Volatility Spillovers across South African Asset Classes during Domestic and Foreign

This paper studies domestic volatility transmission in an emerging economy. Daily volatility spillover indices, relating to South African (SA) currencies, bonds and equities, are estimated using variance decompositions from a generalised vector autoregressive (GVAR) model (Pesaran and Shin 1998). The results suggest substantial time-variation in volatility linkages between October 1996 and June 2010. Typically, large increases in volatility spillovers coincide with domestic and foreign financial crises. Equities are the most important source of volatility spillovers to other asset classes. However, following the 2001 currency crisis, and up until mid-2006, currencies temporarily dominate volatility transmission. Bonds are a consistent net receiver of volatility spillovers. In comparison to similar research focussing on the United States (Diebold and Yilmaz 2010), volatility linkages between SA asset classes are relatively strong.Asset Market Linkages, Dynamic Correlation, Financial Crisis, Generlised Vector Autoregression, Variance Decomposition, Volatility Spillover.

Modelling South African Currency Crises as Structural Changes in the Volatility of the Rand

This study tests the theory that currency crises are associated with sudden large changes in the structure of foreign exchange market volatility. Due to increases in market uncertainty, crisis periods exhibit abnormally high levels of volatility. By studying short-term changes in volatility dynamics, it is possible to identify the start- and end-dates of crisis periods with a high degree of precision. We use the iterative cumulative sum of squares algorithm to detect multiple shifts in the volatility of rand returns between January 1994 and March 2009. Dummy variables controlling for the detected shifts in variance are incorporated in a GARCH modelling framework. The analysis indicates that previously identified crisis periods in the rand coincide with significant structural changes in market volatility.Currency crisis, exchange rate, volatility, ICSS algorithm, GARCH

EVALUATION OF THE 2006/7 AGRICULTURAL INPUT SUBSIDY PROGRAMME, MALAWI. FINAL REPORT

This report evaluates the 2006/7 Malawi Government Agricultural Input Subsidy Programme (AISP). The main objective of the evaluation is to assess the impact and implementation of the AISP in order to provide lessons for future interventions in growth and social protection. The evaluation combined qualitative and quantitative methods of data collection and analysis. Quantitative data were collected through a national survey in 2007 of 2,491 households who were previously interviewed in the 2004/05 Integrated Household Survey, a survey of retail shops selling inputs in six districts and data on stocks and sales from manufacturers, large-scale importers and dealers of fertilizers and seeds. The quantitative data was triangulated by qualitative data from focus group discussions with smallholder farmers in 12 districts, and key informant interviews with government staff, input distributors and beneficiary and non-beneficiary households. The analysis is based on descriptive statistics, econometric modelling and livelihood and rural economy modelling. An Interim Report in March 2007 provides fuller details of the implementation of the programme.Agribusiness, Agricultural and Food Policy, Community/Rural/Urban Development, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Productivity Analysis,

How to Optimally Constrain Galaxy Assembly Bias: Supplement Projected Correlation Functions with Count-in-cells Statistics

Most models for the connection between galaxies and their haloes ignore the possibility that galaxy properties may be correlated with halo properties other than mass, a phenomenon known as galaxy assembly bias. Yet, it is known that such correlations can lead to systematic errors in the interpretation of survey data. At present, the degree to which galaxy assembly bias may be present in the real Universe, and the best strategies for constraining it remain uncertain. We study the ability of several observables to constrain galaxy assembly bias from redshift survey data using the decorated halo occupation distribution (dHOD), an empirical model of the galaxy--halo connection that incorporates assembly bias. We cover an expansive set of observables, including the projected two-point correlation function $w_{\mathrm{p}}(r_{\mathrm{p}})$, the galaxy--galaxy lensing signal $\Delta \Sigma(r_{\mathrm{p}})$, the void probability function $\mathrm{VPF}(r)$, the distributions of counts-in-cylinders $P(N_{\mathrm{CIC}})$, and counts-in-annuli $P(N_{\mathrm{CIA}})$, and the distribution of the ratio of counts in cylinders of different sizes $P(N_2/N_5)$. We find that despite the frequent use of the combination $w_{\mathrm{p}}(r_{\mathrm{p}})+\Delta \Sigma(r_{\mathrm{p}})$ in interpreting galaxy data, the count statistics, $P(N_{\mathrm{CIC}})$ and $P(N_{\mathrm{CIA}})$, are generally more efficient in constraining galaxy assembly bias when combined with $w_{\mathrm{p}}(r_{\mathrm{p}})$. Constraints based upon $w_{\mathrm{p}}(r_{\mathrm{p}})$ and $\Delta \Sigma(r_{\mathrm{p}})$ share common degeneracy directions in the parameter space, while combinations of $w_{\mathrm{p}}(r_{\mathrm{p}})$ with the count statistics are more complementary. Therefore, we strongly suggest that count statistics should be used to complement the canonical observables in future studies of the galaxy--halo connection.Comment: Figures 3 and 4 show the main results. Published in Monthly Notices of the Royal Astronomical Societ

