Relevant bibliographies by topics / Financial risk management - Forecasting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Financial risk management - Forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Financial risk management - Forecasting"

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Bohdalová, Mária, and Michal Greguš. "VaR BASED RISK MANAGEMENT." CBU International Conference Proceedings 1 (June 30, 2013): 25–33. http://dx.doi.org/10.12955/cbup.v1.11.

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In this paper we discuss the Value–at–Risk concept and we analyse the market risk by using EWMA approach. EWMA (exponentially weighted moving average) forecasting technique is a popular measure of various risks in financial risk management. We will compare standard EWMA, robust EWMA and skewed EWMA forecast of VaR. JP Morgan standard EWMA is derived from Gaussian distribution. Robust EWMA is based on Laplace distribution and skewed EWMA is a new approach derived from an asymmetric Laplace distribution. Asymmetric Laplace distribution takes into account both skewness and heavy tails in return d
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Christoffersen, Peter F., and Francis X. Diebold. "How Relevant is Volatility Forecasting for Financial Risk Management?" Review of Economics and Statistics 82, no. 1 (2000): 12–22. http://dx.doi.org/10.1162/003465300558597.

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Tsintsadze, Asie, Vladimer Glonti, Lela Oniani, and Tamar Ghoghoberidze. "Empirical Analysis of Financial and Non-Financial Risks of the Commercial Bank." European Journal of Sustainable Development 8, no. 2 (2019): 101. http://dx.doi.org/10.14207/ejsd.2019.v8n2p101.

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Background: Activities of commercial banks are connected with numerous risks, the source of which is the internal and external processes of the bank. Objectives: Risk management science has been studying the origins of the risks, determining their impact quality and avoiding expected loss models from the 1950s. Method/Approach: Credit risk regressive analysis is based on the selection of effective factors, determination of their influence and prediction of future according to the correlation coefficient. Results/Findings: In the article, it is discussed the regressive analysis of operational r
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Chen, Cathy W. S., Richard Gerlach, Edward M H. Lin, and W. C. W. Lee. "Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis." Journal of Forecasting 31, no. 8 (2011): 661–87. http://dx.doi.org/10.1002/for.1237.

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Valipour, Hashem, and Mostafa Sohouli Vahed. "Risk Management and Forecasting Macro-Variables Influences on Bank Risk." International Journal of Business and Management 12, no. 6 (2017): 137. http://dx.doi.org/10.5539/ijbm.v12n6p137.

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Nowadays banks, as the most important component ofmoney market, are playing a very important role in country’s economy. By developing money markets, banking and financial institutes’ activities it is extensively developed and with no doubts economic development is not possible without considering the role of banking and money markets. By virtue of special and sensitive role of banks in Iran economic system, any shock, disturbances and/or ineffectiveness in economic systems directly effect on banks’ and financial institutes’ performance as well as phenomenon such as high inflation and/or price
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Tung, H. K. K., and M. C. S. Wong. "Financial risk forecasting with nonlinear dynamics and support vector regression." Journal of the Operational Research Society 60, no. 5 (2009): 685–95. http://dx.doi.org/10.1057/palgrave.jors.2602594.

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Chen, Qian, and Richard H. Gerlach. "The two-sided Weibull distribution and forecasting financial tail risk." International Journal of Forecasting 29, no. 4 (2013): 527–40. http://dx.doi.org/10.1016/j.ijforecast.2013.01.007.

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I. Dimitras, Augustinos, Stelios Papadakis, and Alexandros Garefalakis. "Evaluation of empirical attributes for credit risk forecasting from numerical data." Investment Management and Financial Innovations 14, no. 1 (2017): 9–18. http://dx.doi.org/10.21511/imfi.14(1).2017.01.

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In this research, the authors proposed a new method to evaluate borrowers’ credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers’ credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric mo
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Yarygina, I. Z., V. B. Gisin, and B. A. Putko. "Fractal Asset Pricing Models for Financial Risk Management." Finance: Theory and Practice 23, no. 6 (2019): 117–30. http://dx.doi.org/10.26794/2587-5671-2019-23-6-117-130.

