency transaction prices, time-stamped to the millisecond on the S&P500 index and its two most liquid tracking ETFs. They find that their lead–lag relationship is affected by the rate of information arrival whose proxy is the unexpected trading volume of these instruments. They discover that when information arrives, the leadership of the leading instrument may strengthen or weaken depending on whether the leading or lagging instrument responds to that information. The second paper, by Liu, Pantelous and Mettenheim, forecasts and trades the volatility of the S&P500 index and the liquid SPY ETF, VIX index and VXX ETN using high frequency data. In their approach, they include a test of their forecasts through trading an appropriate volatility derivative. As a method they use parametric, artificial intelligence models and combinations of these models. In the third paper, Huptas examines the out-of-sample point and density forecasting performance of Bayesian Autoregressive Conditional Volume (ACV) models for intra-day volume data. Based on 5-min traded volume data, for stocks quoted on the Warsaw Stock Exchange, they find that in terms of point forecasts, the linear ACV models significantly outperform benchmarks such as the naïve or Rolling Means methods but not necessarily Autoregressive Moving Average models. In addition to that, the study finds that point forecasts obtained within the exponential error ACV model are significantly superior to those calculated in other competing structures for which Burr or generalized gamma distributions are specified. Zhao, Stasinakis, Sermpinis and Shi investigate if artificial intelligence models can lead to statistical and economically significant benefits in portfolio management decisions. They combine Multi-Layer Perceptron, Recurrent and Psi Sigma Network neural networks with a dynamic asymmetric copula model and capture the dependence structure across ETF returns. Weekly rebalanced portfolios are obtained and compared using the traditional mean-variance and the mean-CVaR portfolio optimization approach. In the fifth paper Giner, Mendoza and Morini introduce a tool which translates a correlation matrix into an equivalent probability matrix and vice versa. Thus, the correlation coefficient parameter is more understandable in terms of joint probability of two stocks’ returns, and much more useful in terms of the information it provides. The accuracy of this tool is measured theoretically and some applications from the practitioners’ point of view are provided. Goyal, Kallinterakis, Kambouridis and Laws test for the performance of a series of volatility forecasting models (GARCH 1,1; EGARCH 1,1; CGARCH) in the context of several indices from the two oldest cross-border exchanges (Euronext; OMX). Their findings indicate that the EGARCH (1,1) model outperforms the other two, both before and after the outbreak of the global financial crisis. Controlling for the presence of feedback traders, the accuracy of the EGARCH (1,1) model is not affected, something further confirmed for both the pre and post the global finance crisis samples. BenSaïda, Boubaker and Nguyen develop a tractable regime-switching version of the copula functions to model the inter-markets linkages during turmoil and normal periods, while taking into account structural changes. Markov regime-switching C-vine and D-vine decompositions of the Student’s t copula are proposed and applied to returns on diversified portfolios of stocks, represented by the G7 stock market indices. The empirical results show evidence of regime shifts in the dependence structure with high contagion risk during crisis periods. Both the Cand D-vines highly outperform the multivariate Student’s t copula, which suggests that the shock transmission path is as important as the dependence itself, and better detected with vine copula decomposition. In the eighth paper, Verousis and Voukelatos investigate whether the cross-sectional dispersion of stock returns as a Special Issue of Quantitative Finance on the ‘23rd Forecasting Financial Markets Conference’