Previous, current, and future stereotactic EEG techniques for localising epileptic foci

INTRODUCTION: Drug-resistant focal epilepsy presents a significant morbidity burden globally, and epilepsy surgery has been shown to be an effective treatment modality. Therefore, accurate identification of the epileptogenic zone for surgery is crucial, and in those with unclear noninvasive data, stereoencephalography is required. AREAS COVERED: This review covers the history and current practices in the field of intracranial EEG, particularly analyzing how stereotactic image-guidance, robot-assisted navigation, and improved imaging techniques have increased the accuracy, scope, and use of SEEG globally. EXPERT OPINION: We provide a perspective on the future directions in the field, reviewing improvements in predicting electrode bending, image acquisition, machine learning and artificial intelligence, advances in surgical planning and visualization software and hardware. We also see the development of EEG analysis tools based on machine learning algorithms that are likely to work synergistically with neurophysiology experts and improve the efficiency of EEG and SEEG analysis and 3D visualization. Improving computer-assisted planning to minimize manual input from the surgeon, and seamless integration into an ergonomic and adaptive operating theater, incorporating hybrid microscopes, virtual and augmented reality is likely to be a significant area of improvement in the near future

Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks

WRAH 2011: Weather Radar and Hydrology International Symposium, 18-21 April 2011, University of Exeter, UKThis paper describes the application of ANNs (Artificial Neural Networks) as DDMs (Data Driven Models) to predict urban flooding in real-time based on BADC weather radar and/or rainfall data. A 123-manhole combined sewer sub-network from Keighley, West Yorkshire, UK is used to demonstrate the methodology. An ANN is configured for prediction of flooding at manholes based on rainfall. In the absence of actual flood data, the 3DNet / SIPSON simulator, which uses a conventional fluid-dynamic approach to predict flooding surcharge levels in sewer networks, is employed to provide the target data for training the ANN. Artificial rainfall profiles derived from observed data provide the input. Both flood-level analogue and flood-severity classification schemes are implemented. We also investigate the use of an ANN for nowcasting of rainfall based on the relationship between radar data and recorded rainfall history. This allows the two ANNs to be cascaded to predict flooding in real-time based on weather radar

RAPIDS: Early Warning System for Urban Flooding and Water Quality Hazards

Machine Learning in Water Systems symposium: part of AISB Annual Convention 2013, University of Exeter, UK, 3-5 April 2013Convention organised by the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB), www.aisb.org.uk/This paper describes the application of Artificial Neural Networks (ANNs) as Data Driven Models (DDMs) to predict urban flooding in real-time based on weather radar and/or raingauge rainfall data. A time-lagged ANN is configured for prediction of flooding at sewerage nodes and outfalls based on input parameters including rainfall. In the absence of observed flood data, a hydrodynamic simulator may be used to predict flooding surcharge levels at nodes of interest in sewer networks and thus provide the target data for training and testing the ANN. The model, once trained, acts as a rapid surrogate for the hydrodynamic simulator and can thus be used as part of an urban flooding Early Warning System (EWS). Predicted rainfall over the catchment is required as input, to extend prediction times to operationally useful levels. Both flood-level analogue and flood-severity classification schemes are implemented. An initial case study using Keighley, W Yorks, UK demonstrated proof-of-concept. Three further case studies for UK cities of different sizes explore issues of soil-moisture, early operation of pumps as flood-mitigation/prevention strategy and spatially variable rainfall. We investigate the use of ANNs for nowcasting of rainfall based on the relationship between radar data and recorded rainfall history; a feature extraction scheme is described. This would allow the two ANNs to be cascaded to predict flooding in real-time based on current weather radar Quantitative Precipitation Estimates (QPE). We also briefly describe the extension of this methodology to Bathing Water Quality (BWQ) prediction

