Global ETD Search

1	Measuring reputational risk in the South African banking sector Ferreira, Susara January 2015 (has links) With few previous data and literature based on the South African banking sector, the key aim of this study was to contribute further results concerning the effect of operational loss events on the reputation of South African banks. The main distinction between this study and previous empirical research is that a small sample of South African banks listed on the JSE, between 2000 and 2014 was used. Insurance companies fell outside the scope of the study. The study primarily focused on identifying reputational risk among Regal Treasury Bank, Saambou Bank, African Bank and Standard Bank. The events announced by these banks occurred between 2000 and 2014. The precise date of the announcement of the operational events was also determined. Stock price data were collected for those banks that had unanticipated operational loss announcements (i.e. the event). Microsoft Excel models applied to the reputational loss as the difference between the operational loss announcement and the loss in the stock returns of the selected banks. The results indicated significant negative abnormal returns on the announcement day for three of the four banks. For one of the banks it was assumed that the operational loss was not significant enough to cause reputational risk. The event methodology similar to previous literature, furthermore examined the behaviour of return volatility after specific operational loss events using the sample of banks. The study further aimed at making two contributions. Firstly, to analyse return volatility after operational loss announcements had been made among South African banks, and secondly, to compare the sample of affected banks with un-affected banks to further identify whether these events spilled over into the banking industry and the market. The volatility of these four banks were compared to three un-affected South African banks. The results found that the operational loss events for Regal Treasury Bank and Saambou Bank had no influence on the unaffected banks. However the operational loss events for African Bank and Standard Bank influenced the sample of unaffected banks and the Bank Index, indicating systemic risk. Operational risk Reputational risk Event study Banks Abnormal return South Africa Volatility Exponential weighted moving average
2	Measuring reputational risk in the South African banking sector Ferreira, Susara January 2015 (has links) With few previous data and literature based on the South African banking sector, the key aim of this study was to contribute further results concerning the effect of operational loss events on the reputation of South African banks. The main distinction between this study and previous empirical research is that a small sample of South African banks listed on the JSE, between 2000 and 2014 was used. Insurance companies fell outside the scope of the study. The study primarily focused on identifying reputational risk among Regal Treasury Bank, Saambou Bank, African Bank and Standard Bank. The events announced by these banks occurred between 2000 and 2014. The precise date of the announcement of the operational events was also determined. Stock price data were collected for those banks that had unanticipated operational loss announcements (i.e. the event). Microsoft Excel models applied to the reputational loss as the difference between the operational loss announcement and the loss in the stock returns of the selected banks. The results indicated significant negative abnormal returns on the announcement day for three of the four banks. For one of the banks it was assumed that the operational loss was not significant enough to cause reputational risk. The event methodology similar to previous literature, furthermore examined the behaviour of return volatility after specific operational loss events using the sample of banks. The study further aimed at making two contributions. Firstly, to analyse return volatility after operational loss announcements had been made among South African banks, and secondly, to compare the sample of affected banks with un-affected banks to further identify whether these events spilled over into the banking industry and the market. The volatility of these four banks were compared to three un-affected South African banks. The results found that the operational loss events for Regal Treasury Bank and Saambou Bank had no influence on the unaffected banks. However the operational loss events for African Bank and Standard Bank influenced the sample of unaffected banks and the Bank Index, indicating systemic risk. Operational risk Reputational risk Event study Banks Abnormal return South Africa Volatility Exponential weighted moving average
3	Kompiuterių tinklo srautų anomalijų aptikimo metodai / Detection of network traffic anomalies Krakauskas, Vytautas 03 June 2006 (has links) This paper describes various network monitoring technologies and anomaly detection methods. NetFlow were chosen for anomaly detection system being developed. Anomalies are detected using a deviation value. After evaluating quality of developed system, new enhancements were suggested and implemented. Flow data distribution was suggested, to achieve more precise NetFlow data representation, enabling a more precise network monitoring information usage for anomaly detection. Arithmetic average calculations were replaced with more flexible Exponential Weighted Moving Average algorithm. Deviation weight was introduced to reduce false alarms. Results from experiment with real life data showed that proposed changes increased precision of NetFlow based anomaly detection system. Informatics Engineering Anomaly detection Exponential weighted moving average Netflow Anomalijų aptikimas
4	Forecasting Ability of the Phillips Curve / Předpověď inflace Euro zóny pomocí Phillipsovy křivky Michálková, Simona January 2015 (has links) The aim of this paper is to investigate various versions of the Phillips curve and their inflation forecasting ability for Euro Area. We consider autoregressive distributed lag models and use two types of trend estimation -- successive (the trend is estimated before the remaining parameters are) and join, using exponential smoothing. The versions of the Phillips curve are evaluated by rolling and recursive window methods, various selection criteria for lag variables and different combination of the inflation indicators. To evaluate the forecasted values, we calculate the RMSE in three 7-year periods: 1993-1999 (run up Euro area), 2000-2006 (stable inflation period) and 2007-2013 (financial crisis). According to all our modifications, we find some models which achieve satisfying results in terms of the RMSE, albeit not for all forecasting periods. We notice that some models are satisfactory only in the stable period however not in the periods with low inflation and vice versa.
5	Natural gas storage level forecasting using temperature data Sundin, Daniel January 2020 (has links) Even though the theory of storage is historically a popular view to explain commodity futures prices, many authors focus on the oil price link. Past studies have shown an increased futures price volatility on Mondays and days when natural gas storage levels are released, which could both implicate that storage levels and temperature data are incorporated in the prices. In this thesis, the U.S. natural gas storage level change is studied as a function of the consumption and production. Consumption and production are furthered segmented and separately forecasted by modelling inverse problems that are solved by least squares regression using temperature data and timeseries analysis. The results indicate that each consumer consumption segment is highly dependent of the temperature with R2-values of above 90%. However, modelling each segment completely by time-series analysis proved to be more efficient due to lack of flexibility in the polynomials, lack of used weather stations and seasonal patterns in addition to the temperatures. Although the forecasting models could not beat analysts’ consensus estimates, these present natural gas storage level drivers and can thus be used to incorporate temperature forecasts when estimating futures prices. Natural gas storage forecasting Natural gas storage Natural gas Storage forecasting Forecasting Futures Futures forecasting Natural gas futures forecasting Least squares regression Regression Machine learning Exponential Weighted Moving Average Moving Average EWMA MA Seasonality Commodity futures Optimisation Inverse problems Consumption forecasting Production Forecasting Residential Commercial Industrial Electric power NOAA EIA Pipelines Polynomial Weather stations Mathematics Matematik

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