text: Wilderness War takes two players into the French & Indian War, 1755-1760, the climactic struggle between France and Britain for control of North America. It uses strategy cards and a point-to-point map similar to GMT´s award winning For the People and Paths of Glory. Players maneuver and fight over a map stretching from Northern Virginia to Canada. As the leader of French or British forces in North America, you will need to defend your frontier, raid your enemy´s frontier, build fortification networks through the harsh wilderness, recruit Indian allies, besiege forts and fortresses, and deal with events occurring in Europe that are above and beyond your control.
  Wilderness War includes a deck of strategy cards for conducting campaigns and incorporating the many events and personalities of the war. The French player can recruit up to sixteen different Indian tribes as allies, secure a continental alliance in Europe against Great Britain, sortie his squadron at Louisbourg, force a ministerial crisis in Londo
  
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  Tips from the Field – Proximal Tibiofibular Joint Injuries by Dr. Shannon Bauman, MD – Sports & Exercise Medicine
  One cause of lateral knee pain that I have encountered in runners, cyclists, and soccer players is an injury to the proximal tibiofibular joint. During sport, torsional stresses originating from the ankle and resistant forces created in weightbearing deliver stress through this joint. Lower limb biomechanics such as a pes cavus foot type and supination on gait may also contribute to increased stresses. An athlete’s complaint of pain and/or instability can be due to an acute or chronic sprain of the proximal tibiofibular ligament, or to a subluxation of the joint as the fibular head shifts anteriorly in knee flexion. A simple, yet effective “tip” to manage the symptoms associated with a proximal tibiofibular joint injury is to use a compression type strap (ex. Levy Patellar Strap by Trainer’s Choice) to stabilize the joint and limit stress through the proximal tibiofibular ligament. To apply this strap, I locate the proximal tibiofibular ligament, and place a pen mark on the patient’s skin two- finger- breaths below this point. I position the strap centered over my mark and secure it to the proximal lateral lower limb. This should be snug, but not too compressive to the gastrocnemius or peroneal nerve. I re-test the mechanism that would reproduce symptoms, and assess for clinical improvement. I owe credit to Dr. David Levy who taught me this great tip during my sports medicine fellowship, and I use it with good success today.
  
  22 year-old Accountant I Malissa Ashburne, hailing from Rimouski enjoys watching movies like "Edward, My Son" and Yo-yoing. Took a trip to Heritage of Mercury. AlmadÃ©n and Idrija and drives a XF.- by [url=https://1-news.net]ì˜¨ë�¼ì�¸ì¹´ì§€ë…¸[/url]
  
  Decorative Sheepskin Rug in shades of gray and brown. The circle rug pattern with a diameter of 90 cm.
  
  South Africa is a cornucopia of the platinum group metals particularly platinum and palladium. These metals have many unique physical and chemical characteristics which render them indispensable to technology and industry, the markets and the medical field. In this paper we carry out a holistic investigation on long memory (LM), structural breaks and stylized facts in platinum and palladium return and volatility series. To investigate LM we employed a wide range of methods based on time domain, Fourier and wavelet based techniques while we attend to the dual LM phenomenon using ARFIMA–FIGARCH type models, namely FIGARCH, ARFIMA–FIEGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH models. Our results suggests that platinum and palladium returns are mean reverting while volatility exhibited strong LM. Using the Akaike information criterion (AIC) the ARFIMA–FIAPARCH model under the Student distribution was adjudged to be the best model in the case of platinum returns although the ARCH-effect was slightly significant while using the Schwarz information criterion (SIC) the ARFIMA–FIAPARCH under the Normal Distribution outperforms all the other models. Further, the ARFIMA–FIEGARCH under the Skewed Student distribution model and ARFIMA–HYGARCH under the Normal distribution models were able to capture the ARCH-effect. In the case of palladium based on both the AIC and SIC, the ARFIMA–FIAPARCH under the GED distribution model is selected although the ARCH-effect was slightly significant. Also, ARFIMA–FIEGARCH under the GED and ARFIMA–HYGARCH under the normal distribution models were able to capture the ARCH-effect. The best models with respect to prediction excluded the ARFIMA–FIGARCH model and were dominated by the ARFIMA–FIAPARCH model under Non-normal error distributions indicating the importance of asymmetry and heavy tailed error distributions.