Incorporating habitat distribution in wildlife disease models: conservation implications for the threat of squirrelpox on the Isle of Arran

Emerging infectious diseases are a substantial threat to native populations. The spread of disease through naive native populations will depend on both demographic and disease parameters, as well as on habitat suitability and connectivity. Using the potential spread of squirrelpox virus (SQPV) on the Isle of Arran as a case study, we develop mathematical models to examine the impact of an emerging disease on a population in a complex landscape of different habitat types. Furthermore, by considering a range of disease parameters, we infer more generally how complex landscapes interact with disease characteristics to determine the spread and persistence of disease. Specific findings indicate that a SQPV outbreak on Arran is likely to be short lived and localized to the point of introduction allowing recovery of red squirrels to pre-infection densities; this has important consequences for the conservation of red squirrels. More generally, we find that the extent of disease spread is dependent on the rare passage of infection through poor quality corridors connecting good quality habitats. Acute, highly transmissible infectious diseases are predicted to spread rapidly causing high mortality. Nonetheless, the disease typically fades out following local epidemics and is not supported in the long term. A chronic infectious disease is predicted to spread more slowly but can remain endemic in the population. This allows the disease to spread more extensively in the long term as it increases the chance of spread between poorly connected populations. Our results highlight how a detailed understanding of landscape connectivity is crucial when considering conservation strategies to protect native species from disease threats

PinR mediates the generation of reversible population diversity in Streptococcus zooepidemicus

Opportunistic pathogens must adapt to and survive in a wide range of complex ecosystems. Streptococcus zooepidemicus is an opportunistic pathogen of horses and many other animals, including humans. The assembly of different surface architecture phenotypes from one genotype is likely to be crucial to the successful exploitation of such an opportunistic lifestyle. Construction of a series of mutants revealed that a serine recombinase, PinR, inverts 114 bp of the promoter of SZO_08560, which is bordered by GTAGACTTTA and TAAAGTCTAC inverted repeats. Inversion acts as a switch, controlling the transcription of this sortase-processed protein, which may enhance the attachment of S. zooepidemicus to equine trachea. The genome of a recently sequenced strain of S. zooepidemicus, 2329 (Sz2329), was found to contain a disruptive internal inversion of 7 kb of the FimIV pilus locus, which is bordered by TAGAAA and TTTCTA inverted repeats. This strain lacks pinR and this inversion may have become irreversible following the loss of this recombinase. Active inversion of FimIV was detected in three strains of S. zooepidemicus, 1770 (Sz1770), B260863 (SzB260863) and H050840501 (SzH050840501), all of which encoded pinR. A deletion mutant of Sz1770 that lacked pinR was no longer capable of inverting its internal region of FimIV. The data highlight redundancy in the PinR sequence recognition motif around a short TAGA consensus and suggest that PinR can reversibly influence the wider surface architecture of S. zooepidemicus, providing this organism with a bet-hedging solution to survival in fluctuating environments

Design of a graphical framework for simple prototyping of pluvial flooding cellular automata algorithms

CCWI 2011: Computing and Control for the Water Industry, 5-7 September 2011, University of Exeter, UKCellular automata (CA) algorithms can be used for quickly describing models of complex systems using simple rules. CADDIES is a new EPSRC and industry-sponsored project that aims to use the computational speed of CA algorithms to produce operationally useful real/near-real time pluvial urban flood models for both 1D-sewer and 2D-surface (dual-drainage) flows. In this paper, the design of a graphical software framework for the CADDIES project is presented. This is intended to simplify the development, testing and use of CA algorithms, and to facilitate the handling of the peripheral tasks of data management and display; allowing the research users to focus on the central tasks of optimisation of CA models and algorithms themselves