A numerical procedure based upon a boundary integral method for gravity wave making problems is studied in the time domain. The free-surface boundary conditions are combined and expressed in a Lagrangian notation to follow the free-surface particle's motion in time. The corresponding material derivative term is approximated by a finite difference expression, and the velocity terms are extrapolated in time for the completion of the formulations. The fluid-body intersection position at the free surface is predicted by an interpolation function that requires information from both the free surface and the submerged surface conditions. Solutions corresponding to a linear free-surface condition and to a non-linear free-surface condition are obtained at small time increment values. Numerical modelling of surface wave problems is studied in two dimensions and in three dimensions. Comparisons are made to linear analytical solutions as well as to published experimental results. Good agreement between the numerical solutions and measured values is found. For the modelling of a three dimensional wave diffraction problem, results at high wave amplitude are restricted because of the use of quadrilateral elements. The near cylinder region of the free surface is not considered to be well represented because of the coarse element size. Wave forces calculated on the vertical cylinder are found to be affected by the modelled tank length. When the simulated wave length is comparable to the wave tank's dimension, numerical results are found to be less than the experimental measurements. However, when the wave length is shorter than the tank's length, solutions are obtained with very good precision. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

Thesis (MBA)--Stellenbosch University, 1985. / Aangesien historiese data geredelik beskikbaar was, is 'n kwantitatiewe vooruitskattingsmetode gebruik met die doel om gebeure in die verlede te bestuur. Sodoende kon die onderliggende struktuur van die data beter begryp word en daarom kon 'n model daargestel word om die nodige inligting te verskaf vir bestuursbesluitneming. Die klassieke vermenigvuldigende tydreeks is gebruik om die toekomstige verkope van Stodels Nurseries (Edms.) Bpk. te projekteer. Aangesien die maatskappy se verkope onderhewig is aan hewige seisoenskommelings, is kontantvloeibeplanning van kardinale belang vir die finansiele bestuur van die maatskappy.

This write-up is submitted in partial fulfilment of the Master of Management Degree in Finance and Investments Degree. / Growth in commodity exporting economies, such as South Africa, is highly dependent on the revenue generated from exports. It is thus evident that as commodity prices fluctuate, income and the balance of payments will be accordingly impacted. This is further exacerbated by strong dependence on the imports of certain commodities. Oil is one such commodity on whose imports South Africa is highly dependent. Although natural gas is also imported, it is in lower quantities and is as such expected to impact South Africa to a lower extent. Coal, on the other hand, is among the main commodity exports and was expected to have an impact on (and be impacted by) South African macroeconomic fundamentals. In this study, we use a VECM and MGARCH model to test the interaction between South African macroeconomic variables and these three commodities. Our VECM findings indicate that oil and exchange rates are inflationary. This implies that an increase in oil prices and/or exchange rates (indicating a depreciation of the Rand against the U.S. Dollar) results in an increase in inflation. Inflation, on the other hand, propagates higher coal prices and to a lesser extent, higher interest rates. We account the latter to South Africa’s inflation targeting regime and the former to demand and supply dynamics which occur at RBCT as production costs increase (short-term coal export contracts and spot market sales). Natural gas is found to have weak impacts on interest rates and exchange rates. Our MGARCH model shows that only the innovations in natural gas and oil prices spillover into interest rates and exchange rate. There is no direct spillover captured. However, there is strong direct spillover from oil to inflation. Lastly, interest rates are found to have a strong direct volatility spillover to both oil and natural gas. We attribute this to the exchange rate impact that interest rates have and is supported by the exchange rate impact on commodity price volatility. We conclude that an in-depth understanding of triggers is pertinent for monetary and fiscal policy decisions in South Africa. Although the South African economy is relatively diversified compared to other developing countries, commodity price fluctuations do have a significant impact on economic performance. / MT2017

Thesis (M.Com.(Finance)--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2016. / This study sets out to investigate whether the Taylor Rule provides better the forecasts of the future short-term interest rates than the yield curve in the South African market. For the Taylor Rule we use OLS and use the open-market forward-looking Taylor Rule to forecast interest rates. For the yield curve, simple linear interpolation is used to derive forecast. We find that in the short term, forecasted one-month ahead interest rates closely track the actuals interest rates for both models. At longer horizons, there are larger deviations of forecasts from the actual. The RMSE analyses support the Taylor Rule as a superior forecasting model in all forecasting horizons. / MT2017

