Risks Inherent in Trading Activities

For capital-markets and trading activities, risk is generally defined as the potential for loss on an instrument, portfolio, or activity. Thus, the risks referred in this article are discussed in terms of the impact of some event on value(value-at-risk) and income (earnings-at-risk) from the instrument, activity, or portfolio being addressed.

Some of the risks inherent in the trading process are described below:

Market (price) risk is the risk that the value of a financial instrument or a portfolio of financial instruments will change as a result of a change in market conditions (for example, interest-rate movement).

Funding-liquidity risk refers to the ability to meet investment and funding requirements arising from cash-flow mismatches.

Market-liquidity risk refers to the risk of being unable to close out open positions quickly enough and in sufficient quantities at a reasonable price to avoid adverse financial impacts.

Counterparty credit risk is the risk that a counterparty to a transaction will fail to perform according to the terms and conditions of the contract, thus causing the holder of the claim to suffer a loss in cash flow or market value.

Clearing/settlement credit risk is (1) the risk that a counterparty who has received a payment or delivery of assets defaults before delivery of the asset or payment or (2) the risk that technical difficulties interrupt delivery or settlement despite the counterparty’s ability or willingness to perform.

Operations and systems risk is the risk of human error or fraud or the risk that systems will fail to adequately record, monitor, and account for transactions or positions.

Legal risk is the risk that a transaction cannot be consummated because of some legal barrier, such as inadequate documentation, a regulatory prohibition on a specific counterparty, and nonenforceability of bilateral and multilateral close-out netting and collateral arrangements in bankruptcy.

Reputational risk is the risk arising from negative public opinion regarding an institution’s products or activities.

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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.