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The article presents the analysis findings of the problems and prospects of using the fractal markets theory to mathematically predict the price dynamics of assets as part of a financial risk management strategy. The aim of the article is to find out the features of value of bank assets and to develop recommendations for assessing financial risks based on mathematical methods for forecasting economic processes. Theoretical and empirical research methods were used to achieve the aim. The article reveals the features of mathematical modeling of economic processes related to asset pricing in a vo
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Amri, Adil El, Rachid Boutti, Salah Oulfarsi, Florence Rodhain, and Brahim Bouzahir. "Carbon financial markets underlying climate risk management, pricing and forecasting: Fundamental analysis." Financial Markets, Institutions and Risks 4, no. 4 (2020): 31–44. http://dx.doi.org/10.21272/fmir.4(4).31-44.2020.

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Climate Change (CC) is a major issue of our century. Controlling the constraints of Greenhouse Gas (GHG) emissions through transformation into opportunities, in an organization to increase industrial production, has become a necessity. The main reason for this adoption was the effectiveness of energy management and responsible linkages that are being developed to determine the issues and opportunities of carbon finance for organizations. Through analysis of the European Union Emissions Trading Scheme (EU ETS) and the Clean Development Mechanism (CDM), this article presents and demonstrates a v
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Dissertations / Theses on the topic "Financial risk management - Forecasting"

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Bedendo, Mascia. "Density forecasting in financial risk modelling." Thesis, University of Warwick, 2003. http://wrap.warwick.ac.uk/2661/.

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As a result of an increasingly stringent regulation aimed at monitoring financial risk exposures, nowadays the risk measurement systems play a crucial role in all banks. In this thesis we tackle a variety of problems, related to density forecasting, which are fundamental to market risk managers. The computation of risk measures (e.g. Value-at-Risk) for any portfolio of financial assets requires the generation of density forecasts for the driving risk factors. Appropriate testing procedures must then be identified for an accurate appraisal of these forecasts. We start our research by assessing
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Lu, Shan. "Essays on volatility forecasting and density estimation." Thesis, University of Aberdeen, 2019. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=240161.

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This thesis studies two subareas within the forecasting literature: volatility forecasting and risk-neutral density estimation and asks the question of how accurate volatility forecasts and risk-neutral density estimates can be made based on the given information. Two sources of information are employed to make those forecasts: historical information contained in time series of asset prices, and forward-looking information embedded in prices of traded options. Chapter 2 tests the comparative performance of two volatility scaling laws - the square-root-of-time (√T) and an empirical law, TH, cha
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Manganelli, Simone. "Conditional autoregressive value at risk and other essays in financial econometrics /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2000. http://wwwlib.umi.com/cr/ucsd/fullcit?p9980049.

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Neves, João Miguel Louçada. "Risk Models and Management - Computing VaR for Options' Portfolio." Master's thesis, Instituto Superior de Economia e Gestão, 2010. http://hdl.handle.net/10400.5/3101.

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Mestrado em Finanças<br>Increasingly, extreme events are less rarely in financial markets behaviour. To face that changes and prevent bigger catastrophes, Value-at-risk (VaR) has emerged as one of the most powerful and disseminated tools. Summarizing into a single number, the maximum loss of a portfolio over a given time horizon at a given level of confidence can be an extremely important way to control and measure risk. In this paper, several techniques of valuation were focused to calculate the risk of a financial options portfolio. Although that approach, not all techniques were integrated
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Famy, George. "Forecasting Reurns to Pure Factors: A Study of Time Varying Risk Premia." restricted, 2006. http://etd.gsu.edu/theses/available/etd-04282006-162928/.

