Risk Management: Common Issues and Lessons Learned

Risk and return are two sides of the same coin.  Almost every profit making opportunity involves risk. Stakeholders rely on senior management to find profit opportunities and manage risk appropriately. Risk management does not mean complete risk avoidance.  It is the proactive  process by which a company avoids unwanted risk and takes acceptable risk, the nature and level of which are:

  • Consistent with a company’s business plan and within its ability to manage.
  • Not so large or so concentrated that the company’s survival is at risk.
  • Optimized versus the potential returns offered

A key goal of risk managers is to provide senior management and board with timely, accurate and complete information that is sufficient to assess and control risk. This requires them to follow a streamlined process of risk identification, measurement, reporting, monitoring, and risk control. Good risk management will not prevent all losses, but it should prevent large surprises.

As the complexity of their portfolios increases, they will require a different approach to manage risk. Managers use advanced pricing models that help them to unbundle risks of various security and derivative positions, and categorize similar exposures generated by a variety of different and complex positions. The unbundling and aggregation process provides the risk manager with a tool that helps to predict portfolio performance under different market movements and uncover risk concentrations created by a combination of seemingly unrelated positions.

The following are a few common issues and lessons learned about risk management.

  • Reliance on a single measure of risk (ex. VaR, RWA).
  • Models inappropriately applied to the products for which they are used.
  • Over-reliance or lack of understanding of models.  The function of models is to unbundle financial products into more basic exposures so that like exposures can be aggregated.  However, even the best models produce only approximations of actual performance.
  • Loss of information on liquidity, concentration and basis risk in the exposure aggregation process, combined with insufficient inquiry into risk drivers.
    • Hedging versus leverage.
    • Where does market risk have the potential to become investment or credit risk?
  • Lack of capture or understanding of portfolio convexity.  A combination of illiquidity and negative convexity can be toxic.
  • Ownership, validation and controls in the risk reporting process.
  • Handoffs and inconsistency in risk systems and processes.
  • Distortions caused by the use of regulatory (trading vs. banking book) and accounting conventions (ex. accrual accounting vs. mark-to-market), causes inconsistent treatment of similar exposures.
  • Partial views of risk: Work with your risk management partners and risk decision-makers to find out where risk is reinforcing (ex. Wrong-way risk).
  • Exemptions.  Risks will tend to migrate to areas that are not covered by thorough review and controls.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.