The basis of this thesis is the analysis of business risks, their variations over time and the impact that these risks can have on the development of the company. In particular, a program to estimate strategic risks has been implemented with the aim of predicting the trend of risks over time. The thesis is divided into five chapters: in the first chapter a brief historical introduction is given in which the birth of probability and of the simulation of Monte Carlo is described. In the second chapter there is description of Markov Chains, which we will use for the first programs of estimation and risk analysis. The third chapter focuses on stationary stochastic processes with several examples of self-regressive models. In the fourth chapter the analysis shifts its attention to risks. We try to describe the concept of risk and to analyze the method most used by companies to manage it, Enterprise Risk Management. Finally, in the fifth chapter, the results obtained from the analysis of data processing are commented, showing the most relevant elements of the analysis carried out. In fact, the programs and graphs are commented and the model used for the estimate is explained.
The basis of this thesis is the analysis of business risks, their variations over time and the impact that these risks can have on the development of the company. In particular, a program to estimate strategic risks has been implemented with the aim of predicting the trend of risks over time. The thesis is divided into five chapters: in the first chapter a brief historical introduction is given in which the birth of probability and of the simulation of Monte Carlo is described. In the second chapter there is description of Markov Chains, which we will use for the first programs of estimation and risk analysis. The third chapter focuses on stationary stochastic processes with several examples of self-regressive models. In the fourth chapter the analysis shifts its attention to risks. We try to describe the concept of risk and to analyze the method most used by companies to manage it, Enterprise Risk Management. Finally, in the fifth chapter, the results obtained from the analysis of data processing are commented, showing the most relevant elements of the analysis carried out. In fact, the programs and graphs are commented and the model used for the estimate is explained.
An Autoregressive Model to Estimate Risk using Monte Carlo Simulation
SEMINO, FEDERICA
2017/2018
Abstract
The basis of this thesis is the analysis of business risks, their variations over time and the impact that these risks can have on the development of the company. In particular, a program to estimate strategic risks has been implemented with the aim of predicting the trend of risks over time. The thesis is divided into five chapters: in the first chapter a brief historical introduction is given in which the birth of probability and of the simulation of Monte Carlo is described. In the second chapter there is description of Markov Chains, which we will use for the first programs of estimation and risk analysis. The third chapter focuses on stationary stochastic processes with several examples of self-regressive models. In the fourth chapter the analysis shifts its attention to risks. We try to describe the concept of risk and to analyze the method most used by companies to manage it, Enterprise Risk Management. Finally, in the fifth chapter, the results obtained from the analysis of data processing are commented, showing the most relevant elements of the analysis carried out. In fact, the programs and graphs are commented and the model used for the estimate is explained.È consentito all'utente scaricare e condividere i documenti disponibili a testo pieno in UNITESI UNIPV nel rispetto della licenza Creative Commons del tipo CC BY NC ND.
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https://hdl.handle.net/20.500.14239/24773