Search

Le misure di rischio e l’asset allocation sono questioni di fondamentale importanza per i mercati finanziari. Lo studio è suddiviso in due parti. Nella prima parte vengono dimostrate proprietà molto importanti per il Lambda Value at Risk, mentre nella seconda parte vengono utilizzati modelli Markov Switching per modellizzare i rendimenti di serie finanziarie e viene introdotta una regola di trade basata sui regimi. L’ultima crisi finanziaria ha evidenziato le debolezze del Value at Risk. Per questo l’interesse verso misure di rischio alternative è aumentato notevolmente negli ultimi anni. In questo studio viene dimostrato che il Lambda Value at Risk è robuto ed elicitabile in particolari famiglie di distribuzioni ed è anche consistente. Il comportamento dei mercati finanziari è generalmente sottoposto a radicali cambiamenti quando si verificano guerre, crisi politiche o economiche ed altri eventi. Tali cambiamenti non sono permanenti ma possono persistere per periodi più o meno lunghi. Questo si rispecchia in alcune caratteristiche proprie delle serie storiche finanziare come ad esempio l’eccesso di kurtosis, gli effetti di skewness e la volatilità non costante. I modelli Markov Switching riescono a catturare questi cambiamenti che si verificano in maniera non deterministica e possono persistere per alcuni periodi dopo il cambiamento. Più precisamente, utilizziamo misture Markov Switching di distributioni normali fissando il numero di stati a N=2 corrispondenti a Normale Volatilità e Alta Volatilità. Gli obiettivi dello studio sono fondamentalmente due. Prima di tutto i parametri del modello vengono stimati attraverso Gibbs Sampling. Successivamente viene presentata una regola di trade basata sui regimi e confrontata con una semplice strategia buy-and-hold. Il database consiste in rendimenti giornalieri da Gennaio 1997 a Giugno 2018. Vengono analizzate diverse asset class appartenenti a diverse aeree geografiche. Il modello Markov-Switching è stato stima, Risk measures and Asset allocation are a matter of primary concern for the financial market. My study is divided into two parts. In the first part important properties of the Lambda Value at Risk are showed. In the second part Markov Switching models are used to handle the stocks returns and regime-based trade rule is introduced. The last global financial crisis has highlighted the lacks of the Value at Risk. Thus, the interest on alternative risk measures has considerably increased in the last years. In this study we showed that Lambda Value at Risk is robust and elicitable within particular classes of distributions. In addition, it also satisfies the consistency property without any condition on the mechanism generating data. The behavior of financial markets may be changed radically when wars, economical or political crises and other events occur. This changes are generally not permanent but persist for longer or shorter periods of time. This is reflected in certain specific features of financial time series such as the leptokurtosis, the skewness and the heteroskedasticity. Markov Switching models can handle these behavioral changes that occur randomly and persist for several periods after the change. Specifically, we model the returns by a Markov Switching mixture of gaussian distributions and we fix the number of regimes to N=2 corresponding to Normal Volatility and High Volatility. The purpose of the study is twofold. First of all, the model is estimated using Markov Chain Monte Carlo methods. Specifically Gibbs Sampling algorithm is used. Secondly, regime-based trade rule is presented and compared with a buy-and-hold strategy. The data consists of daily returns from Jan 1997 to June 2018. We analyzed different Asset classes across different geographic areas. We estimate both univariate and multivariate Markov Switching models to take into account the correlations among asset classes. In the univariate case, most indices exhibit two states clearly separated a

Tree vegetation and size structure was sampled in a miombo woodland area in E Tanzania and related to environmental factors, particularly soil and disturbance history. A total of 86 tree species was found. Four plant communities were distinguished through multivariate classification. Community 1 was dominated by Brachystegia boehmii, Brachystegia bussei and Julbernardia globiflora, and community 2 by B. boehmii and Brachystegia spiciformis. Community 1 was found on grey, eroded soil and community 2 on red, residual soil, a fact that opens up possibilities to use soil signals of satellite data for vegetation mapping. Community 3 is heavily disturbed miombo woodland near villages and community 4 was found on more clayey soil where miombo woodland is not expected. At our 42 sampled sites, density ranged from 74 to 1041 individuals ha−1 and basal area from 3.9 to 16.7 m2 ha−1. Regeneration is generally good but large sized trees are less prominent in communities 3 and 4 due to harvesting. With reduced disturbance miombo species may rapidly resume dominance in community 3. A higher than expected representation by the size class 30–40 (−50) cm dbh in community 2 is probably related to disturbance history. Prevalence of certain species (Pseudolachnostylis maprouneifolia, Pterocarpus angolensis and Diplorhynchos condylocarpon) may be related to frequent fires. Selective logging will soon cause extinction of Dalbergia melanoxylon, whereas Pterocarpus angolensis still has good regeneration, possibly because individuals below logging size have a good seed set. A way to get an easy overview of size classes in all species in an area using PCA is discussed.