Unbundling and Dynamically Hedging Risks

Most financial products, whether on-balance sheet or off-balance sheet, will generally contain more than one type of market risk exposure. Hedging one financial product will therefore require use of more than one hedging instrument. For example, a foreign-currency denominated coupon paying bond will have interest rate risk and foreign exchange risk, and both the risks need to be hedged using different hedging instruments.

Hedging can be simplified by unbundling or breaking down the market risk of a product into its basic elements. These unbundled market risk exposures can then be managed separately increasing risk liquidity.  For example if we have a customized OTC contract, it may be illiquid; however, its component risk may be liquid, and hedgeable.

Once the market risk exposures have been unbundled, some unbundled exposures can be managed as separate transactions, while other others can be managed on a portfolio basis. The more closely hedged the risks in a transaction are, the less is the need to actively manage the residual risk. Dynamic hedging is used to hedge the uncovered risks.

When a large number of market participants use dynamic hedging  and trade, it introduces a feedback mechanism and tends to amplify the price movements. Some managers may estimate exposure on the basis of the assumption that dynamic hedging or other rapid portfolio adjustments will keep risk within a given range even in the face of large changes in market prices. However, such portfolio adjustments depend on the existence of sufficient market liquidity to execute the desired transactions, at reasonable costs, as underlying prices change. If a liquidity disruption were to occur, difficulty in executing the transactions needed to change the portfolio’s exposure will cause the actual risk to be higher than anticipated. Those institutions who have open positions in written options and, thus, are short volatility and gamma will be the most exposed.

Data Science in Finance: 9-Book Bundle

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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.