Financial Innovation in Multi-Period Economies

I present an attempt to construct multi-period, finite horizon extensions to the well -known two- period financial innovations literature. I first extend the definition of competitive equilibrium with innovations. It is shown that, with a dominating houseIncomplete markets, financial innovation, multiperiod economies

Evolutionary multi-stage financial scenario tree generation

Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations (represented as scenario trees) have been suggested in the literature. In this paper, a new evolutionary algorithm to create scenario trees for multi-stage financial optimization models will be presented. Numerical results and implementation details conclude the paper

Assets Returns Volatility and Investment Horizon: The French Case

This paper explores French assets returns predictability within a VAR setup. Using quarterly data from 1970Q4 to 2006Q4, it turns out that bonds, equities and bills returns are actually predictable. This feature implies that the investment horizon does indeed matter in the asset allocation. The VAR parameters estimates are then used to compute real returns conditional volatility across investment horizons. The results reveal the same kind of horizon effect as the one found in recent empirical studies using quarterly U.S. data. More specifically, the excess annualized standard deviation of French stocks returns with respect to bills and bonds returns decreases as the investment horizon grows. They suggest that long-horizon investors overstate the share of bonds in their portfolio choice when neglecting the horizon effect on risk of asset returns predictability.asset return predictability, investment horizon, vector autoregression

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl

Applications of biased randomised algorithms and simheuristics to asset and liability management

Asset and Liability Management (ALM) has captured the attention of academics and financial researchers over the last few decades. On the one hand, we need to try to maximise our wealth by taking advantage of the financial market and, on the other hand, we need to cover our payments (liabilities) over time. The purpose of ALM is to give investors a series of resources or techniques to select the appropriate assets on the financial market that respond to the aforementioned two key factors: cover our liabilities and maximise our wealth. This thesis presents a set of techniques that are capable of tackling realistic financial problems without the usual requirement of considerable computational resources. These techniques are based on heuristics and simulation. Specifically, a biased randomised metaheuristic model is developed that has a direct application in the way insurance companies usually operate. The algorithm makes it possible to efficiently select the smallest number of assets, mainly fixed income, on the balance sheet while guaranteeing the company's obligations. This development allows for the incorporating of the credit quality of the issuer of the assets used. Likewise, a portfolio optimisation model with liabilities is developed and solved with a genetic algorithm. The portfolio optimisation problem differs from the usual one in that it is multi-period, and incorporates liabilities over time. Additionally, the possibility of external financing is included when the entity does not have sufficient cash. These conditions give rise to a complex problem that is efficiently solved by an evolutionary algorithm. In both cases, the algorithms are improved with the incorporation of Monte Carlo simulation. This allows the solutions to be robust when considering realistic market situations. The results are very promising. This research shows that simheuristics is an ideal method for this type of problem.La gestión de activos y pasivos (asset and liability management, ALM) ha acaparado la atención de académicos e investigadores financieros en las últimas décadas. Por un lado, debemos tratar de maximizar nuestra riqueza aprovechando el mercado financiero, y por otro, debemos cubrir nuestros pagos (pasivos) a lo largo del tiempo. El objetivo del ALM es dotar al inversor de una serie de recursos o técnicas para seleccionar los activos del mercado financiero adecuados para obedecer a los dos factores clave mencionados: cumplir con nuestros pasivos y maximizar nuestra riqueza. Esta tesis presenta un conjunto de técnicas que son capaces de abordar problemas financieros realistas sin la necesidad habitual de considerables recursos computacionales. Estas técnicas se basan en la heurística y la simulación. En concreto, se desarrolla un modelo metaheurístico sesgado que tiene una aplicación directa en la operación habitual de inmunización de las compañías de seguros. El algoritmo permite seleccionar eficientemente el menor número de activos, principalmente de renta fija, en el balance y garantizar las obligaciones de la compañía. Este desarrollo permite incorporar la calidad crediticia del emisor de los activos utilizados. Asimismo, se desarrolla un modelo de optimización de la cartera con el pasivo y se resuelve con un algoritmo genético. El problema de optimización de la cartera difiere del habitual en que es multiperiodo e incorpora los pasivos a lo largo del tiempo. Además, se incluye la posibilidad de financiación externa cuando la entidad no tiene suficiente efectivo. Estas condiciones dan lugar a un problema complejo que se resuelve eficientemente mediante un algoritmo evolutivo. En ambos casos, los algoritmos se mejoran con la incorporación de la simulación de Montecarlo. Esto permite que las soluciones sean robustas cuando consideramos situaciones de mercado realistas. Los resultados son muy prometedores. Esta investigación demuestra que la simheurística es un método ideal para este tipo de problemas.La gestió d'actius i passius (asset and liability management, ALM) ha acaparat l'atenció d'acadèmics i investigadors financers les darreres dècades. D'una banda, hem de mirar de maximitzar la nostra riquesa aprofitant el mercat financer, i de l'altra, hem de cobrir els nostres pagaments (passius) al llarg del temps. L'objectiu de l'ALM és dotar l'inversor d'una sèrie de recursos o tècniques per seleccionar els actius del mercat financer adequats per obeir als dos factors clau esmentats: complir els passius i maximitzar la nostra riquesa. Aquesta tesi presenta un conjunt de tècniques que són capaces d'abordar problemes financers realistes sense la necessitat habitual de recursos computacionals considerables. Aquestes tècniques es basen en l'heurística i la simulació. En concret, es desenvolupa un model metaheurístic esbiaixat que té una aplicació directa a l'operació habitual d'immunització de les companyies d'assegurances. L'algorisme permet seleccionar eficientment el menor nombre d'actius, principalment de renda fixa, al balanç i garantir les obligacions de la companyia. Aquest desenvolupament permet incorporar la qualitat creditícia de l'emissor dels actius utilitzats. Així mateix, es desenvolupa un model d'optimització de la cartera amb el passiu i es resol amb un algorisme genètic. El problema d'optimització de la cartera difereix de l'habitual en el fet que és multiperíode i incorpora els passius al llarg del temps. A més, s'inclou la possibilitat de finançament extern quan l'entitat no té prou efectiu. Aquestes condicions donen lloc a un problema complex que es resol eficientment mitjançant un algorisme evolutiu. En tots dos casos, els algorismes es milloren amb la incorporació de la simulació de Montecarlo. Això permet que les solucions siguin robustes quan considerem situacions de mercat realistes. Els resultats són molt prometedors. Aquesta recerca demostra que la simheurística és un mètode ideal per a aquesta mena de problemes.Tecnologías de la información y de rede

