Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
5,631
result(s) for
"Risk management -- Statistical methods"
Sort by:
Mathematics and statistics for financial risk management
\"This is an excellent book to grasp the basics of financial risk management. Everything in the book is explained from scratch and the concepts are very well exemplified with real life situations. Accompanied with a website filled with excel sheets for application, the book is great for future course material. This Second Edition of Mathematics and Statistics for Financial Risk Management includes 2 new chapters. The first chapter is on Bayesian Analysis and covers Bayes' Theorem, Many State Problems, Continuous Distributions, Bayesian Networks, and Bayesian Networks versus Correlation Matrices. The second new chapter is on Hypothesis Testing & Confidence Intervals and is on The Sample Mean Revisited, Sample Variance Revisited, Confidence Intervals, Hypothesis Testing, Chebyshev's Inequality, and Application: VaR. All chapters will have problems for testing and answers online\"-- Provided by publisher.
Book
Modeling, measuring and managing risk
This book is the first in the market to treat single- and multi-period risk measures (risk functionals) in a thorough, comprehensive manner. It combines the treatment of properties of the risk measures with the related aspects of decision making under risk.
eBook
Mathematics and statistics for financial risk management
Mathematics and Statistics for Financial Risk Management is a practical guide to modern financial risk management for both practitioners and academics.Now in its second edition with more topics, more sample problems and more real world examples, this popular guide to financial risk management introduces readers to practical quantitative.
eBook
Mathematics and statistics for financial risk management
This is an excellent book to grasp the basics of financial risk management. Everything in the book is explained from scratch and the concepts are very well exemplified with real life situations. Accompanied with a website filled with excel sheets for application, the book is great for future course material. This Second Edition of Mathematics and Statistics for Financial Risk Management includes 2 new chapters. The first chapter is on Bayesian Analysis and covers Bayes' Theorem, Many State Problems, Continuous Distributions, Bayesian Networks, and Bayesian Networks versus Correlation Matrices. The second new chapter is on Hypothesis Testing & Confidence Intervals and is on The Sample Mean Revisited, Sample Variance Revisited, Confidence Intervals, Hypothesis Testing, Chebyshev's Inequality, and Application: VaR. All chapters will have problems for testing and answers online.
Book
The book of alternative data : a guide for investors, traders, and risk managers
The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals.
eBook
GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan
Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate flood-prone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.
Journal Article
Geogenic and anthropogenic sources identification and ecological risk assessment of heavy metals in the urban soil of Yazd, central Iran
Urban soil pollution with heavy metals is one of the environmental problems in recent years, especially in industrial cities. The aim of this study is to evaluate the role of geogenic and anthropogenic sources in the urban soil pollution in Yazd, Iran. For this purpose, 30 top-soil (0–10 cm) samples from Yazd within an area of 136.37 Km 2 and population of nearly 656 thousand are collected, and the concentration of heavy elements is measured. To evaluate factors affecting the concentration of heavy elements in urban soils and determine their possible sources, Multivariate statistical analysis, including correlation coefficient, principal components analysis (PCA) and cluster analysis (CA) are performed. Enrichment Factor (EF), Geo-accumulation index (I geo ), and Modified potential ecological Risk Index (MRI) are used to assess the level and extension of contamination. Results of this study suggest that As, Cd, Pb and Zn are affected by anthropogenic source, while the concentrations of Fe, Mn, Ni, Cr, Co, Cu and Cs have come from mostly natural geologic sources. As, Cd and Pb are considerably enriched in the area, provided moderately enriched for the elements Mn, Zn and Cu. However, the other heavy elements show minimal enrichment. I geo reveal that Co, Cr, Cs, Cu, Fe, Mn, Zn and Ni with negative values are unpolluted, Pb posed unpolluted to moderately polluted, and As and Cd represent high polluted. Based on the results of the ecological risk factor, the heavy metals of Mn, Ni, Cr, Zn and Cu have a low ecological risk level. More specifically, we find that Pb shows a moderated ecological risk in 39% of the urban soil in the studied area. As and Cd with respectively 100 and 72% contribution have considerable and very high ecological risk. According to the results of MRI, the area is in a very high ecological risk level, and appropriate management practice is essential to reduce the pollution of heavy elements in this area.
Journal Article
Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures
Managers and researchers alike have long recognized the importance of corporate textual risk disclosures. Yet it is a nontrivial task to discover and quantify variables of interest from unstructured text. In this paper, we develop a variation of the latent Dirichlet allocation topic model and its learning algorithm for simultaneously discovering and quantifying risk types from textual risk disclosures. We conduct comprehensive evaluations in terms of both conventional statistical fit and substantive fit with respect to the quality of discovered information. Experimental results show that our proposed method outperforms all competing methods, and could find more meaningful topics (risk types). By taking advantage of our proposed method for measuring risk types from textual data, we study how risk disclosures in 10-K forms affect the risk perceptions of investors. Different from prior studies, our results provide support for all three competing arguments regarding whether and how risk disclosures affect the risk perceptions of investors, depending on the specific risk types disclosed. We find that around two-thirds of risk types lack informativeness and have no significant influence. Moreover, we find that the informative risk types do not necessarily increase the risk perceptions of investors-the disclosure of three types of systematic and liquidity risks will increase the risk perceptions of investors, whereas the other five types of unsystematic risks will decrease them.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mnsc.2014.1930
.
This paper was accepted by Alok Gupta, special issue on business analytics
.
Journal Article
Environmental Risk Assessment of Trace Metal Pollution: A Statistical Perspective
Trace metal pollution is primarily driven by industrial, agricultural, and mining activities and presents complex environmental challenges with significant implications for ecological and human health. Traditional methods of environmental risk assessment (ERA) often fall short in addressing the intricate dynamics of trace metals, necessitating the adoption of advanced statistical techniques. This review focuses on integrating contemporary statistical methods, such as Bayesian modeling, machine learning, and geostatistics, into ERA frameworks to improve risk assessment precision, reliability, and interpretability. Using these innovative approaches, either alone or preferably in combination, provides a better understanding of the mechanisms of trace metal transport, bioavailability, and their ecological impacts can be achieved while also predicting future contamination patterns. The use of spatial and temporal analysis, coupled with uncertainty quantification, enhances the assessment of contamination hotspots and their associated risks. Integrating statistical models with ecotoxicology further strengthens the ability to evaluate ecological and human health risks, providing a broad framework for managing trace metal pollution. As new contaminants emerge and existing pollutants evolve in their behavior, the need for adaptable, data-driven ERA methodologies becomes ever more pressing. The advancement of statistical tools and interdisciplinary collaboration will be essential for developing more effective environmental management strategies and informing policy decisions. Ultimately, the future of ERA lies in integrating diverse data sources, advanced analytical techniques, and stakeholder engagement, ensuring a more resilient approach to mitigating trace metal pollution and protecting environmental and public health.
Journal Article