Chapter 20 Optimization Methods in Dynamic Portfolio Management

Birge, John R.

doi:10.1016/s0927-0507(07)15020-9

Cited by 12 publications

(3 citation statements)

References 72 publications

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“…These computations typically execute for only a small fraction of the total execution time. The Blackscholes sanity test uses a no-arbitrage bound [3] on the price of each option to filter out executions that produce option prices that violate basic properties of the Black-Scholes model. The SOR benchmark checks whether the computed average is between the minimum and maximum array value.…”

Section: Hardware Reliability and Energy Specificationsmentioning

confidence: 99%

Chisel

Misailovíc¹,

Carbin²,

Achour³

et al. 2014

SIGPLAN Not.

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The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy.We present Chisel, a system for reliability-and accuracyaware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification.We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the original (exact) kernel implementations while preserving important reliability guarantees.

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Section: Hardware Reliability and Energy Specificationsmentioning

confidence: 99%

Chisel

Misailovíc¹,

Carbin²,

Achour³

et al. 2014

SIGPLAN Not.

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“…There may also be constraints such as a prohibition against short-selling, θ ≥ 0, or an upper bound on a risk measure of W (T ). For background on portfolio optimization, see [14,28,33].…”

Section: Overview Of Financial Simulation Problemsmentioning

confidence: 99%

“…For an overview, see [14]. Another method uses Monte Carlo to estimate conditional expectations involving Malliavin derivatives, which are proved to be the optimal portfolio weights for a portfolio optimization in a complete market [29].…”

Section: Financial Optimization Problemsmentioning

confidence: 99%

Monte Carlo Computation in Finance

Staum

2009

Monte Carlo and Quasi-Monte Carlo Methods 2008

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This advanced tutorial aims at an exposition of problems in finance that are worthy of study by the Monte Carlo research community. It describes problems in valuing and hedging securities, risk management, portfolio optimization, and model calibration. It surveys some areas of active research in efficient procedures for simulation in finance and addresses the impact of the business context on the opportunities for efficiency. There is an emphasis on the many challenging problems in which it is necessary to perform several similar simulations.

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