  South Africa is a country rich in the platinum group metals (PGMs) particularly platinum and palladium and it is the largest producer of platinum and second largest producer of palladium (Matthey 2014) accounting for 96% of known PGMs global reserves. In addition to accounting for a significant proportion of global mineral production and resources, the contribution of the PGMs to South Africa economically and otherwise cannot be over-emphasized. For instance, on average from 2008 to 2013, the percentage contribution to the South African GDP from this sector was 2.3% with a yearly increase of 3.3% and a head count of 191 781 in direct employment. Further, PGMs also play significant roles in the investment arena (Batten et al. 2010). Since platinum and palladium are two of the major precious metals that offer different volatility and returns of lower correlations with stocks at both sector and market levels, they are some of the attractive asset classes eligible for portfolio diversification (Arouri et al. 2012) which appear more likely to act as a financial instrument than gold. Recently, palladium has entered the Johannesburg Securities Exchange (JSE) as exchange traded funds (ETF). Two palladium funds, Standard Bank AfricaPalladium ETF and Absa Capital newPalladium ETF have been launched in March of 2014 on the JSE. These exchange traded funds are backed by the physical palladium metal. Also, the roles of the PMGs in the the medical field (e.g., their use in anticancer complexes) and industrial catalysis are ever-advancing. Given this background, investigating the mechanisms which generate these data returns and their related dynamics are of paramount importance to policy makers, regulators, traders and investors globally.
  It is well known that financial returns and hence volatility are dominated by the stylized facts. These include nonstationarity, volatility clustering, their returns are not normally distributed, i.e., the empirical distributions are more peaked and heavy tailed and sometimes asymmetrical and the autocorrelation functions (ACFs) of squared (absolute) returns and volatility exhibit persistence. Further, in precious metals returns and volatility, evidence of their respective ACFs exhibiting a hyperbolic decay, a phenomenon referred to as long memory (LM) (long range dependence) rather than an exponential one (short memory) exists in the literature. The LM phenomenon may be coupled with structural breaks which are shown to severely compromise LM tests as structural breaks induce spurious LM (Baneree and Urga 2005). Recent events that could result in structural breaks in the PGMs returns and volatility are the 2008/2009 global financial crisis and the occasional mining industry labour unrest since the 2012 Marikana incident which resulted in the death of 34 miner during a nation-wide labour unrest. Such events bring extremes and jumps in data that may alter the underlying data generating mechanisms.
  In the literature nonconstant variance (heteroskedasticity) is handled by autoregressive heteroskedastic (ARCH) models (Engle 1982) and generalized ARCH (GARCH) models (Bollerslev 1986) while LM in the mean is handled by autoregressive fractionally integrated moving average (ARFIMA) models (Tsay 2002). LM can be also inherent in the volatility and fractionally integrated GARCH (FIGARCH) models (Baillie et al. 1996) are proposed as appropriate models. ARFIMA and FIGARCH models generalize the ARIMA and integrated GARCH (IGARCH) to include non-integer (fractional) differencing. In recent times, LM memory has been observed both in the mean and volatility in precious metals, the so-called dual LM, see e.g., Arouri et al. (2012) and Diaz (2016). Using ARFIMA–FIGARCH type models in the article by the first authors did not address structural breaks and heavy tailed error distributions while that by the second author only addressed the dual LM and asymmetry phenomena. Further, their LM analysis was not detailed.
  In this study we attempt a more detailed and holistic approach, i.e., we address LM, structural breaks, asymmetry and heavy tailed distribution phenomenon in modelling platinum and palladium returns and volatility. We attempt to fill in the gaps by
  employing a wide spectrum of tests and methods which includes time domain, Fourier and wavelet domain techniques in exploring LM.