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Thesis (Ph.D.)--Georgia State University, 2006.<br>Stephen D. Smith, committee chair; Jason Greene, James Owens, Alok Srivastava, committee members. Electronic text (132 p. : ill. (some co.)) : digital, PDF file. Description based on contents viewed July 12, 2007. Includes bibliographical references (p. 91-97).
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Apostolidou, Ilektra-Georgia, and Georgios Karmiris. "Risk-adjusted Earned Value and Earned Duration Management models for project performance forecasting." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18965.

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Project control is essential to ensure that the investment on a project is providing the intended benefits and is valuable to the customers. Previous methods offer project performance monitoring and forecasting tools, but they lack accuracy and the associated techniques omit the project financial risk (any unplanned event that has an impact on schedule and budget); the main factor of project failure. Poor project execution, and particularly failure to control and accurately forecast the project performance, may lead to increased costs, upset customers and eventually loss of market share. These
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Vander, Elst Harry-Paul. "Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/228960.

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This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexi
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Thupayagale, Pako. "Essays in long memory : evidence from African stock markets." Thesis, St Andrews, 2010. http://hdl.handle.net/10023/883.

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Brabcová, Lucie. "Řízení likvidity a solventnosti (na příkladu konkrétního podniku)." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-206359.

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The thesis deals with the individual aspects of liquidity and solvency management in the context of financial risk management and working capital components. The main accent is put on the foreign exchange risk management and the cash management tools on the group level: netting and cash pooling. These tools are supported by the cash forecasting system and the actual cash flows evaluation. The methods of liquidity and solvency management are demonstrated on the example of a Shared Service Center organisation.
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Laurent, Marie-Paule. "Essays in financial risk management." Doctoral thesis, Universite Libre de Bruxelles, 2003. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211221.

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Books on the topic "Financial risk management - Forecasting"

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Financial risk forecasting: The theory and practice of forecasting market risk, with implementation in R and Matlab. John Wiley, 2011.

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Christoffersen, Peter F. How relevant is volatility forecasting for financial risk management? National Bureau of Economic Research, 1998.

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Balancing fiscal policy risks. International Monetary Fund, 2012.

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Seshadri, D. V. R., 1957-, ed. Global risk/global opportunity: Ten essential tools for tracking minds, markets & money. Response Books, 2010.

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Engle, R. F. CAViaR: Conditional value at risk by quantile regression. National Bureau of Economic Research, 1999.

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Daníelsson, Jón, ed. Financial Risk Forecasting. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119205869.

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B, Falkena H. Financial risk management. Southern Book Publishers, 1991.

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S, Hughes, ed. Financial risk management. Gower, 1988.

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Financial risk management. McGraw-Hill Book Co., 1995.

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Allen, Steven, ed. Financial Risk Management. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119203209.

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Book chapters on the topic "Financial risk management - Forecasting"

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Alexander, Carol. "Estimating and Forecasting Volatility and Correlation Using ARCH and GARCH Models." In Risk Management and Financial Derivatives. Palgrave Macmillan UK, 1997. http://dx.doi.org/10.1007/978-1-349-14605-5_9.

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Maciel, Leandro. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting." In Advances in Financial Risk Management. Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137025098_11.

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Wang, Hong, Yonggan Zhao, and Shuichang Xie. "Firms’ Financial Risk Forecasting Based on the Macroeconomic Fluctuations." In Proceedings of the Eighth International Conference on Management Science and Engineering Management. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55182-6_43.

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Cornwall, Jeffrey R., David O. Vang, and Jean M. Hartman. "Revenue Forecasting." In Entrepreneurial Financial Management. Routledge, 2019. http://dx.doi.org/10.4324/9780429320484-4.

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Cornwall, Jeffrey R., David O. Vang, and Jean M. Hartman. "Expense Forecasting." In Entrepreneurial Financial Management. Routledge, 2019. http://dx.doi.org/10.4324/9780429320484-5.

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Errington, Charles. "Risk Management." In Financial Engineering. Palgrave Macmillan UK, 1994. http://dx.doi.org/10.1007/978-1-349-13268-3_3.

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Jain, P. K., Shveta Singh, and Surendra Singh Yadav. "Risk Management." In Financial Management Practices. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0990-4_7.

