The Savuti-Mababe-Linyanti ecosystem of northern Botswana: policy implications for management and conservation of an unmodified ecosystem of global scientific significance

The Savuti-Mababe-Linyanti ecosystem (SMLE) consists of extensive woodland landscapes between the Okavango Delta and the Linyanti Swamps (Figure 1) and has a great diversity of seasonal habitats from the extensive pristine wetlands of the Okavango Delta and the Linyanti Swamps to extensive pristine mopane, sandveld and riparian woodlands, as well as the extensive open grasslands and savanna of the Mababe Depression (see the Vegetation and Wildlife Habitats of the Savuti-Mababe-Linyanti ecosystem - Sianga and Fynn in review; Figure 1). This great heterogeneity in functional seasonal habitats, combined with few barriers to wildlife movement and little modification by artificial water, results in exceptional niche diversity for wildlife, which supports great diversity of wildlife and key populations of rare species such as wild dog, roan and sable antelope and eland. A key factor underlying the functional nature of the landscapes of the SMLE is that a large proportion of the woodland landscapes occur greater than 15km from water during the dry season, well beyond the maximum foraging distance from water of the more mobile bull elephant herds (and other large herbivores). This large distance from available water during the dry season creates a spatial refuge in these landscapes where vegetation is spared from excessive impact and degradation by large herbivore populations and also provides niches for rare herbivores that are dependent on these back-country woodlands far from water, such as roan and sable antelope and eland. Of interest is that the greatest proportion of the SMLE is outside of Chobe National Park and Moremi Game Reserve, being mainly in the wildlife management areas of NG 14, 15, 16, 18, 20, 21, 22, 23 and CH1,2. This emphasizes that wildlife management areas play a critical role in maintaining the functional nature and wildlife diversity of the northern conservation area of Botswana

Semi-parametric Expected Shortfall Forecasting

Intra-day sources of data have proven effective for dynamic volatility and tail risk estimation. Expected shortfall is a tail risk measure, that is now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to indirectly model expected shortfall, is generalised to incorporate information on the intra-day range. An asymmetric Gaussian density model error formulation allows a likelihood to be developed that leads to semiparametric estimation and forecasts of expectiles, and subsequently of expected shortfall. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation, while their performance is assessed via a simulation study. The proposed models compare favourably with a large range of competitors in an empirical study forecasting seven financial return series over a ten year period

Nanomaterial structure determination using XUV diffraction

Diffraction using coherent XUV radiation is used to study the structure of nanophotonic materials, in this case an ordered array of 196nm spheres. Crystal structure and defects are visible, and the nanomaterial dielectric constant determined

Bayesian Semi-parametric Expected Shortfall Forecasting in Financial Markets

Bayesian semi-parametric estimation has proven effective for quantile estimation in general and specifically in financial Value at Risk forecasting. Expected short-fall is a competing tail risk measure, involving a conditional expectation beyond a quantile, that has recently been semi-parametrically estimated via asymmetric least squares and so-called expectiles. An asymmetric Gaussian density is proposed allowing a likelihood to be developed that leads to Bayesian semi-parametric estimation and forecasts of expectiles and expected shortfall. Further, the conditional autoregressive expectile class of model is generalised to two fully nonlinear families. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation in these families. The proposed models are clearly favoured in an empirical study forecasting eleven financial return series: clear evidence of more accurate expected shortfall forecasting, compared to a range of competing methods is found. Further, the most favoured models are those estimated by Bayesian methods

Bayesian Assessment of Dynamic Quantile Forecasts

Methods for Bayesian testing and assessment of dynamic quantile forecasts are proposed. Specifically, Bayes factor analogues of popular frequentist tests for independence of violations from, and for correct coverage of a time series of, quantile forecasts are developed. To evaluate the relevant marginal likelihoods involved, analytic integration methods are utilised when possible, otherwise multivariate adaptive quadrature methods are employed to estimate the required quantities. The usual Bayesian interval estimate for a proportion is also examined in this context. The size and power properties of the proposed methods are examined via a simulation study, illustrating favourable comparisons both overall and with their frequentist counterparts. An empirical study employs the proposed methods, in comparison with standard tests, to assess the adequacy of a range of forecasting models for Value at Risk (VaR) in several financial market data series

Bayesian time-varying quantile forecasting for Value-at-Risk in financial markets

Recently, Bayesian solutions to the quantile regression problem, via the likelihood of a Skewed-Laplace distribution, have been proposed. These approaches are extended and applied to a family of dynamic conditional autoregressive quantile models. Popular Value at Risk models, used for risk management in finance, are extended to this fully nonlinear family. An adaptive Markov chain Monte Carlo sampling scheme is adapted for estimation and inference. Simulation studies illustrate favourable performance, compared to the standard numerical optimization of the usual nonparametric quantile criterion function, in finite samples. An empirical study generating Value at Risk forecasts for ten major financial stock indices finds significant nonlinearity in dynamic quantiles and evidence favoring the proposed model family, for lower level quantiles, compared to a range of standard parametric volatility models, a semi-parametric smoothly mixing regression and some nonparametric risk measures, in the literature

Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisi

Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisi

How does the phrasing of house edge information affect gamblers’ perceptions and level of understanding? A registered report

The provision of information to consumers is a common input to tackling various public health issues. By comparison to the information given on food and alcohol products, information on gambling products is either not given at all, or shown in low-prominence locations in a suboptimal format, e.g. the ‘return-to-player’ format, ‘this game has an average percentage payout of 90%’. Some previous research suggests that it would be advantageous to communicate this information via the ‘house edge’ format instead: the average loss from a given gambling product, e.g. ‘this game keeps 10% of all money bet on average’. However, previous empirical work on the house edge format only uses this specific phrasing, and there may be better ways of communicating house edge information. The present work experimentally tested this original phrasing of the house edge against an alternative phrasing that has also been proposed, ‘on average this game is programmed to cost you 10% of your stake on each bet’, while both phrasings were also compared against equivalent return-to-player information (N = 3333 UK-based online gamblers). The two dependent measures were gamblers’ perceived chances of winning and a measure of participants’ correct understanding. Preregistered Stage 1 protocol: https://osf.io/5npy9 (date of in-principle acceptance: 28/11/2022). The alternative house edge phrasing resulted in the lowest perceived chances of winning, but the original phrasing had the highest rate of correct understanding. Compared to return-to-player information, the original phrasing had both lower perceived chances of winning and higher rates of correct understanding, while the alternative phrasing had only lower perceived chances of winning. These results replicated prior work on the advantages of the original house edge phrasing over return-to-player information, while showing that the alternative house edge phrasing has advantageous properties for gamblers’ perceived chances of winning only. The optimal communication of risk information can act as an input to a public health approach to reducing gambling-related harm