Dependence structure in financial time series: Applications and evidence from wavelet analysis

Conventional time series theory and spectral analysis have independently achieved significant popularity in mainstream economics and finance research over long periods. However, the fact remains that each is somewhat lacking if the other is absent. To overcome this problem, a new methodology, wavelet analysis, has been developed to capture all the information localized in time and in frequency, which provides us with an ideal tool to study non-stationary time series. This paper aims to explore the application of a variety of wavelet-based methodologies in conjunction with conventional techniques, such as the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and long-memory parameter estimates, in analysing the short and long term dependence structure of financial returns and volatility. Specifically, by studying the long-memory property of these time series we hope to identify the source of their possible predictability. Above all else, we document the indispensable role of trading activities associated with low frequencies in determining the long-run dependence of volatility. It follows that GARCH models incorporating long-memory and asymmetric returns-volatility dynamics can provide reasonably accurate volatility forecasts. Additionally, the persistence parameter of returns, represented by the Hurst index, is observed to be correlated to trading profits obtained from typical technical rules designed to detect and capitalize on existing trending behaviour of stock prices. This implies that the Hurst index can be used as a good indicator of the long-memory characteristic of the market, which in turn drives such trending behaviour

Developing models for the data-based mechanistic approach to systems analysis:Increasing objectivity and reducing assumptions

Stochastic State-Space Time-Varying Random Walk models have been developed, allowing the existing Stochastic State Space models to operate directly on irregularly sampled time-series. These TVRW models have been successfully applied to two different classes of models benefiting each class in different ways. The first class of models - State Dependent Parameter (SDP) models and used to investigate the dominant dynamic modes of nonlinear dynamic systems and the non-linearities in these models affected by arbitrary State Variables. In SDP locally linearised models it is assumed that the parameters that describe system’s behaviour changes are dependent upon some aspect of the system (it’s ‘state’). Each parameter can be dependent on one or more states. To estimate the parameters that are changing at a rate related to that of it’s states, the estimation procedure is conducted in the state-space along the potentially multivariate trajectory of the states which drive the parameters. The introduction of the newly developed TVRW models significantly improves parameter estimation, particularly in data rich neighbourhoods of the state-space when the parameter is dependent on more than one state, and the ends of the data-series when the parameter is dependent on one state with few data points. The second class of models are known as Dynamic Harmonic Regression (DHR) models and are used to identify the dominant cycles and trends of time-series. DHR models the assumption is that a signal (such as a time-series) can be broken down into four (unobserved) components occupying different parts of the spectrum: trend, seasonal cycle, other cycles, and a high frequency irregular component. DHR is confined to uniformly sampled time-series. The introduction of the TVRW models allows DHR to operate on irregularly sampled time-series, with the added benefit of forecasting origin no longer being confined to starting at the end of the time-series but can now begin at any point in the future. Additionally, the forecasting sampling rate is no longer limited to the sampling rate of the time-series. Importantly, both classes of model were designed to follow the Data-Based Mechanistic (DBM) approach to modelling environmental systems, where the model structure and parameters are to be determined by the data (Data-Based) and then the subsequent models are to be validated based on their physical interpretation (Mechanistic). The aim is to remove the researcher’s preconceptions from model development in order to eliminate any bias, and then use the researcher’s knowledge to validate the models presented to them. Both classes of model lacked model structure identification procedures and so model structure was determined by the researcher, against the DBM approach. Two different model structure identification procedures, one for SDP and the other for DHR, were developed to bring both classes of models back within the DBM framework. These developments have been presented and tested here on both simulated data and real environmental data, demonstrating their importance, benefits and role in environmental modelling and exploratory data analysis

Regional forecasting on labour markets

"Regional Forecasting on labour markets is an approach to generate data about future developments that cover adequately the information needs of labour market actors. Approaches from different European regions are presented in this book. Furthermore, the central elements of these approaches are discussed with respect to their problems in data and/ or methodology. Some solutions are described within the different models presented." (author's abstract). Contents: Rob Wilson: Local and Sub-regional medium-term skills forecasting in the UK (13-37); Ben Kriechel, Frank Cörvers, Hans Heijke: Regional Labour Market Forecasts in the Netherlands (38-51); Claudia Knobel, Dirk Drass, Lioba Trabert: Forecasting System for the Development of Employment and Qualifications In the Metropolitan Region Frankfurt/ Rhine-Main (52-67); Katharina Hampel, Marcus Kunz, Norbert Schanne, Rüdiger Wapler, Antje Weyh: Regional Employment Forecasts with Spatial Interdependencies (68-88); Franz-Josef Bade: Forecasting Regional Employment by Time-Series Modeling (89-113); Biancamaria Zavanella, Mario Mezzanzanica, Matteo M. Pelagatti, Simona C. Mintoti, Mattia Martini: A Two-Step Approach for Regional Medium-Term Skill Needs Forecasting (114-133); Bernhard Hillau: Evolving Occupations, an Approach at the Heart of the Future for French Regions (134-147); Stefan Humpl, Maria Kargl: AMS-Skills Barometer - Austria's Target Group oriented Labour Market Information System (148-161); John McGrath: The Systems for Early Identification of Skills Needs in Ireland (162-171)

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

Space-time statistical analysis of malaria morbidity incidence cases in Ghana: A geostatistical modelling approach

