Support Analytics For Asset-Based Trading
Financial Engineering tools for asset-backed trading and hedging strategies. By Carlos Blanco & Michael Pearce
HERE, WE DISCUSS the role of risk and trading analytics to support asset-based optimization and trading strategies – building upon our prior contributions which showed how dynamic risk simulation tools can be applied to complex decision problems involving multiple sources of risk and state variables as well as calculate risk, value and performance metrics.1
Optimization of Physical Assets & Trading Strategies Traditionally, physical assets
such as storage facilities, pipelines, transmission networks, refineries, and power plants were operated by engineers rather than trading groups, resulting in sub-optimal performance from a profit maximization perspective. Many energy and commodity firms
have established asset-backed trading groups whose objective is to enhance the risk adjusted profitability of its physical assets based on observable market spreads, their potential variability, as well as the specific asset operating constraints. Succesful asset-based trading groups
have been able to articulate and communicate the value and risk of those strategies with asset operators as well as senior management. For different reasons, poor communication and
lack of understanding between engineers that operate and maintain the assets and the ‘financial engineers’ in charge of modelling the performance of those assets is the norm rather than the exception. For example, the first generation models used
by risk groups and traders were too simplistic and did not accurately capture the market and asset dynamics and constraints, leading to poor or unrealistic decisions from an operational perspective. Figure 1 shows the key building blocks for the design of decision support analytics for asset-based strategies. The valuation, risk metrics and hedging ratios calculated from model that do not fit the criteria outlined above are not just likely to be inaccurate,
but also likely to lead to sub-optimal decisions that would impact the asset-base strategy’s profitability. Fortunately, one of the more relevant developments
in recent years in financial engineering applied to energy risk modelling is the pricing, modelling and hedging of physical assets and asset-based strategies for power and gas portfolios.
Figure 1: Building Blocks of Asset-Backed Trading Support Analytics
Realistic Multi-Step Spot, Forward Curve & Spread Scenario
Asset & Trading Constraints Explicitly Captured & Modelled
Integration Of Market Liquidity, Hedging & Transaction Costs
Backtesting Performance Of Asset-Based Trading Strategy
Source:
www.nquantx.com
Risk Management For Asset-Based Trading Strategies Risk management for asset-based trading
strategies involves identifying and quantifying multiple risk dimensions. To capture those risk dimensions, a dynamic simulation framework
Many energy and commodity firms have established asset- backed trading groups
with three critical components is needed: Ability to handle multiple risk factors (e.g. commodities, credit events, operational issues), multiple instruments (e.g. physical contracts, derivatives), as well as the ability to capture events taking place at multiple steps in time.
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