Ever since the financial crisis in 2008 non-maturity deposits (NMDs) have had a floored deposit rate at zero. Now due to external factors some speculate that the market rate will increase. Regulations say that NMDs core deposits, which are used for further investments, must remove their rate sensitive part. In this work, high interest scenarios has been made to investigate the core deposits using an extended Vasicek model calibrated on the forward rate. Deposit rate models have been made using different regression techniques, mainly using linear models and a threshold regression model. We found that using a moving average on 21 days on Stibor 1M as a predicting variable yielded the best models. The models slope was then used to calculate the deposit rate on the given scenarioto calculate when the accounts will become rate sensitive again. At the end of the scenario, the deposits was found to decrease with 20%, 76% and 49% for the transaction-, savings account and the combined core deposits respectively using the median scenario. In order to regulate the decrease of the core deposits onecan use different rate sensitives similarly to the threshold model.

Beetle-killed trees and woody residues degenerate and may lead to wildfires and uncontrolled CO2 emission. Woody biomass is known as a neutral CO2 solid fuel since it generates the same amount of CO2 that takes from atmosphere during its growing up. Cofiring woody biomass with coal in existing coal power plants is a reasonable solution to reduce the net amount of CO2 emission and decrease the risk of wildfires. However, there are some challenges ranging from providing and handling the woody biomass to the operation of cofiring woody biomass with coal. Co-milling of the fuels and ash deposition on the heat exchanger surfaces during cofiring are among the most critical challenges. A CFD model simulated the behavior of the pulverized particles and evaluate the impact of geometry and operational changes on mill performance. In addition, we measured the ash deposit rate derived from cofiring woody biomass with coal in a pilot combustor (1500 kW) and full-scale furnace. Moreover, we developed a model to predict ash deposit rate during combustion of coal and its blend with a variety of biomass. The post-processing analysis of CFD modelling of co-milling woody biomass with coal shows that the entrained large woody biomass particles exit the pulverizer along with the fine coal particles due to their lower density than that of coal particles. Some simple geometry and operational changes can optimize mill performance by reducing the number of large biomass particles in the product stream. Therefore, it makes the particle size distribution (PSD) of the product stream of co-milling more like that of coal. The collected data set of fly ash particles and ash deposit samples shows that the ash formation and deposit rates were not impacted significantly by cofiring woody biomass with coal. The concentration of alkali metals in the ash aerosol during cofiring was slightly higher than that of coal. Cofiring in pilot scale combustor made a tri-modal PSD of ash aerosol particles; however, the distribution was bimodal in the full-scale boiler. The ash deposit rates during cofiring in 1500 kW combustor were higher (30 to 70%) at locations closer to the burner at short operation times. Our developed model of ash deposit rate investigated two types of stickiness models of fly ash particles to the surface of heat exchanger: melt fraction stickiness model (MFSM) and kinetic energy stickiness model (KESM). The developed model suggested that the MFSM, which is based on the melt fraction of ash and our novel approach to condensation of alkali vapor species, was more accurate in predicting ash deposit rate of a variety of fuel combustion of a 100-kW combustor. The model calculated four mechanisms: inertial impaction, thermophoresis, condensation, and eddy impaction.

The objective of this report is to carry out a pre-study and develop a framework for how the liquidity and interest rate risk of a bank's demand deposits can be modeled. This is done by first calibrating a Vasicek short rate model and then deriving models for the bank's deposit volume and deposit rate using multiple regression. The volume model and the deposit rate model are used to determine the liquidity and interest rate risk, which is done separately. The liquidity risk is determined by a liquidity quantile which estimates the minimum deposit volume that is expected to remain in the bank over a given time period. The interest rate risk is quantified by an arbitrage-free valuation of the demand deposit which can be used to determine the sensitivity of the net present value of the demand deposit caused by a parallel shift in the market rates. Furthermore, an immunization and a replicating portfolio are constructed and the performances of these are tested when introducing the same parallel shifts in the market rates as in the valuation of the demand deposit. The conclusion of this thesis is that the framework for the liquidity risk management that is developed gave satisfactory results and could be used by the bank if the deposit volume is estimated on representative data and a more accurate model for the short rate is used. The interest rate risk framework did however not yield as reliable results and would be more challenging to implement as a more advanced model for the deposit rate is required. / Målet med denna rapport är att utveckla ett ramverk för att bestämma likviditets-och ränterisken som är relaterad till en banks inlåningsvolym. Detta görs genom att först ta fram en modell för korträntan via kalibrering av en Vasicek modell. Därefter utvecklas, genom multipelregression, modeller för att beskriva bankens inlåningsvolym och inlåningsränta. Dessa modeller används för att kvantifiera likviditets- och ränterisken för inlånings-volymen, vilka beräknas och presenteras separat. Likviditetsrisken bestäms genom att en likviditetskvantil tas fram, vilken estimerar den minimala inlånings-volymen som förväntas kvarstå hos banken över en given tidsperiod. Ränterisken kvantifieras med en arbitragefri värdering av inlåningen och resultatet används för att bestämma känsligheten för hur nuvärdet av inlåningsvolymen påverkas av ett parallellskifte. Utöver detta bestäms en immuniseringsportfölj samt en rep-likerande portfölj och resultatet av dessa utvärderas mot hur nuvärdet förändras givet att samma parallellskifte i ränteläget som tidigare introduceras. Slutsatsen av projektet är att det framtagna ramverket för att bestämma likviditetsrisken för inlåningen gav bra resultat och skulle kunna implementeras i dagsläget av banken, förutsatt att volymmodellen estimeras på representativ data samt att en bättre modell för korträntan används. Ramverket för att bestämma ränterisken gav dock inte lika tillförlitliga resultat och är mer utmanande att implementera då en mer avancerad modell för inlåningsräntan krävs.