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Zhu, Ning. "Risk Managment! Risk Management!" In Financial Decision Making. Routledge, 2017. http://dx.doi.org/10.4324/9781315619859-12.

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García, Francisco Javier Población. "Derivative Credit Risk (Counterparty Risk)." In Financial Risk Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41366-2_12.

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García, Francisco Javier Población. "One-Dimensional Market Risk; Equity Risk." In Financial Risk Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41366-2_3.

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Conference papers on the topic "Financial risk management - Forecasting"

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Bouchti, Abdelali El, Younes Tribis, Tarik Nahhal, and Chafik Okar. "Forecasting Financial Risk using Quantum Neural Networks." In 2018 Thirteenth International Conference on Digital Information Management (ICDIM). IEEE, 2018. http://dx.doi.org/10.1109/icdim.2018.8847063.

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Yang, Wei, Ai Han, and Shouyang Wang. "Forecasting Financial Volatility with Interval-Valued Time Series Data." In Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA). American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413609.123.

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Gavėnaitė-Sirvydienė, Julija, and Algita Miečinskienė. "FORECASTING COSTS OF CYBER ATTACKS USING ESTIMATION THE GLOBAL COST OF CYBER RISK CALCULATOR V 1.2." In International Scientific Conference „Contemporary Issues in Business, Management and Economics Engineering". Vilnius Gediminas Technical University, 2021. http://dx.doi.org/10.3846/cibmee.2021.618.

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Purpose – due to the constant increase of cyber-attacks not only the measures of identifying and controlling cyber risks are created, but also the methods of estimating possible cyber-attacks financial costs should be developed to increase business preparedness. The purpose of this research is to forecast potential costs of cyber-attacks in Baltic countries. Research methodology – to achieve the aim of the article and prepare a prognosis of possible cyber-attacks costs the Estimation the Global Costs of Cyber Risk Calculator V 1.2 tool was used. Findings – estimated costs of cyber-attacks in L
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Pashchenko, Svetlana, Nikolay Pashchenko, and Olga Krioni. "Financial risk management." In International Conference on Trends of Technologies and Innovations in Economic and Social Studies 2017. Atlantis Press, 2017. http://dx.doi.org/10.2991/ttiess-17.2017.84.

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Kuznietsova, Nataliia, and Petro Bidyuk. "Forecasting of Financial Risk Users' Outflow." In 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 2018. http://dx.doi.org/10.1109/saic.2018.8516782.

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Sawhney, Ramit, Puneet Mathur, Ayush Mangal, Piyush Khanna, Rajiv Ratn Shah, and Roger Zimmermann. "Multimodal Multi-Task Financial Risk Forecasting." In MM '20: The 28th ACM International Conference on Multimedia. ACM, 2020. http://dx.doi.org/10.1145/3394171.3413752.

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Romanova, A. A., L. A. Terekhova, and P. A. Romanov. "Financial Innovations’ Risk Management." In International Scientific Conference "Far East Con" (ISCFEC 2020). Atlantis Press, 2020. http://dx.doi.org/10.2991/aebmr.k.200312.070.

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"An Overview of Financial Risk Management HomeAn Overview of Financial Risk Management." In rd Joint International Conference on Accounting, Business, Economics and Politics. Tishk International University, 2021. http://dx.doi.org/10.23918/icabep2021p30.

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Mata, Carlos, Luigi Saputelli, Dorzhi Badmaev, et al. "Automated Reservoir Management Workflows to Identify Candidates and Rank Opportunities for Production Enhancement and Cost Optimization in a Giant Field in Offshore Abu Dhabi." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31295-ms.

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Abstract As the field matures and overall field production decline, an accelerated and advanced selection of existing wells for workovers and sidetracks would be critical to meet with the increasing demand for production. Traditionally, an intensive effort is required to identify the right candidates and to ensure the technical and economic success of well interventions, infill-drilling locations and sidetrack locations. An advanced workflow is developed to automate the repetitive and less added value tasks such as data gathering and validation. The data set included historical production perf
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Liu, Chunmei, and Qian Jiang. "Mixed Financial Forecasting Index System Construct and Financial Forecasting Study on the C4.5 Decision Tree." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5302147.

