563 research outputs found
Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments
In this doctoral dissertation characteristics of very large industrial real investments (VLIRI) are investigated and a special group of VLIRI is defined as giga-investments. The investment decision-making regarding to giga-investments is discussed from the points of view of discounted cash-flow based methods and real option valuation. Based on the bacground of establishing giga-investments, state-of-the-art in capital budgeting (including real options) and by applying fuzzy numbers a novel method for the evaluation and profitability analysis of giga-investments is presented. Application of the method is illustrated and issues regarding investment decision-making of large industrial real investments are discussed.Real Options; Fuzzy Real Option Valuation; Giga-Investments; Very Large Industrial Real Investments; Dissertation
Balance Sheet Interlinkages and Macro-Financial Risk Analysis in the Euro Area
The financial crisis has highlighted the need for models that can identify counterparty risk exposures and shock transmission processes at the systemic level. We use the euro area financial accounts (flow of funds) data to construct a sector-level network of bilateral balance sheet exposures and show how local shocks can propagate throughout the network and affect the balance sheets in other, even seemingly remote, parts of the financial system. We then use the contingent claims approach to extend this accounting-based network of interlinked exposures to risk-based balance sheets which are sensitive to changes in leverage and asset volatility. We conclude that the bilateral cross-sector exposures in the euro area financial system constitute important channels through which local risk exposures and balance sheet dislocations can be transmitted, with the financial intermediaries playing a key role in the processes. High financial leverage and high asset volatility are found to increase a sector’s vulnerability to shocks and contagion. JEL Classification: C22, E01, E21, E44, F36, G01, G12, G14Balance sheet contagion, contingent claims analysis, financial accounts, macro-prudential analysis, network models, systemic risk
Operational risk management and new computational needs in banks
Basel II banking regulation introduces new needs for computational schemes. They involve both optimal stochastic control, and large scale simulations of decision processes of preventing low-frequency high loss-impact events. This paper will first state the problem and present its parameters. It then spells out the equations that represent a rational risk management behavior and link together the variables: Levy processes are used to model operational risk losses, where calibration by historical loss databases is possible ; where it is not the case, qualitative variables such as quality of business environment and internal controls can provide both costs-side and profits-side impacts. Among other control variables are business growth rate, and efficiency of risk mitigation. The economic value of a policy is maximized by resolving the resulting Hamilton-Jacobi-Bellman type equation. Computational complexity arises from embedded interactions between 3 levels: * Programming global optimal dynamic expenditures budget in Basel II context, * Arbitraging between the cost of risk-reduction policies (as measured by organizational qualitative scorecards and insurance buying) and the impact of incurred losses themselves. This implies modeling the efficiency of the process through which forward-looking measures of threats minimization, can actually reduce stochastic losses, * And optimal allocation according to profitability across subsidiaries and business lines. The paper next reviews the different types of approaches that can be envisaged in deriving a sound budgetary policy solution for operational risk management, based on this HJB equation. It is argued that while this complex, high dimensional problem can be resolved by taking some usual simplifications (Galerkin approach, imposing Merton form solutions, viscosity approach, ad hoc utility functions that provide closed form solutions, etc.) , the main interest of this model lies in exploring the scenarios in an adaptive learning framework ( MDP, partially observed MDP, Q-learning, neuro-dynamic programming, greedy algorithm, etc.). This makes more sense from a management point of view, and solutions are more easily communicated to, and accepted by, the operational level staff in banks through the explicit scenarios that can be derived. This kind of approach combines different computational techniques such as POMDP, stochastic control theory and learning algorithms under uncertainty and incomplete information. The paper concludes by presenting the benefits of such a consistent computational approach to managing budgets, as opposed to a policy of operational risk management made up from disconnected expenditures. Such consistency satisfies the qualifying criteria for banks to apply for the AMA (Advanced Measurement Approach) that will allow large economies of regulatory capital charge under Basel II Accord.REGULAR - Operational risk management, HJB equation, Levy processes, budget optimization, capital allocation
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
The use of real options approach in energy sector investments