Yick, Kwok Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 67-68). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The importance of forecasting --- p.1 / Chapter 1.2 --- Objective --- p.3 / Chapter 2 --- Vector Autoregressive Model --- p.5 / Chapter 2.1 --- The VAR(p) model --- p.5 / Chapter 2.2 --- Least squares estimation method --- p.7 / Chapter 2.3 --- VAR order selection method --- p.10 / Chapter 2.4 --- Constructing simultaneous prediction intervals procedures --- p.11 / Chapter 2.4.1 --- Bonferroni procedure --- p.12 / Chapter 2.4.2 --- The 'Exact' procedure --- p.13 / Chapter 2.4.3 --- Two variables case --- p.15 / Chapter 2.4.4 --- Three variables case --- p.18 / Chapter 3 --- A System of Linear Equations with Exogenous Variables --- p.23 / Chapter 3.1 --- Restriction of VAR model --- p.23 / Chapter 3.2 --- Least squares estimation method --- p.24 / Chapter 3.3 --- Hsiao's sequential method for estimating the lag lengths --- p.26 / Chapter 3.3.1 --- Two variables case --- p.27 / Chapter 3.3.2 --- Three variables case --- p.29 / Chapter 3.4 --- Using VAR model to construct simultaneous prediction intervals --- p.32 / Chapter 3.4.1 --- Bonferroni procedure --- p.34 / Chapter 3.4.2 --- The 'Exact' procedure --- p.35 / Chapter 3.4.3 --- Two variables case --- p.36 / Chapter 3.4.4 --- Three variables case --- p.38 / Chapter 4 --- Illustrative Examples --- p.42 / Chapter 5 --- A Simulation Study --- p.52 / Chapter 5.1 --- Design of the experiment --- p.52 / Chapter 5.2 --- Simulation results --- p.58 / Chapter 5.3 --- Concluding remarks --- p.60 / Chapter 5.4 --- Further research --- p.60 / References --- p.67

Pan, Qi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 70-73). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Main Contributions --- p.5 / Chapter 1.2 --- Structure of Thesis --- p.6 / Chapter 2 --- Related Works --- p.7 / Chapter 2.1 --- Literature Review --- p.7 / Chapter 2.1.1 --- Existing Works on Bursty Feature Idenfication --- p.9 / Chapter 2.2 --- Classification --- p.9 / Chapter 2.2.1 --- Support Vector Machine --- p.9 / Chapter 2.2.2 --- Decision Tree and C4.5 Algorithm --- p.10 / Chapter 2.3 --- PageRank and HITS Algorithm --- p.10 / Chapter 2.3.1 --- PageRank --- p.11 / Chapter 2.3.2 --- HITS --- p.11 / Chapter 2.4 --- Efficient Market Hypothesis --- p.12 / Chapter 3 --- Problem Statement --- p.14 / Chapter 3.1 --- Volatility --- p.14 / Chapter 3.2 --- Financial Model --- p.15 / Chapter 3.3 --- Problem Statement --- p.16 / Chapter 4 --- Volatility V.S. Trend Prediction --- p.18 / Chapter 5 --- Bursty Volatility Features --- p.22 / Chapter 5.1 --- ADFIDF Measure --- p.24 / Chapter 5.2 --- Bursty Volatility Features --- p.28 / Chapter 5.3 --- Bursty Volatility Features Selection --- p.29 / Chapter 6 --- Volatility Ranking --- p.32 / Chapter 6.1 --- Graph Construction --- p.32 / Chapter 6.2 --- Volatility Ranking By News --- p.35 / Chapter 7 --- Volatility Index for Stock Volatility --- p.37 / Chapter 8 --- Experiments --- p.41 / Chapter 8.1 --- Experiments for Volatility Index --- p.41 / Chapter 8.1.1 --- Effectiveness of Volatility Index --- p.42 / Chapter 8.1.2 --- Information from News --- p.42 / Chapter 8.1.3 --- Information from Market --- p.45 / Chapter 8.1.4 --- Correlation Value --- p.46 / Chapter 8.1.5 --- Bursty Feature selection --- p.47 / Chapter 8.2 --- Experiments for Ranking --- p.48 / Chapter 8.2.1 --- Ranking Quality Comparison --- p.49 / Chapter 8.2.2 --- Capturing Bursty Features --- p.51 / Chapter 8.2.3 --- The Effectiveness of Feature Rank --- p.52 / Chapter 8.2.4 --- The Effectiveness of Random Walk --- p.53 / Chapter 8.2.5 --- Combination of VbN and GARCH --- p.54 / Chapter 8.2.6 --- Ranking Result Sample --- p.56 / Chapter 9 --- Conclusion --- p.58 / Chapter A --- Most Important Features for Stocks --- p.60 / Chapter B --- Correlation Matrix of Stocks --- p.63 / Chapter C --- News Index Evaluation Result Table --- p.65 / Chapter D --- Stock Data in Experiments --- p.67 / Chapter E --- Constructed Graph --- p.68 / Bibliograph --- p.70

