THE BUSINESS ISSUE

The European branch of the large Australian commodity trading company had recently expanded into a new trading business - ocean freight trading. The new model proved to be very profitable, and soon the Australian Head Office formed a new traders' desk to support the strategy. As the new business grew, its Risk Management became problematic. This was despite the fact that the Company had an established and robust Risk framework and a dedicated Risk department.

 There were three key issues which prevented effective Risk Management:

a)    The back office operations in Europe and Australia ran different Excel spreadsheets to calculate and report the P&L. The portfolio valuations were based on two different pricing models.

b)   The models used broker quotes which required manual processing by each respective back office. The broker reports were irregular with some market quotes missing. To overcome this some trades were priced based on assumptions about the market, rather than on direct quotes.

As a result, similar trades executed by either branch could be valued differently based on assumptions about the market price and the pricing model used.

c)    The new business was overlooked by existing risk models and as such trading positions were not accurately reported and aggregate risk was not duly quantified.

 

CHANGES IMPLEMENTED

d2e worked with the client to agree and deploy the following three phase approach:

a) Introduce consistent pricing models for daily P&L calculation

b) Automate routine tasks and create accurate position reporting

c) Develop and deploy a market risk model to enable effective Risk Management

To introduce consistent models the two existing trading spreadsheets in Excel were thoroughly examined, and a number of inconsistencies fixed. A new generic template was created to accommodate both business models. Research revealed that the freight market is regulated by the Baltic Exchange, which publishes daily quotes for specified overlapped future time intervals: spot, months, quarters and calendar years. These 'raw' quotes could not be used directly to calculate the P&L but were rather used to create a methodology for building forward curves. The process was documented and a working algorithm was built. The technical solution in the form of a VBA code was implemented in a Reuters PowerPlus spreadsheet. The live data feed from the Baltic Exchange was sourced from Reuters, and the freight forward curves were generated on the fly. The format of the output curves was designed to match the input of the working trading spreadsheets. To make the P&L calculation independent from the trading desks, the task to process market data and distribute generated forward curves was delegated to the Market Risk team.

Routine tasks were automated by developing a new application to process trading spreadsheets in Visual Basic. The new platform (called 'Chartering Manager') introduced such attributes as generating trading positions, splitting trading books by sub accounts and counterparties, performing detailed portfolio P&L reconciliation, monitoring trading limits, and generating input files for Value-at-Risk model. For reporting purposes 'Chartering Manager' could calculate the P&L across a number of time horizons: year-to-date, month-do-date, previous_report-to-date, and 1-day P&L. See picture for indicative screenshots of the Chartering Manager.

Case_Study_009_Pic_001

A Risk Management approach comprising of Value-at-Risk (VaR), Stress testing analysis and monitoring trading limits was deployed:

  • The freight forward curve generation algorithm was modified by adding a module that generated historical curves for all trading routes retrospectively. This historical data was then used to populate the volatility and correlation engine. Another application ('Freight Grabber') was built to populate the volatility and correlation engine with fresh market prices every time the VaR model was run. Open positions were supplied to the VaR model by the 'Chartering Manager'. The VaR model produced Monte Carlo, Historical and Analytical VaRs.
  • The Stress Testing analysis module provided users with the flexibility of performing various shocks to the major forward curves. The parallel shift (up or down) of the forward curves or individual forward months’ adjustments to each of the five forward curves (tilting) was user-selectable. The resulting report showed break-down of the stressed portfolio P&L by routes and businesses across shipment months.
  • Compliance of established trading limits was monitored by the 'Chartering Manager'. In the event of any breach, a breaking parameter was highlighted and a warning message was generated.

 

BENEFITS DELIVERED

  • Accurate position reporting and reliable daily P&L calculation for the new trading activity was created and approved by auditors
  • Accurate P&L reconciliation with traders and back office books every time the model was run
  • Reduction in Operational risk
  • Dramatically improved turnaround times with the ability to run P&L and Risk model on a daily basis
  • Alignment of risk measurement for the new business with the existing company Risk framework
  • Diversification benefit was demonstrated on a group level releasing trading limits for business units
  • Ability to allocate capital objectively - risk calculations by sub portfolios showed the distribution of P&L among business units and traders' desks
  • Ability to use stress test results to supplement VaR when VaR model produced arguable results
  • Ability to use risk calculations by counterparties as an input for the contingency credit risk report