Abstract Infants with an older sibling diagnosed with an autism spectrum disorder (ASD) have a twentyfold increase in risk of developing ASD. Deficits in gesture use are among the first signs of impairment in infants later diagnosed with ASD. Typically, infants develop gestures incidentally in the context of social interactions with their parents. However, infants at risk for ASD may not acquire gestures within these natural interactions. The first purpose of this research was to determine whether infants at high risk for ASD show patterns of communicative and play gestures that are delayed and/or different relative to low-risk infants. The second purpose was to compare mothers of infants at risk for ASD with mothers of infants at low risk for ASD in their use of gestures, gesture strategies, and prompts. Seventeen 15-month-old infant-mother dyads were recruited from a longitudinal study of the emergence of autism symptoms in infants with an older sibling with ASD (high risk for ASD, n = 8; low risk for ASD, n = 9). Infant gestures were examined in three contexts: during clinical assessment, during naturalistic play with their mothers, and by parent report. Maternal gestures and gesture-related behaviours were recorded during the play interaction. Infant and maternal gesture behaviours were later coded from video. High-risk infants showed different patterns of gesture use relative to low-risk infants. In clinic and home contexts, high-risk infants: (a) used gestures that were not directed to a communicative partner more often than low-risk infants, and (b) showed specific deficits in the use of deictic and joint attention gestures. In addition, high-risk infants: (a) demonstrated fewer symbolic play acts at home, and (b) had a smaller inventory of communicative and play gestures by parent report. Mothers of high-risk infants used more play gestures, but were otherwise no different in their gesture behaviours from mothers of low-risk infants. This research demonstrated that, at 15 months of age infants at risk for ASD showed delays and differences in gesture use despite receiving typical gestural input from their mothers. The patterns of these deficits may be important in early identification and could inform intervention practices.

I began with an interest in what makes a difference for girls who face challenging circumstances: What helps them to develop sturdy, resilient, and resistant selves? What role does narrative play in this process? I set in motion a process of storytelling and reflecting by inviting girls and women to share stories together—their own stories, fictional narratives, and myths. The participants had faced particular challenges in adolescence, including economic hardship; disrupted social or family circumstances; mental health; abuse; or trauma. The girls and women had differing racialized, class, cultural, social, religious, and ethnic backgrounds. Drawing on the work of biblical scholars who understand Jesus’ parables as poetic metaphor, I identified 11 aspects of parables that helped me to hear and interpret girls’ stories: participation, difficulty, metaphor, fractals, truth, emergence, performance, possibility, power, wisdom, and beauty. Listening with a parabolic ear, I came to experience girls’ storytelling selves as participatory, metaphorical, fractal, truthful, and emergent; I observed girls’ selves as artistic practices that are embodied performances of their wisdom, power, and beauty. And I discovered how such performances of the self create enlarged spaces of possibility for girls in the face of life’s difficulty. I discovered that storytelling selves are girls’ power—power realized as storytelling, participation, mutual relation, meaning-making, enlarging spaces of possibility, disidentification, and embodiment. I identified six elements that seemed to be important in nurturing girls’ parabolic imagination. These are community participation, experienced observation, complexity, care, interpretation, and artmaking. These elements provide a framework for considering how educators might support girls’ selves but they do not provide a methodology. Taken together, they are more like a parable—an opening onto a particular worldview that invites participation in the world of a girl. These six elements may be signs that point to places where parables of the self are already being told. They become questions that make sense only to those who already understand: Is this community? Is anyone listening? Is it complex? Is this a place of compassion and care? Is meaning being shaped and questioned and reimagined here? Is there art? Is there play? / Thesis (Ph.D, Education) -- Queen's University, 2009-12-18 17:19:42.63

Early school leaving continues to be an issue that garners much attention from administrators, educators, and academics. In this study I review the existing literature on risk factors relating to early school leaving while examining the role of social context on educational aspirations and expectations. Research findings (Alberta Learning, 2001; Ferguson, Tilleczek, Boydell, & Rummens, 2005; Satchwell, 2004) show that early school leaving is a long process of disengagement that arises from multiple factors associated with experiences both inside and outside of school. In this study I carry out a qualitative analysis of both school and non-school related risk factors deemed to be significant to the early leaving of four young Portuguese-Canadian men. Their stories attest to the complexity of the phenomenon as they affirm the impact of both school and non-school related factors on early school leaving such as irrelevant curriculum, learning community, socio-economic status, and social context. In recounting their stories, and analyzing them through Bourdieu and Passeron’s (1979) understanding of “cultural capital” and “habitus,” I provide insights in this study into how administrators, educators and policymakers, alike may make learning more meaningful and authentic in order to curb early school leaving. / Thesis (Master, Education) -- Queen's University, 2010-05-29 09:54:48.856

