Discovering the hidden structure of financial markets through bayesian modelling

Understanding what is driving the price of a financial asset is a question that is currently mostly unanswered. In this work we go beyond the classic one step ahead prediction and instead construct models that create new information on the behaviour of these time series. Our aim is to get a better understanding of the hidden structures that drive the moves of each financial time series and thus the market as a whole. We propose a tool to decompose multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving their underlying variability. The methodology we introduce goes beyond the direct model forecast. Indeed, since our model continuously adapts its variables and coefficients, we can study the time series of coefficients and selected variables. We also present a model to construct the causal graph of relations between these time series and include them in the exogenous factors. Hence, we obtain a model able to explain what is driving the move of both each specific time series and the market as a whole. In addition, the obtained graph of the time series provides new information on the underlying risk structure of this environment. With this deeper understanding of the hidden structure we propose novel ways to detect and forecast risks in the market. We investigate our results with inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time. We also go in more details on the economic interpretation of the new variables and discuss the created graph structure of the market.Open Acces

Assessing the skill of a high-resolution marine biophysical model using geostatistical analysis of mesoscale ocean chlorophyll variability from field observations and remote sensing

© The Author(s), 2021. This article is distributed under the terms of the Creaive Commons Attribution License. The definitive version was published in Eveleth, R., Glover, D. M., Long, M. C., Lima, I. D., Chase, A. P., & Doney, S. C. . Assessing the skill of a high-resolution marine biophysical model using geostatistical analysis of mesoscale ocean chlorophyll variability from field observations and remote sensing. Frontiers in Marine Science, 8, (2021): 612764, https://doi.org/10.3389/fmars.2021.612764.High-resolution ocean biophysical models are now routinely being conducted at basin and global-scale, opening opportunities to deepen our understanding of the mechanistic coupling of physical and biological processes at the mesoscale. Prior to using these models to test scientific questions, we need to assess their skill. While progress has been made in validating the mean field, little work has been done to evaluate skill of the simulated mesoscale variability. Here we use geostatistical 2-D variograms to quantify the magnitude and spatial scale of chlorophyll a patchiness in a 1/10th-degree eddy-resolving coupled Community Earth System Model simulation. We compare results from satellite remote sensing and ship underway observations in the North Atlantic Ocean, where there is a large seasonal phytoplankton bloom. The coefficients of variation, i.e., the arithmetic standard deviation divided by the mean, from the two observational data sets are approximately invariant across a large range of mean chlorophyll a values from oligotrophic and winter to subpolar bloom conditions. This relationship between the chlorophyll a mesoscale variability and the mean field appears to reflect an emergent property of marine biophysics, and the high-resolution simulation does poorly in capturing this skill metric, with the model underestimating observed variability under low chlorophyll a conditions such as in the subtropics.This work was supported in part by the National Aeronautics and Space Administration (NASA) as part of the North Atlantic Aerosol and Marine Ecosystems Study (NAAMES; NASA grant 80NSSC18K0018). The CESM project is supported by the National Science Foundation and the Office of Science (BER) of the United States Department of Energy. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation and other agencies. This research was enabled by CISL compute and storage resources

Spatial patterns and processes in a regenerating mangrove forest

The global effort to rehabilitate and restore destroyed mangrove forests is unable to keep up with the high mangrove deforestation rates which exceed the average pace of global deforestation by three to five times. Our knowledge of the underlying processes of mangrove forest regeneration is too limited in order to find suitable techniques for the restoration of degraded mangrove areas. The general objective of my dissertation was to improve mangrove restoration by understanding regeneration processes and local plant-plant interaction in a regenerating Avicennia germinans forest. The study was conducted in a high-shore mangrove forest area on the Ajuruteua peninsula, State of Para, Northern Brazil. The dwarf forest consisting of shrub-like trees is recovering from a stand-replacing event caused by a road construction in 1974 which interrupted the tidal inundation of the study area. Consequently, infrequent inundation and high porewater salinity limit tree growth and canopy closure. All trees and seedlings were stem-mapped in six 20 m x 20 m plots which were located along a tree density gradient. Moreover, height, crown extent, basal stem diameter of trees were measured. The area of herbaceous ground vegetation and wood debris were mapped as well. The mapped spatial distribution of trees, seedlings and covariates was studied using point pattern analysis and point process models, such as Gibbs and Thomas point process, in order to infer underlying ecological processes, such as seed dispersal, seedling establishment, tree recruitment and tree interaction. In the first study (chapter 2), I analyzed the influence of abiotic and biotic factors on the seedling establishment and tree recruitment of A. germinans during the recolonization of severely degraded mangrove sites using point process modeling. Most seedlings established adjacent to adult trees especially under their crown cover. Moreover, seedling density was higher within patches of the herbaceous salt-marsh plants Blutaparon portulacoides and Sesuvium portulacastrum than in uncovered areas. The higher density of recruited A. germinans trees in herb patches indicated that ground vegetation did not negatively influence tree development of A. germinans. In addition, tree recruitment occurred in clusters. Coarse wood debris had no apparent effect on either life stage. These results confirm that salt-marsh vegetation acts as the starting point for mangrove recolonization and indicate that the positive interaction among trees accelerates forest regeneration. In the second study (chapter 3), I analyzed how intraspecific interaction among A. germinans trees determines their growth and size under harsh environmental conditions. Interaction among a higher number of neighboring trees was positively related to the development of a focal tree. However, tree height, internode length and basal stem diameter were only positively associated in low-density forest stands (1.2 trees m-2) and not in forest stands of higher tree density (2.7 trees m-2). These results indicated a shift from facilitation, i.e. a positive effect of tree interaction, towards a balance between facilitation and competition. In the third study (chapter 4), I used point process modeling and the individual-based model mesoFON to disentangle the impact of regeneration and interaction processes on the spatial distribution of seedlings and trees. In this infrequently inundated area, propagules of A. germinans are only dispersed at a maximum distance of 3 m from their parent tree. Furthermore, there is no evidence that the following seedling establishment is influenced by trees. I was able to differentiate positive and negative tree interactions simulated by the mangrove model mesoFON regardless of dispersal processes based on static tree size information using the mark-correlation function. The results of this dissertation suggest that mangrove forest regeneration in degraded areas is a result of facilitative and not competitive interactions among mangrove trees, seedling and herbaceous vegetation. This has important implications for the restoration of degraded mangrove forest. Degraded mangrove areas are usually restored by planting a high number of evenly spaced seedlings. However, high costs constrain this approach to small areas. Assisting natural regeneration could be a less costly alternative. Herbaceous vegetation plays a crucial role in forest recolonization by entrapping propagules and possibly ameliorating harsh environmental conditions. So far only competition among mangrove trees has been considered during restoration. However, facilitative tree interactions could be utilized by planting seedling clusters in order to assist natural regeneration instead of planting seedlings evenly-spaced over large areas. This dissertation also showed that point pattern analysis and point process modeling can enable forest ecologists to describe the spatial distribution of trees as well as to infer underlying ecological processes

