Approximating Intraday Derivatives VaR with Overnight Scenarios

Summary

The document asks how to speed up intraday value-at-risk calculations for derivatives portfolios when full historical scenario revaluation is too slow, especially for contracts requiring Monte Carlo simulation. It proposes doing full valuation overnight and retaining scenario P&L vectors for each contract, then updating those results as market data changes during the day.

Two candidate approximations are raised: adjust scenario P&Ls using sensitivities such as delta and gamma, or precompute valuations across a discrete grid of underlying price changes and interpolate intraday. The central question is whether either method improves on starting with a delta-gamma approximation. The document reports no experiments, performance measurements, or conclusion, so it presents research questions rather than a validated method. Its ideas also focus on price changes and do not specify how to handle other market factors, interactions, or interpolation error in a production risk estimate.

Key ideas

• Overnight full revaluation can produce scenario P&L vectors for intraday use.
• Delta and gamma sensitivities could adjust scenario P&Ls as market prices move.
• A discrete valuation grid could support intraday fair-value estimates through interpolation.
• The document does not establish whether either approach is more accurate or efficient than delta-gamma approximation.

Tags

# Intraday Value at Risk approximations


# Intraday Value at Risk approximations












We use full valuation of derivatives portfolios using scenarios from historical data.

For simple contracts, this is relatively fast.

For contracts requiring monte carlo simulation, this becomes painfully slow for large portfolios.

I am thinking about ways of improving the current situation. Most likely, in my mind, this would involve a full valuation done over night, resulting in PnL vectors per contract and possibly other results.

During intraday, as market data changes, these "base PnLs" could probably be used along with any other previously calculated result to get a decent approximation without having to perform a full valuation.

I am looking for some existing work making these ideas concrete (or possibly suggesting a different approach entirely...).

A basic idea would be to compute sensitivities alongside the full valuation. In this way, the PnLs will be accompanied by, say, delta and gamma sensitivities, and these could be used in combination with the new market data to get an updated PnL vector.

The question is if this is feasible or indeed if it's any better than simply using a delta-gamma approximation to begin with?

Another idea could be to rely on sensitivities implicitly by simply valuing the contract under a discrete set of price points with the current price in the middle, say [-3%, -2%, -1%, -0.5%, 0, ...], and performing interpolation for finding a fair value intraday.

Any feedback is welcome.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.