Analysis of business demography using markov chains : an application to Belgian data

This paper applies the theory of finite Markov chains to analyse the demographic evolution of Belgian enterprises. While other methodologies concentrate on the entry and exit of firms, the Markov approach also analyses migrations between economic sectors. Besides helping to provide a fuller picture of the evolution of the population, Markov chains also enable forecasts of its future composition to be made, as well as the computation of average lifetimes of companies by branch of activity. The method is applied to Belgian data from the Crossroads Bank for Enterprises (CBE). To ensure compliance with Eurostat-OECD definitions, only 'active' enterprises, i.e. enterprises with a positive turnover and/or at least one employee, are considered. The forecasting method is applied to simulate the demographic evolution of the CBE population between 2000 and 2006. This simulation seems to match well the observed changes. Taking migrations into account yields better forecasts than if they are not considered. Moreover, several off-diagonal percentages in the transition matrix are sigificantly different from zero. A case study shows that these migrations are changes in main activity and not the consequence of corrections of wrongly classified firms. Next, the average remaining lifetime and the average age of enterprises in a particular branch of activity is computed and analysed. These lifetimes and ages differ considerably across branches. As expected the life-times of public services are longer than average. Shorter lifetimes combined with an increasing number of enterprises is an indication of renewal inside the branch. A low average age is a sign of relatively new branches. Comparing age to total expected lifetime yields an indicator of closeness to extinction. This might be an indicator of the maturity of the branch. The method is more generally applicable in the sense that it can be used to analyse other populations than those from the CBE and other partitions of the populationBusiness demography, Markov chains, Transition matrix

Community Bank Assessment of Agricultural Portfolio Risk Exposure: The Literature and the Methods in Use

Agricultural Finance,

Managing uncertainty:financial, actuarial and statistical modelling.

present value; Value; Actuarial;

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.

Analysis of Polish business demography using Markov chains

The article describes the use of the Markov chains methodology for analysis of demographic evolution of Polish enterprises in the years 2003 - 2009. According to the results’ presented in the article, flexibility of Polish companies’ activity in changing economic conditions is stable. The level of migration between sectors is low and limited to several sectors. Expected company life is relatively short (on average, Polish companies exist more than twice shorter than e.g. Belgian companies subject to a study by the National Bank of Belgium). In general, the least “vital” companies may be considered companies from the transport section and then from the building industry, other services and commerce sections. Enterprises that stay on the market the longest are companies from the agricultural and industrial sectors. The mean value of the closeness to extinction indicator amounts to 46% for the whole population. Among all sectors and sections, non-specialised exporters have the highest average age. State-owned companies have significantly higher both the average age and the remaining lifetime than private companies. The bigger is a company the higher is its average age and average remaining lifetime.Business demography, Markov chains, Transition matrix

Dynamic analysis of bankruptcy and economic waves

The procedures presented in this paper provide a dynamic apparatus of crediting the industrial operating systems with the assignment to avoid their correlated defaulting, to conserve general safety and soundness and improve its ability to serve as a source for sustainable growth for economy. The quality of operating firms at different periods is evaluated using the migration matrixes between some classes of default risk.credit risk ; economic growth ; regulation

Capital Regulation, Liquidity Requirements and Taxation in a Dynamic Model of Banking

This paper formulates a dynamic model of a bank exposed to both credit and liquidity risk, which can resolve financial distress in three costly forms: fire sales, bond issuance and equity issuance. We use the model to analyze the impact of capital regulation, liquidity requirements and taxation on banks' optimal policies and metrics of efficiency of intermediation and social value. We obtain three main results. First, mild capital requirements increase bank lending, bank efficiency and social value relative to an unregulated bank, but these benefits turn into costs if capital requirements are too stringent. Second, liquidity requirements reduce bank lending, efficiency and social value significantly, they nullify the benifits of mild capital requirements, and their private and social costs increase monotonically with their stringency. Third, increases in corporate income and bank liabilities taxes reduce bank lending, bank effciency and social value, with tax receipts increasing with the former but decreasing with the latter. Moreover, the effects of an increase in both forms of taxation are dampened if they are jointly implemented with increases in capital and liquidity requirements.Capital requirements;liquidity requirements;taxation of liabilities. JEL Classifications

Business intelligence in risk management: Some recent progresses

Risk management has become a vital topic both in academia and practice during the past several decades. Most business intelligence tools have been used to enhance risk management, and the risk management tools have benefited from business intelligence approaches. This introductory article provides a review of the state-of-the-art research in business intelligence in risk management, and of the work that has been accepted for publication in this issue of Information Sciences

Extending Credit Risk (Pricing) Models for the Simulation of Portfolios of Interest Rate and Credit Risk Sensitive Securities

We discuss extensions of intensity based models for pricing credit risk and derivative securities to the simulation and valuation of portfolios. The stochasticity in interest rates, credit spreads (default intensities) and rating migrations are incorporated in a unified framework. Scenarios of future prices of all securities are calculated in a risk-neutral world. The calculated prices are consistent with observed prices and the term structure of default free and defaultable interest rates. Three applications are discussed: (i) study of the inter-temporal price sensitivity of credit bonds to changes in interest rates, default probabilities, recovery rates and rating migration, (ii) portfolio simulations with attribution of changes to credit events and interest rates and, (iii) tracking of corporate bond indices. Key words: credit risk, default risk, simulation, integrated product management

Artificial Intelligence in Engineering Risk Analytics

Risks exist in every aspect of our lives, and can mean different things to different people. While negative in general they always cause a great deal of potential damage and inconvenience for stakeholders. Recent engineering risks include the Fukushima nuclear plant disaster from the 2011 tsunami, a year that also saw earthquakes in New Zealand, tornados in the US, and floods in both Australia and Thailand. Earthquakes, tornados (not to mention hurricanes) and floods are repetitive natural phenomenon. But the October 2011 floods in Thailand were the worst in 50 years, impacting supply chains including those of Honda, Toyota, Lenovo, Fujitsu, Nippon Steel, Tesco, and Canon. Human-induced tragedies included a clothing factory fire in Bangladesh in 2012 that left over 100 dead. Wal-Mart and Sears supply chains were downstream customers. The events of Bhopal in 1984, Chernobyl in 1986, Exxon Valdez in 1989, and the Gulf oil spill of 2010 were tragic accidents. There are also malicious events such as the Tokyo Sarin attach in 1995, The World Trade Center and Pentagon attacks in 2001, and terrorist attacks on subways in Madrid (2004), London (2005), and Moscow (2010). The news brings us reports of such events all too often. The next step up in intensity is war, which seems to always be with us in some form somewhere in the world. Complex human systems also cause problems. The financial crisis resulted in recession in all aspects of the economy. Risk and analytics has become an important topic in today’s more complex, interrelated global environment, replete with threats from natural, engineering, economic, and technical sources (Olson and Wu, 2015)