Dynamic General Equilibrium and T-Period Fund Separation

In a dynamic general equilibrium model, we derive conditions for a mutual fund separation property by which the savings decision is separated from the asset allocation decision. With logarithmic utility functions, this separation holds for any heterogeneity in discount factors, while the generalization to constant relative risk aversion holds only for homogeneous discount factors but allows for any heterogeneity in endowments. The logarithmic case provides a general equilibrium foundation for the growth-optimal portfolio literature. Both cases yield equilibrium asset pricing formulas that allow for investor heterogeneity, in which the return process is endogenous and asset prices are determined by expected discounted relative dividends. Our results have simple asset pricing implications for the time series as well as the cross section of relative asset prices. It is found that on data from the Dow Jones Industrial Average, a risk aversion smaller than in the logarithmic case fits bes

Labour supply: a review of alternative approaches

This chapter surveys existing approaches to modeling labor supply and identifies important gaps in the literature that could be addressed in future research. The discussion begins with a look at recent policy reforms and labor market facts that motivate the study of labor supply. The analysis then presents a unifying framework that allows alternative empirical formulations of the labor supply model to be compared and their resulting elasticities to be interpreted. This is followed by critical reviews of alternative approaches to labor-supply modeling. The first review assesses the difference in-differences approach and its relationship to natural experiments. The second analyzes estimation with non-linear budget constraints and welfare-program participation. The third appraises developments of family labor-supply models including both the standard unitary and collective labor-supply formulations. The fourth briefly explores dynamic extensions of the labor supply model, characterizing how participation decisions, learning-by-doing, human capital accumulation and habit formation affect the analysis of the lifecycle model. At the end of each of the four broad reviews, we summarize a selection of the recent empirical findings. The concluding section asks whether the developments reviewed in this chapter place us in a better position to answer the policy-reform questions and to interpret the trends in participation and hours with which we began this review. q1999 Elsevier Science B.V. All rights reserved.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts

Modelling Investment Strategies: Bayesian Learning, Regime Switches and Evolutionary Finance

In this research project we endeavour to model a financial marketplace dominated by a few interacting large institutional investors and draw conclusions about the financial market dynamics that this interaction gives rise to. More specifically, we study the problem of institutional investors, such as pension funds and life assurance companies, which operate in an environment of uncertain cash inflows and uncertain payouts with a minimum threshold. Investment strategies are taken as model primitives and an artificial financial market is populated by multiple investor types. Trading and investment decisions take place in discrete time. There exist a certain predetermined number of long-lived risky assets paying a random amount of dividends at each discrete point in time, as well as a risk-free asset with a constant interest rate. The risky assets generate random dividend intensities. Asset payoffs are aggregated and paid to investors at the end of each time period. The general economy is assumed to follow a hidden Markov model with two states, corresponding to normal and recessionary regimes. The existence of a minimum consumption constraint implies the possibility of bankruptcy. The effect of this occurrence on market clearing is modelled explicitly. We solve our model by means of numerical simulation. The size of the wealth endowment of the different agents is monitored through time over a large number of simulation runs. Our results suggest that both trend following and value investing strategies can be selected by the market under different circumstances. These two results lead to markedly different outcomes for the economy, as the prevalence of the trend following style leads to destabilization of the marketplace, volatility clustering and severe deflationary spirals. Dividend yield and modified regime-switching CAPM strategies are never selected by the market