Malaria is one of the most prevalent and devastating health problems worldwide. It is a highly endemic disease in Ghana, which poses a major challenge to both the public health and socio-economic development of the country. Major factors accounting for this situation include variability in environmental conditions and lack of prevention services coupled with host of other socio-economic factors. Ghana’s National Malaria Control Programme (NMCP) risk assessment measures have been largely based on household surveys which provided inadequate data for accurate prediction of new incidence cases coupled with frequent incomplete monthly case reports. These raise concerns about annual estimates on the disease burden and also pose serious threats to efficient public health planning including the country’s quest of reducing malaria morbidity and mortality cases by 75% by 2015. In this thesis, both geostatistical space-time models and time series seasonal autoregressive integrated moving average (SARIMA) predictive models have been studied and applied to the monthly malaria morbidity cases from both district and regional health facilities in Ghana. The study sought to explore the spatio-temporal distributions of the malaria morbidity incidence and to account for the potential influence of climate variability, with particular focus on producing monthly spatial maps, delimiting areas with high risk of morbidity. This was achieved by modelling the morbidity cases as incidence rates, being the number of new reported cases per 100,000 residents, which together with the climatic covariates were considered as realisations of random processes occurring in space and/or time. The SARIMA models indicated an upward trend of morbidity incidence in the regions with strong seasonal variation which can be explained primarily by the effects of rainfall, temperature and relative humidity in the month preceding incidence of the disease as well as the morbidity incidence in the previous months. The various spacetime ordinary kriging (STOK) models showed varied spatial and temporal distributions of the morbidity incidence rates, which have increased and expanded across the country over the years. The space-time semivariogram models characterising the spatio-temporal continuity of the incidence rates indicated that the occurrence of the malaria morbidity was spatially and temporally correlated within spatial and temporal ranges varying between 30 and 250 km and 6 and 100 months, respectively. The predicted incidence rates were found to be heterogeneous with highly elevated risk at locations near the borders with neighbouring countries in the north and west as well as the central parts towards the east. The spatial maps showed transition of high risk areas from the north-west to the north-east parts with climatic variables contributing to the variations in the number of morbidity cases across the country. The morbidity incidence estimates were found to be higher during the wet season when temperatures were relatively low whilst low incidence rates were observed in the warm weather period during the dry seasons. In conclusion, the study quantified the malaria morbidity burden in Ghana to produce evidence-based monthly morbidity maps, illustrating the risk patterns of the morbidity of the disease. Increased morbidity risk, delimiting the highest risk areas was also established. This statistical-based modelling approach is important as it allows shortterm prediction of the malaria morbidity incidence in specific regions and districts and also helps support efficient public health planning in the country

Changes in extreme hydroclimate events in Interior Alaskan boreal forest watersheds

Thesis (Ph.D.) University of Alaska Fairbanks, 2014The high latitude regions of the globe are responding to climate change at unprecedented magnitudes and rates. As the climate warms, extreme hydroclimate events are likely to change more than the mean events, and it is the extreme changes that present a risk to society, the economy and the environment of the north. The subarctic boreal forest is one of the largest ecosystems in the world and is greatly understudied with respect to hydroclimate extremes. Thus, defining a baseline for changing extremes is the first step towards planning and implementing adaptation measures to reduce risk and costs associated with the changing extremes. This thesis focuses on quantitative analysis of extreme events using historical data and future model projections of changing temperature, precipitation and streamflow in the Interior forested region of boreal Alaska. Historically, shifts in the climate have resulted in declining magnitudes of peak flow for snow dominated and glacial Interior Alaskan basins. However, changes are variable and dependent upon watershed topography, permafrost conditions, and glacial extents. Therefore, adjacent basins respond in considerably different ways to the same climate drivers. For example, peak streamflow events in the adjacent Salcha and Chena River basins had different responses to changes in climate. In the higher elevation Salcha basin, maximum streamflow increased as spring temperatures increased but in the lower elevation Chena, winter precipitation was a control on increases in maximum streamflow, while both were influenced by the Pacific Decadal Oscillation. Analysis of hydrologic change must take this variability into account to understand extreme hydroclimate responses and correctly account for process shifts. To examine future changes in peak streamflow, the implementation and parameterization of hydrologic models to simulate hydroclimate extremes is required. In the northern latitudes of the world, there is a sparse observational station network that may be used for evaluation and correction of hydrologic models. This presents a limitation to science in these regions of the globe and has led to a paucity of research results and consequently, a lack of understanding of the hydrology of northern landscapes. Input of observations from remote sensing and the implementation of models that contain parameterizations specific to northern regions (i.e. permafrost) is one aim of this thesis. Remote sensing of snow cover extent, an important indicator of climate change in the north, was positively validated at snow telemetry sites across Interior Alaska. Input of the snow cover extent observations into a hydrologic model used by the Alaska Pacific River Forecast Center for streamflow flood forecasting improved discharge estimates for poorly observed basins, whereas the discharge estimates in basins with good quality river discharge observations improved little. Estimates of snow water equivalent were improved compared to station results and the adaptation of the model parameters indicated that the model is more robust, particularly during the snowmelt period when model simulations are error prone. Use of two independent hydrologic models and multiple global climate models (GCMs) and emission scenarios to simulate changes in future hydroclimate extremes indicated that large regime shifts are projected for snowmelt dominated basins of Interior Alaska. The Chena River basin, nearby Fairbanks, Alaska, is projected to be rainfall dominated by the 2080s, with smaller snowmelt peaks. Return intervals for flooding will increase by one-and-one half to double the flow volume magnitude compared to the historical return interval. Frequency of extreme streamflow events will increase five times the mean increase. These changes in extreme streamflow events necessitate further research on the implications for infrastructure, ecology and economy to constrain risk associated with the projected regime shift in boreal forested watersheds of Interior Alaska