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Reports on the topic "Financial risk management - Forecasting"

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Christoffersen, Peter, and Francis Diebold. How Relevant is Volatility Forecasting for Financial Risk Management? National Bureau of Economic Research, 1998. http://dx.doi.org/10.3386/w6844.

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Andersen, Torben, Tim Bollerslev, Peter Christoffersen, and Francis Diebold. Financial Risk Measurement for Financial Risk Management. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w18084.

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Rampini, Adriano, S. Viswanathan, and Guillaume Vuillemey. Risk Management in Financial Institutions. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w25698.

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Draghi, Mario, Francesco Giavazzi, and Robert Merton. Transparency, Risk Management and International Financial Fragility. National Bureau of Economic Research, 2003. http://dx.doi.org/10.3386/w9806.

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Andersen, Torben, Tim Bollerslev, Peter Christoffersen, and Francis Diebold. Practical Volatility and Correlation Modeling for Financial Market Risk Management. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/w11069.

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Froot, Kenneth, and Jeremy Stein. Risk Management, Capital Budgeting and Capital Structure Policy for Financial Institutions: An Integrated Approach. National Bureau of Economic Research, 1996. http://dx.doi.org/10.3386/w5403.

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Bodnar, Gordon, and Gunther Gebhardt. Derivatives Usage in Risk Management by US and German Non-Financial Firms: A Comparative Survey. National Bureau of Economic Research, 1998. http://dx.doi.org/10.3386/w6705.

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Demaestri, Edgardo C., Cynthia Moskovits, and Jimena Chiara. Management of Fiscal and Financial Risks Generated by PPPs: Conceptual Issues and Country Experiences. Inter-American Development Bank, 2018. http://dx.doi.org/10.18235/0001470.

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This paper discusses the main issues concerning sovereign fiscal and financial risks from public–private partnerships (PPPs) with a focus on contingent liabilities (CLs). It is based on the presentations and discussions that took place during the XI Annual Meeting of the Group of Latin American and the Caribbean Debt Management Specialists (LAC Debt Group), held in Barbados in August 2015. The main issues discussed include PPP risks assessment, institutional framework for PPP risk management, and accounting and reporting of CLs generated by PPPs. Six country cases (Chile, Colombia, Costa Rica,
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Venäläinen, Ari, Sanna Luhtala, Mikko Laapas, et al. Sää- ja ilmastotiedot sekä uudet palvelut auttavat metsäbiotaloutta sopeutumaan ilmastonmuutokseen. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361317.

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Climate change will increase weather induced risks to forests, and thus effective adaptation measures are needed. In Säätyö project funded by the Ministry of Agriculture and Forestry, we have summarized the data that facilitate adaptation measures, developed weather and climate services that benefit forestry, and mapped what kind of new weather and climate services are needed in forestry. In addition, we have recorded key further development needs to promote adaptation. The Säätyö project developed a service product describing the harvesting conditions of trees based on the soil moisture asses
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Reyes, Julian, Jeb Williamson, and Emile Elias. Spatio-temporal analysis of Federal crop insurance cause of loss data: A roadmap for research and outreach effort. U.S. Department of Agriculture, 2018. http://dx.doi.org/10.32747/2018.7202608.ch.

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Federal crop insurance provides a financial safety net for farmers against insured perils such as drought, heat, and freeze. In 2016 over $100 billion dollars of crops were insured through the Federal crop insurance program administered by the U.S. Department of Agriculture Risk Management Agency. In this white paper, we analyze publicly-available Federal crop insurance data to understand how weather and climate-related perils, or causes of loss (COL), change over time and spatial areas. We find that over 75% of all weather/climate-related indemnities (i.e., crop losses) from 2001 to 2016 are
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