Pastaruoju metu, investavimui tampant vis populiaresniu, atsiranda poreikis skaičiuoti portfelių rizikuojamąją vertę (angl. Value at Risk, toliau tekste VaR). Pastaroji gali būti skaičiuojama portfeliams sudarytiems iš skirtingų finansinių instrumentų. Tačiau iškyla problemų, kai finansiniai instrumentai yra tarpusavyje susiję (priklausomi). Šiai situacijai išspręsti naudojame VaR, kuris skaičiuojamas jungčių (angl. Copula) pagalba. Darbo tikslas – nagrinėjamiems portfeliams parinkti jungtis, kurios geriausiai atspindėtų bendrą duomenų pasiskirstymą. Tada, turint jungtis, apskaičiuoti VaR. Gavome, kad vertinant 1 portfelį ateinančiu laiko momentu mūsų didžiausias tikėtinas nuostolis yra intervale tarp 4.34 ir 4.70 litų. 2 portfelio nuostolis yra intervale (2.88, 3.42), 3 portfelio – (3.29, 5.28 ). / Recently, investments acquire vogue and it’s necessary to compute the Value at Risk of portfolio. VaR can be computed for portfolio which is made from different finance instruments. But the problem arises when these instruments are interdependent. In order to solve this problem, we compute VaR using copulas. The aim of this work is to pick copulas for real data which is the best for the distribution of the data. At that point compute VaR using selected copulas. The results are: in future time the biggest loss for first portfolio is in the interval 4.43 ant 4.7 Litas, for second portfolio the biggest loss – (2.88, 3.42) ant for third portfolio – (3.29, 5.28).

The measurement of operational risk has surely been one of the biggest challenges for banks worldwide. Most banks worldwide have opted for a value-at-risk (VaR) approach, based on the success achieved with market risk, to measure and quantify operational risk. The problem banks have is that they do not always find it difficult to calculate this VaR figure, as there are numerous mathematical and statistical methods and models that can calculate VaR, but they struggle to understand and interpret the values that are produced by VaR models and methods. Senior management and normal staff do not always understand how these VaR values will impact their decision-making and they do not always know how to incorporate these values in their day-to-day management of the bank. This study therefore aims to explain and discuss the calculation of VaR for operational risk as well as the factors that influence this figure, and then also to discuss how this figure is managed and the impact that it has on the management of a bank. The main goal of this study is then to explain the management of VaR for operational risk in order to understand how it can be incorporated in the overall management of a bank. The methodology used includes a literature review, in-depth interviews and a case study on a South African Retail Bank to determine and evaluate some of the most renowned methods for calculating VaR for operational risk. The first objective of this study is to define operational risk and all its elements in order to distinguish it from all the other risks the banking industry faces and to better understand the management thereof. It is the view of this study that it will be impossible to manage and measure operational risk if it is not clearly defined, and it is therefore important to have a clear and understandable definition of operational risk. The second objective is to establish an operational risk management process that will ensure a structured approach to the management of operational risk, by focusing on the different phases of operational risk. The process discussed by this study is a combination of some of the most frequent used processes by international banks, and is intended to guide the reader in terms of the steps required for managing operational risk. The third objective of this study is to discuss and explain the qualitative factors that play a role in the management of operational risk, and to determine where these factors fit into the operational risk process and the role they play in calculating the VaR for operational risk. These qualitative factors include, amongst others, key risk indicators (KRIs), risk and control self-assessments and the tracking of operational losses. The fourth objective is to identify and evaluate the quantitative factors that play a role in the management of operational risk, to distinguish these factors from the qualitative factors, and also to determine where these factors fit into the operational risk management process and the role they play in calculating VaR for operational risk. Most of these quantitative factors are prescribed by the Base1 Committee by means of its New Capital Accord, whereby this new framework aims to measure operational risk in order to determine the amount of capital needed to safeguard a bank against operational risk. The fifth objective is to discuss and explain the calculation of VaR for operational risk by means of discussing all the elements of this calculation. This study mainly bases its discussion on the loss distribution approach (LDA), where the frequency and severity of operational loss events are convoluted by means of Monte Carlo simulations. This study uses real data obtained from a South African Retail Bank to illustrate this calculation on a practical level. The sixth and final objective of this study is to explain how VaR for operational risk is interpreted in order for management to deal with it and make proper management decisions based on it. The above-mentioned discussion is predominantly based on the two types of capital that are influenced by VaR for operational risk. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2007.

In this thesis we use the GARCH(1,1) and GJR-GARCH(1,1) models to estimate the conditional variance for five equities from the OMX Nasdaq Stockholm (OMXS) stock exchange. We predict 95% and 99% Value-at-Risk (VaR) using one-day ahead forecasts, under three different error distribution assumptions, the Normal, Student’s t and the General Error Distribution. A 500 observations rolling forecast-window is used on the dataset of daily returns from 2007 to 2014. The empirical size VaR is evaluated using the Kupiec’s test of unconditional coverage and Christoffersen’s test of independence in order to provide the most statistically fit model. The results are ultimately filtered to correspond with the Basel (II) Accord Penalty Zones to present the preferred models. The study finds that the GARCH(1,1) is the preferred model when predicting the 99% VaR under varying distribution assumptions.