  In examining structural breaks we concentrate on validating whether the inherent LM is due to structural breaks, i.e., spurious or not. Most methods for testing the existence of structural breaks are based on out of sample forecasts and model comparison. On the other hand the two methods suggested by Shimotsu (2006) are advantageous in that they are unique in-sample tests for LM with good power and size. These tests are based on two notions, namely, the LM parameter estimate \(\hat{d}\) from sub-samples of the full data set should be consistent with that of the full data set and that applying the \(d\text {th}\) difference to an I(d) process should yield and I(0) process (based on KPSS test statistic). Although choosing a break fraction \(\tau\) arbitrarily may be suboptimal, estimating it from the the data under the null hypothesis of no break existence would render \(\hat{\tau }\) not to converge to a constant but to rather to a random variable which in turn adversely affect the asymptotic normality of the test statistic (Hassler and Olivares 2008). Different empirical multiple splitting scenarios are often arbitrarily carried out in practice before settling for one. Here in applying the former notion of the methods introduced by Shimotsu (2006) we split the full sample into sub-samples as in Arouri et al. (2012) who carried out a similar study.
  Results from this method will assist in understanding if LM in the platinum and palladium returns are spurious or not. Lastly, we will compare different ARFIMA–FIGARCH type models under various distributional scenarios to find the models for platinum and palladium return and volatility series that best fit these data.
  The outline of this paper is as follows. “Preliminary data exploration” section provides some preliminary data exploration aspects. “Long memory and structural breaks” section presents LM and structural breaks methods. “Volatility models” section discusses FIGARCH related volatility models. “Modelling of platinum and palladium returns series volatility” section gives empirical results of volatility models of the return series. “Conclusion” section gives the conclusion and further research work.
  The data used in this paper are daily closing platinum and palladium prices from February 1994 to June 2014, data is sourced from Matthey (2014). Both data series have 5237 data points. Log returns of price data used are defined as
  where \(X_t\) is the daily price at time t in days. As a point of departure we undertake a preliminary exploration of the return series of the two metals.
  Jarque–Bera and Kolmogorov–Smirnov tests in Table 2 illustrates that the series are not Normally distributed. To test for unit roots, we use the Phillips–Perron test since it is robust to the presence of serial correlation and heteroskedasticity. Phillips–Perron test at truncation lag 10 shows that the returns are stationary in mean. The ARCH-test confirm that heteroskedasticity is inherent in both series. Further, the ACF plots of log squared returns in Fig. 1 show hyperbolic decay (unsummable ACFs), a phenomenon referred to as LM.
  From these results, it is evident that these data are dominated by the stylized facts as well as LM. Since structural breaks usually induce spurious LM in financial time series, we discuss both LM and structural breaks in the next section.
  A stationary time series process \(X_t\) is a LM process if there exists a real number \(0<H<1\) such that the ACF, denoted by \(\rho (\tau )\), has a hyperbolic decay rate of the form \(\lim _{x\rightarrow \infty }\rho (\tau )=C^{2H-2},\) where \(C>0\) is a finite constant and H is the Hurst exponent (Hurst 1951). In LM literature, the parameter d, called the long range dependence (long memory) parameter is associated to the Hurst exponent with the relationship, \(d=H-1/2\). Although the ARFIMA model is stationary and invertible for d in the range \(-1/2<d<1/2\) evidence of precious metals exhibiting strong persistence (\(0<d<1/2\)) as opposed to intermediate persistence (antipersistence) (\(-1/2<d<0\)) is well documented in the literature, see e.g., Diaz (2016). The spectral density of a LM process will satisfy \(f(\omega )=C|\omega |^{-2d}\), \(0<d<1/2\). It is well known that this phenomenon can be spuriously induced by structural breaks. In this section we firstly dwell on LM and further elaborate on tests for structural breaks which confirm whether the inherent LM is authentic or spurious.
  In the literature, methods for estimating the long range dependence parameter are divided into three classes, namely heuristic, semi-parametric and maximum likelihood estimation (MLE) method. Heuristic (variance-type) methods are easy to compute and interpret but are both not accurate and robust. However, they are useful to test if LM exists and to obtain an initial estimate of d (or H). While on the other hand both semi-parametric and MLE methods give more accurate estimates, parametric methods require prior knowledge of the true model which infact is always unknown. For a comparative study of these classes of methods see Boutahar et al. (2007). In the following sub sections, we discuss these methods.