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"Schumann, Enrico"
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Heuristic optimisation in financial modelling
There is a large number of optimisation problems in theoretical and applied finance that are difficult to solve as they exhibit multiple local optima or are not ‘well-behaved’ in other ways (e.g., discontinuities in the objective function). One way to deal with such problems is to adjust and to simplify them, for instance by dropping constraints, until they can be solved with standard numerical methods. We argue that an alternative approach is the application of optimisation heuristics like Simulated Annealing or Genetic Algorithms. These methods have been shown to be capable of handling non-convex optimisation problems with all kinds of constraints. To motivate the use of such techniques in finance, we present several actual problems where classical methods fail. Next, several well-known heuristic techniques that may be deployed in such cases are described. Since such presentations are quite general, we then describe in some detail how a particular problem, portfolio selection, can be tackled by a particular heuristic method, Threshold Accepting. Finally, the stochastics of the solutions obtained from heuristics are discussed. We show, again for the example from portfolio selection, how this random character of the solutions can be exploited to inform the distribution of computations.
Journal Article
Optimal enough?
An alleged weakness of heuristic optimisation methods is the stochastic character of their solutions: instead of finding the truly optimal solution, they only provide a stochastic approximation of this optimum. In this paper we look into a particular application, portfolio optimisation. We demonstrate that the randomness of the ‘optimal’ solution obtained from the algorithm can be made so small that for all practical purposes it can be neglected. More importantly, we look at the relevance of the remaining uncertainty in the out-of-sample period. The relationship between in-sample fit and out-of-sample performance is not monotonous, but still, we observe that up to a point better solutions in-sample lead to better solutions out-of-sample. Beyond this point there is no more cause for improving the solution any further: any in-sample improvement leads out-of-sample only to financially meaningless improvements and unpredictable changes (noise) in performance.
Journal Article
Risk–reward optimisation for long-run investors: an empirical analysis
A common approach in portfolio selection is to characterise a portfolio of assets by a desired property, the reward, and something undesirable, the risk. These properties are often identified with mean and variance of returns, respectively, even though, given the non-Gaussian nature of financial time series, alternative specifications like partial and conditional moments, quantiles, and drawdowns seem theoretically more appropriate. We analyse the empirical performance of portfolios selected by optimising risk–reward ratios constructed from such alternative functions. We find that in many cases these portfolios outperform our benchmark (minimum-variance), in particular when long-run returns are concerned. We also find, however, that all the strategies tested (including minimum-variance) are sensitive to relatively small changes in the data. The main theme throughout our analysis is that minimising risk, as opposed to maximising reward, leads to good out-of-sample performance. Adding a reward-function to the selection criterion usually improves a given strategy only marginally.
Journal Article
Constructing 130/30-portfolios with the Omega ratio
We construct portfolios with an alternative selection criterion, the Omega function, which can be expressed as the ratio of two partial moments of a portfolio's return distribution. The main purpose of the article is to investigate the empirical performance of the selected portfolios, especially the effects of allowing short positions. Many studies on portfolio optimisation assume that short sales are not allowed. This is despite the fact that theoretically, short positions can improve the risk-return characteristics of a portfolio, and practically, institutional investors can and do sell stocks short. We investigate whether removing the non-negativity constraint really improves out-of-sample portfolio performance under realistic assumptions, that is when optimal weights need to be estimated from the data and different transaction costs apply to long and short positions.
Journal Article
Risk-Reward Ratio Optimisation (Revisited)
We study the empirical performance of alternative risk and reward specifications in portfolio selection. In particular, we look at models that take into account asymmetry of returns, and treat losses and gains differently. In tests on a dataset of German equities we find that portfolios constructed with the help of such models generally outperform the market index and in many cases also the risk-based benchmark (minimum variance). In part, higher returns can be explained by exposure to factors such as momentum and value. Nevertheless, a substantial part of the performance cannot be explained by standard asset-pricing models.
Paper
A note on 'good starting values' in numerical optimisation
Many optimisation problems in finance and economics have multiple local optima or discontinuities in their objective functions. In such cases it is stressed that 'good starting points are important'. We look into a particular example: calibrating a yield curve model. We find that while 'good starting values' suggested in the literature produce parameters that are indeed 'good', a simple best-of-n-restarts strategy with random starting points gives results that are never worse, but better in many cases.
Paper
Calibrating Option Pricing Models with Heuristics
Calibrating option pricing models to market prices often leads to optimisation problems to which standard methods (like such based on gradients) cannot be applied. We investigate two models: Heston's stochastic volatility model, and Bates's model which also includes jumps. We discuss how to price options under these models, and how to calibrate the parameters of the models with heuristic techniques.
Paper
Heuristic Optimisation in Financial Modelling
There is a large number of optimisation problems in theoretical and applied finance that are difficult to solve as they exhibit multiple local optima or are not 'well- behaved' in other ways (eg, discontinuities in the objective function). One way to deal with such problems is to adjust and to simplify them, for instance by dropping constraints, until they can be solved with standard numerical methods. This paper argues that an alternative approach is the application of optimisation heuristics like Simulated Annealing or Genetic Algorithms. These methods have been shown to be capable to handle non-convex optimisation problems with all kinds of constraints. To motivate the use of such techniques in finance, the paper presents several actual problems where classical methods fail. Next, several well-known heuristic techniques that may be deployed in such cases are described. Since such presentations are quite general, the paper describes in some detail how a particular problem, portfolio selection, can be tackled by a particular heuristic method, Threshold Accepting. Finally, the stochastics of the solutions obtained from heuristics are discussed. It is shown, again for the example from portfolio selection, how this random character of the solutions can be exploited to inform the distribution of computations.
Paper
Implementing Binomial Trees
This paper details the implementation of binomial tree methods for the pricing of European and American options. Pseudocode and sample programmes for Matlab and R are given.
Paper