Response surface methodology for simulating hedging and trading strategies

Authors:
R. Evren Baysal;Barry L. Nelson;Jeremy Staum
Affiliations:
Northwestern University, Evanston, IL;Northwestern University, Evanston, IL;Northwestern University, Evanston, IL
Venue:
Proceedings of the 40th Conference on Winter Simulation
Year:
2008

Citing 1
Cited 3

Design and Modeling for Computer Experiments (Computer Science & Data Analysis)

Design and Modeling for Computer Experiments (Computer Science & Data Analysis)

Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation

Operations Research
XDRA: exploration and optimization of last-level cache for energy reduction in DDR DRAMs

Proceedings of the 50th Annual Design Automation Conference
Simulation optimization of a multi-stage multi-product paint shop line with Response Surface Methodology

Simulation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Suppose that one wishes to evaluate the distribution of profit and loss (P&L) resulting from a dynamic trading strategy. A straightforward method is to simulate thousands of paths (i.e., time series) of relevant financial variables and to track the resulting P&L at every time at which the trading strategy rebalances its portfolio. In many cases, this requires numerical computation of portfolio weights at every rebalancing time on every path, for example, by a nested simulation performed conditional on market conditions at that time on that path. Such a two-level simulation could involve many millions of simulations to compute portfolio weights, and thus be too computationally expensive to attain high accuracy. We show that response surface methodology enables a more efficient simulation procedure: in particular, it is possible to do far fewer simulations by using kriging to model portfolio weights as a function of underlying financial variables.