Spatio-temporal Modeling and Analysis for Wind Energy Applications

The promising potential of wind energy as a source for carbon-free electricity is still hampered by the uncertainty and limited predictability of the wind resource. The overarching theme of this dissertation is to leverage the advancements in statistical learning for developing a set of physics-informed statistical methods that can enrich our understanding of local wind dynamics, enhance our predictions of the wind resource and associated power, and ultimately assist in making better operational decisions. At the heart of the methods proposed in this dissertation, the wind field is modeled as a stochastic spatio-temporal process. Specifically, two sets of methods are presented. The first set of methods is concerned with the statistical modeling and analysis of the transport effect of wind—a physical property related to the prevailing flow of wind in a certain dominant direction. To unearth the influence of the transport effect, a statistical tool called the spatio-temporal lens is proposed for understanding the complex spatio-temporal correlations and interactions in local wind fields. Motivated by the findings of the spatio-temporal lens, a statistical model is proposed, which takes into account the transport effect in local wind fields by characterizing the spatial and temporal dependence in tandem. Substantial improvements in the accuracy of wind speed and power forecasts are achieved relative to several existing data-driven approaches. The second part of this dissertation comprises the development of an advanced spatio-temporal statistical model, called the calibrated regime-switching model. The proposed model captures the regime-switching dynamics in wind behavior, which are often reflected in sudden power generation ramps. Tested on 11 months of data, double-digit improvements in the accuracy of wind speed and power forecasts are achieved relative to six approaches in the wind forecasting literature. This dissertation contributes to both methodology development and wind energy applications. From a methodological point of view, the contributions are relevant to the literatures on spatiotemporal statistical learning and regime-switching modeling. On the application front, these methodological innovations can minimize the uncertainty associated with the large-scale integration of wind energy in power systems, thus, ultimately boosting the economic outlook of wind energy

Determining future aflatoxin contamination risk scenarios for corn in Southern Georgia, USA using spatio-temporal modelling and future climate simulations

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Aflatoxins (AFs) are produced by fungi in crops and can cause liver cancer. Permitted levels are legislated and batches of grain are rejected based on average concentrations. Corn grown in Southern Georgia (GA), USA, which experiences drought during the mid-silk growth period in June, is particularly susceptible to infection by Aspergillus section Flavi species which produce AFs. Previous studies showed strong association between AFs and June weather. Risk factors were developed: June maximum temperatures > 33 °C and June rainfall  33 °C and rainfall < 50 mm increased and then plateaued for both emissions scenarios. The percentage of years thresholds were exceeded was greater for RCP 8.5 than RCP 4.5. The spatial distribution of high-risk counties changed over time. Results suggest corn growth distribution should be changed or adaptation strategies employed like planting resistant varieties, irrigating and planting earlier. There were significantly more counties exceeding thresholds in 2010-2040 compared to 2000-2030 suggesting that adaptation strategies should be employed as soon as possible.Peer reviewe

A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha−1 for P and up to £81 ha−1 for K, with concurrent environmental benefits due to a reduction of 4–17 kg ha−1 applied P fertiliser when compared with uniform application.Biotechnology and Biological Sciences Research Council (BBSRC): BBS/E/C/000I0320; BBS/E/C/000I0330; BBS/E/C/000I0100