{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":41,"n_cols":5,"rows_kept":41,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[10,2,10,3],[11,2,11,3],[12,2,12,3],[13,2,13,3],[16,2,16,3],[19,2,19,3],[24,2,24,3],[25,2,25,3],[28,2,28,3],[31,2,31,3],[31,4,31,5],[32,2,32,3],[32,4,32,5],[33,2,33,3],[33,4,33,5],[34,2,34,3],[35,2,35,3],[36,2,36,3],[37,2,37,3],[38,2,38,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910139247603321"]
[null,null,"Autore",null,"Vassiliou P. C. G."]
[null,null,"Titolo",null,"Discrete-time Asset Pricing Models in Applied Stochastic Finance / / P. C. G. Vassiliou"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken : , : John Wiley, , 2013"]
[{"$nulls":4},"2010"]
[null,null,"ISBN",null,"9781118557860"]
[{"$nulls":4},"1-118-55786-7"]
[{"$nulls":4},"1-118-61866-1"]
[{"$nulls":4},"1-299-31536-4"]
[{"$nulls":4},"1-118-61877-7"]
[null,null,"Edizione",null,"[First edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (418 pages)"]
[null,null,"Collana",null,"ISTE"]
[null,null,"Classificazione",null,"MAT 600f"]
[{"$nulls":4},"MAT 606f"]
[{"$nulls":4},"WIR 160f"]
[null,null,"Disciplina",null,"332.0151"]
[{"$nulls":4},"332.63/22201"]
[{"$nulls":4},"332.6322201"]
[null,null,"Soggetti",null,"Securities - Mathematical models - Prices"]
[{"$nulls":4},"Capital assets pricing model - Mathematical models"]
[{"$nulls":4},"Stochastic analysis"]
[{"$nulls":4},"Finance"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,"Nota di contenuto",null,"CHAPTER 1. Probability and Random Variables  -- CHAPTER 2. An Introduction to Financial Instruments and Derivatives -- CHAPTER 3. Conditional Expectation and Markov Chains -- CHAPTER 4. The No-Arbitrage Binomial Pricing Model -- CHAPTER 5. Martingales -- CHAPTER 6. Equivalent Martingale Measures, No-Arbitrage and Complete Markets -- CHAPTER 7. American Derivative Securities -- CHAPTER 8. Fixed-Income Markets and Interest Rates -- CHAPTER 9. Credit Risk -- CHAPTER 10. The Heath-Jarrow-Morton Model."]
[null,null,null,"Sommario/riassunto","Stochastic finance and financial engineering have been rapidly expanding fields of science over the past four decades, mainly due to the success of sophisticated quantitative methodologies in helping "]
[{"$nulls":4},"professionals manage financial risks. In recent years, we have witnessed a tremendous acceleration in research efforts aimed at better comprehending, modeling and hedging this kind of risk. These two volumes aim to provide a foundation course on applied stochastic finance. They are designed for three groups of readers: firstly, students of various backgrounds seeking a core knowledge on the subject of stochastic finance; secondly financial analysts and practitioners in the investment, banking and insurance industries; and finally other professionals who are interested in learning advanced mathematical and stochastic methods, which are basic knowledge in many areas, through finance. Volume 1 starts with the introduction of the basic financial instruments and the fundamental principles of financial modeling and arbitrage valuation of derivatives. Next, we use the discrete-time binomial model to introduce all relevant concepts. The mathematical simplicity of the binomial model also provides us with the opportunity to introduce and discuss in depth concepts such as conditional expectations and martingales in discrete time. However, we do not expand beyond the needs of the stochastic finance framework. Numerous examples, each highlighted and isolated from the text for easy reference and identification, are included. The book concludes with the use of the binomial model to introduce interest rate models and the use of the Markov chain model to introduce credit risk. This volume is designed in such a way that, among other uses, makes it useful as an undergraduate course."]
[null,"2.","Record Nr.",null,"UNINA9910557351203321"]
[null,null,"Autore",null,"Fang Fangxin"]
[null,null,"Titolo",null,"Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering"]
[null,null,"Pubbl/distr/stampa",null,"Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021"]
[null,null,"Descrizione fisica",null,"1 online resource (110 p.)"]
[null,null,"Soggetti",null,"Research & information: general"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-makers"]