Simulation and hedging oil price with geometric Brownian Motion and single-step binomial price model

This paper[1] uses the Geometric Brownian Motion (GBM) to model the behaviour of crude oil price in a Monte Carlo simulation framework. The performance of the GBM method is compared with the naïve strategy using different forecast evaluation techniques. The results from the forecasting accuracy statistics suggest that the GBM outperforms the naïve model and can act as a proxy for modelling movement of oil prices. We also test the empirical viability of using a call option contract to hedge oil price declines. The results from the simulations reveal that the single-step binomial price model can be effective in hedging oil price volatility. The findings from this paper will be of interest to the government of Nigeria that views the price of oil as one of the key variables in the national budget. JEL Classification Numbers: E64; C22; Q30 Keywords: Oil price volatility; Geometric Brownian Motion; Monte Carlo Simulation; Single-Step Binomial Price Model [1] Acknowledgement: We wish to thank the two anonymous reviewers for their insightful comments and kind considerations. Memos to: Azeez Abiola Oyedele, School of Business and Enterprise, University of the West of Scotland, Paisley Campus, Paisley PA1 2BE, Scotland, Email: [email protected]

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

Development of an open-source platform for calculating losses from earthquakes

Risk analysis has a critical role in the reduction of casualties and damages due to earthquakes. Recognition of this relation has led to a rapid rise in demand for accurate, reliable and flexible risk assessment numerical tools and software. As a response to this need, the Global Earthquake Model (GEM) started the development of an open source platform called OpenQuake, for calculating seismic hazard and risk at different scales. Along with this framework, also several other tools to support users creating their own models and visualizing their results are currently being developed, and will be made available as a Modelers Tool Kit (MTK). In this paper, a description of the architecture of OpenQuake is provided, highlighting the current data model, workflow of the calculators and the main challenges raised when running this type of calculations in a global scale. In addition, a case study is presented using the Marmara Region (Turkey) for the calculations, in which the losses for a single event are estimated, as well as probabilistic risk for a 50 years time span

Learning from Private and Public Observation of Other's Actions

We study how a continuum of agents learn about disseminated information by observing others’ actions. Every period each agent observes a public and private noisy signal centered around the aggregate action taken by the population. The public signal represents an endogenous aggregate variable such as a price or a quantity. The private signal represents the information gathered through private communication and local interactions. We identify conditions such that the average learning curve is S-shaped: learning is slow initially, intensifies rapidly, and finally converges slowly to the truth. We show that increasing public information always slows down learning in the long run and, under some conditions, reduces welfare. Lastly, optimal diffusion of information requires that agents “strive to be different”: agents need to be rewarded for choosing actions away from the population average.Learning externality; welfare cost of public information

Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065