The measurement of operational risk has surely been one of the biggest challenges for banks worldwide. Most banks worldwide have opted for a value-at-risk (VaR) approach, based on the success achieved with market risk, to measure and quantify operational risk. The problem banks have is that they do not always find it difficult to calculate this VaR figure, as there are numerous mathematical and statistical methods and models that can calculate VaR, but they struggle to understand and interpret the values that are produced by VaR models and methods. Senior management and normal staff do not always understand how these VaR values will impact their decision-making and they do not always know how to incorporate these values in their day-to-day management of the bank. This study therefore aims to explain and discuss the calculation of VaR for operational risk as well as the factors that influence this figure, and then also to discuss how this figure is managed and the impact that it has on the management of a bank. The main goal of this study is then to explain the management of VaR for operational risk in order to understand how it can be incorporated in the overall management of a bank. The methodology used includes a literature review, in-depth interviews and a case study on a South African Retail Bank to determine and evaluate some of the most renowned methods for calculating VaR for operational risk. The first objective of this study is to define operational risk and all its elements in order to distinguish it from all the other risks the banking industry faces and to better understand the management thereof. It is the view of this study that it will be impossible to manage and measure operational risk if it is not clearly defined, and it is therefore important to have a clear and understandable definition of operational risk. The second objective is to establish an operational risk management process that will ensure a structured approach to the management of operational risk, by focusing on the different phases of operational risk. The process discussed by this study is a combination of some of the most frequent used processes by international banks, and is intended to guide the reader in terms of the steps required for managing operational risk. The third objective of this study is to discuss and explain the qualitative factors that play a role in the management of operational risk, and to determine where these factors fit into the operational risk process and the role they play in calculating the VaR for operational risk. These qualitative factors include, amongst others, key risk indicators (KRIs), risk and control self-assessments and the tracking of operational losses. The fourth objective is to identify and evaluate the quantitative factors that play a role in the management of operational risk, to distinguish these factors from the qualitative factors, and also to determine where these factors fit into the operational risk management process and the role they play in calculating VaR for operational risk. Most of these quantitative factors are prescribed by the Base1 Committee by means of its New Capital Accord, whereby this new framework aims to measure operational risk in order to determine the amount of capital needed to safeguard a bank against operational risk. The fifth objective is to discuss and explain the calculation of VaR for operational risk by means of discussing all the elements of this calculation. This study mainly bases its discussion on the loss distribution approach (LDA), where the frequency and severity of operational loss events are convoluted by means of Monte Carlo simulations. This study uses real data obtained from a South African Retail Bank to illustrate this calculation on a practical level. The sixth and final objective of this study is to explain how VaR for operational risk is interpreted in order for management to deal with it and make proper management decisions based on it. The above-mentioned discussion is predominantly based on the two types of capital that are influenced by VaR for operational risk. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2007.

Our planet is currently in the midst of a mass extinction event. Plants and animals are dying off at a rate undocumented since the dinosaurs went extinct 65 million years ago. Unlike earlier extinction events, however, the current ecological crisis is primarily being driven by a single species – homo sapiens. Although a seemingly overwhelming issue, environmental non-governmental organizations (ENGOs) have dedicated themselves to ensuring strong species protection. In Canada, these interest groups launched and sustained a successful national campaign for federal endangered species legislation that culminated in the enactment of the Species at Risk Act (SARA). While ENGOs‟ campaign for protective legislation has been well-documented by scholars, there is a dearth of research regarding ENGOs‟ strategies following the passage of SARA. In order to address this knowledge gap, this thesis examines and compares the strategies employed by interest groups in both the pre- and post-passage stages of the Act. After conducting qualitative interviews with seven representatives of Canadian-based ENGOs, this study finds that following the passage of SARA interest groups, dissatisfied with the government‟s weak implementation of the Act, decided to overhaul their strategic approach and shifted the species at risk issue to three new institutional venues: the boardrooms of private corporations, the Commission for Environmental Cooperation (CEC), and the domestic judicial arena. The thesis concludes that, despite inherent challenges, shifting institutional venues can be a successful strategy for ENGOs faced with a government reluctant to implement the hard-won legislative commitments. This work may prove to be particularly pertinent for other non-governmental organizations facing similar obstacles. / Graduate

This essay investigates three different GARCH-models (GARCH, EGARCH and GJR-GARCH) along with two distributions (Normal and Student’s t), which are used to forecast the Value at Risk (VaR) for different return series. Seven major international equity indices are examined. The purpose of the essay is to answer which of the three models that is better at forecasting the VaR and which distribution is more appropriate.  The results show that the EGARCH(1,1)  is preferred for all indices included in the study.

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