Energy shortage, global warming, and climate change led to an increase in the use of alternative sources of energy, with renewable energy sources (RES) playing a fundamental role in this new energetic paradigm. However, the investment costs often constitute a major barrier to their spread use. Moreover, the overall benefits of renewable energy technologies are often not well understood and consequently they are often evaluated to be not as cost effective as traditional technologies. From the moment that the energy sector started a deregulation process, with a high level of competitiveness and associated increased market uncertainty, traditional project evaluation techniques alone became insufficient to properly deal with these additional risk and uncertainty factors. The diffusion of the renewable energy technologies is also affected by this feature. The way investors evaluate their investments call now for the use of more sophisticated evaluation techniques. Real options approach can deal with these issues and, as so, began to be considered and applied for the energy sector decision aid. This approach it is now extensively widespread in evaluating investment projects in the energy sector. A large set of applications in almost all fields of energy decision making, from electricity generation technologies appraisal to policy evaluation is available in the literature. However the use of this technique in the field of RES is still limited and worth to be analysed. This paper addresses this issue. A review of the current state of the art in the application of real options approach to investments in non-renewable energy sources and RES is presented, giving perspectives for further research in this field.This work was financed by: the QREN – Operational Programme for Competitiveness Factors, the European Union – European Regional Development Fund and National Funds- Portuguese Foundation for Science and Technology, under Project FCOMP-01-0124-FEDER-011377 and Project Pest-OE/EME/UI0252/201
Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments
In this doctoral dissertation characteristics of very large industrial real investments (VLIRI) are investigated and a special group of VLIRI is defined as giga-investments. The investment decision-making regarding to giga-investments is discussed from the points of view of discounted cash-flow based methods and real option valuation. Based on the bacground of establishing giga-investments, state-of-the-art in capital budgeting (including real options) and by applying fuzzy numbers a novel method for the evaluation and profitability analysis of giga-investments is presented. Application of the method is illustrated and issues regarding investment decision-making of large industrial real investments are discussed
Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments
In this doctoral dissertation characteristics of very large industrial real investments (VLIRI) are investigated and a special group of VLIRI is defined as giga-investments. The investment decision-making regarding to giga-investments is discussed from the points of view of discounted cash-flow based methods and real option valuation. Based on the bacground of establishing giga-investments, state-of-the-art in capital budgeting (including real options) and by applying fuzzy numbers a novel method for the evaluation and profitability analysis of giga-investments is presented. Application of the method is illustrated and issues regarding investment decision-making of large industrial real investments are discussed
Modeling financial environments using geometric fractional Brownian motion model with long memory stochastic volatility
Geometric Fractional Brownian Motion (GFBM) model is widely used in financial environments. This model consists of important parameters i.e. mean, volatility, and Hurst index, which are significant to many problems in finance particularly option pricing, value at risk, exchange rate, and mortgage insurance. Most current works investigated GFBM under the assumption of its volatility that is constant due to its simplicity. However, such assumption is normally rejected in most empirical studies.
Therefore, this research develops a new GFBM model that can better describe and reflect real life situations particularly in financial scenario. All parameters involved in the developed model are estimated by using innovation algorithm. A simulation study is then conducted to determine the performance of the new model. The results of simulation reveal that the proposed estimators are efficient based on the bias, variance, and mean square error. Subsequently, two theorems on existence and
uniqueness of the solution for the new model and its generalisation are constructed. The validation of the developed model was then carried out by comparing with other models in forecasting adjusted prices of Standard and Poor 500, Shanghai Stock Exchange Composite Index, and FTSE Kuala Lumpur Composite Index. Empirical
studies on four selected financial applications, i.e. option pricing, value at risk, exchange rate, and mortgage insurance, indicate that the new model performs better than the existing ones. Hence, the new model has strong potential to be employed as an underlying model for any financial applications that capable of reflecting the real situation more accurately
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