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

Thesis (M.S.)--University of Hawaii at Manoa, 2006. / Includes bibliographical references (leaves 44-49). / vii, 50 leaves, bound ill. (some col.) 29 cm

Data assimilation plays an important role in weather forecasting. The purpose of data assimilation is try to provide a more accurate atmospheric state for future forecast. Several existed methods currently used in this field fall into two categories: statistical data assimilation and variational data assimilation. This thesis focuses mainly on variational data assimilation. The original objective function of three dimensional data assimilation (3D-VAR) consists of two terms: the difference between the pervious forecast and analysis and the difference between the observations and analysis in observation space. Considering the inaccuracy of previous forecasting results, we replace the first term by the difference between the previous forecast gradients and analysis gradients. The associated data fitting term can be interpreted using the second-order finite difference matrix as the inverse of the background error covariance matrix in the 3D-VAR setting. In our approach, it is not necessary to estimate the background error covariance matrix and to deal with its inverse in the 3D-VAR algorithm. Indeed, the existence and uniqueness of the analysis solution of the proposed objective function are already established. Instead, the solution can be calculated using the conjugate gradient method iteratively. We present the experimental results based on WRF simulations. We show that the performance of this forecast gradient based DA model is better than that of 3D-VAR. Next, we propose another optimization method of variational data assimilation. Using the tensor completion in the cost function for the analysis, we replace the second term in the 3D-VAR cost function. This model is motivated by a small number of observations compared with the large portion of the grids. Applying the alternating direction method of multipliers to solve this optimization problem, we conduct numerical experiments on real data. The results show that this tensor completion based DA model is competitive in terms of prediction accuracy with 3D-VAR and the forecast gradient based DA model. Then, 3D-VAR and the two model proposed above lack temporal information, we construct a third model in four-dimensional space. To include temporal information, this model is based on the second proposed model, in which introduce the total variation to describe the change of atmospheric state. To this end, we use the alternating direction method of multipliers. One set of experimental results generates a positive performance. In fact, the prediction accuracy of our third model is better than that of 3D-VAR, the forecast gradient based DA model, and the tensor completion based DA model. Nevertheless, although the other sets of experimental results show that this model has a better performance than 3D-VAR and the forecast gradient based DA model, its prediction accuracy is slightly lower than the tensor completion based model.

An empirical method is developed for constructing likelihood functions required in a Bayesian probabilistic flash flood forecasting model using data on objective quantitative precipitation forecasts and their verification. Likelihoods based on categorical and probabilistic forecast information for several forecast periods, seasons, and locations are shown and compared. Data record length, forecast information type and magnitude, grid area, and discretized interval size are shown to affect probabilistic differentiation of amounts of potential rainfall. Use of these likelihoods in Bayes' Theorem to update prior probability distributions of potential rainfall, based on preliminary data, to posterior probability distributions, reflecting the latest forecast information, demonstrates that an abbreviated version of the flash flood forecasting methodology is currently practicable. For this application, likelihoods based on the categorical forecast are indicated. Apart from flash flood forecasting, it is shown that likelihoods can provide detailed insight into the value of information contained in particular forecast products.