We introduce a modelling paradigm which integrates credit risk and market risk in describing the random dynamical behaviour of the underlying fixed income assets. We then consider an asset and liability management (ALM) problem and develop a multistage stochastic programming model which focuses on optimum risk decisions. These models exploit the dynamical multiperiod structure of credit risk and provide insight into the corrective recourse decisions whereby issues such as the timing risk of default is appropriately taken into consideration. We also present an index tracking model in which risk is measured (and optimised) by the CVaR of the tracking portfolio in relation to the index. In-sample as well as out-of-sample (backtesting) experiments are undertaken to validate our approach. The main benefits of backtesting, that is, ex-post analysis are that (a) we gain insight into asset allocation decisions, and (b) we are able to demonstrate the feasibility and flexibility of the chosen framework.

We develop a stochastic programming model to address in a unified manner a number of interrelated decisions in international portfolio management: optimal portfolio diversification and mitigation of market and currency risks. The goal is to control the portfolio’s total risk exposure and attain an effective balance between risk and expected return. By incorporating options and forward contracts in the portfolio optimization model we are able to numerically assess the performance of alternative tactics for mitigating exposure to the primary risks. We find that control of market risk with options has more significant impact on portfolio performance than currency hedging. We demonstrate through extensive empirical tests that incremental benefits, in terms of reducing risk and generating profits, are gained when both the market and currency risks are jointly controlled through appropriate means.

We examine valuation procedures that can be applied to incorporate options in scenario-based portfolio optimization models. Stochastic programming models use discrete scenarios to represent the stochastic evolution of asset prices. At issue is the adoption of suitable procedures to price options on the basis of the postulated discrete distributions of asset prices so as to ensure internally consistent portfolio optimization models. We adapt and implement two methods to price European options in accordance with discrete distributions represented by scenario trees and assess their performance with numerical tests. We consider features of option prices that are observed in practice. We find that asymmetries and/or leptokurtic features in the distribution of the underlying materially affect option prices; we quantify the impact of higher moments (skewness and excess kurtosis) on option prices. We demonstrate through empirical tests using market prices of the S&P500 stock index and options on the index that the proposed procedures consistently approximate the observed prices of options under different market regimes, especially for deep out-of-the-money options.

We develop an integrated simulation and optimization framework for multicurrency asset allocation problems. The simulation applies principal component analysis to generate scenarios depicting the discrete joint distributions of uncertain asset returns and exchange rates. We then develop and implement models that optimize the conditional-value-at-risk (CVaR) metric. The scenario-based optimization models encompass alternative hedging strategies, including selective hedging that incorporates currency hedging decisions within the portfolio selection problem. Thus, the selective hedging model determines jointly the portfolio composition and the level of currency hedging for each market via forward exchanges. We examine empirically the benefits of international diversification and the impact of hedging policies on risk–return profiles of portfolios. We assess the effectiveness of the scenario generation procedure and the stability of the model's results by means of out-of-sample simulations. We also compare the performance of the CVaR model against that of a model that employs the mean absolute deviation (MAD) risk measure. We investigate empirically the ex post performance of the models on international portfolios of stock and bond indices using historical market data. Selective hedging proves to be the superior hedging strategy that improves the risk–return profile of portfolios regardless of the risk measurement metric. Although in static tests the MAD and CVaR models often select portfolios that trace practically indistinguishable ex ante risk–return efficient frontiers, in successive applications over several consecutive time periods the CVaR model attains superior ex post results in terms of both higher returns and lower volatility.

We find evidence consistent with risk mispricing in the eurozone sovereign credit market for crisis and non-crisis countries alike, using a novel variable of sovereign debt expansion (DE) that we construct. DE predicts increased default probability, but panel regressions from 2002 to 2017 show a negative association with risk premia, even when controlling for risk appetite and the known determinants of sovereign risk premia. As expected, the negative association was only briefly interrupted by the 2010 Deauville Summit, but it resumed by the onset of the 2011 eurozone crisis. The introduction of quantitative easing in 2015 mutes the negative association, raising the concern of what will happen once quantitative easing ends. Our finding is robust to several model specifications.

Modern insurance products are becoming increasingly complex, offering various guarantees, surrender options and bonus provisions. A case in point are the with-profits insurance policies offered by UK insurers. While these policies have been offered in some form for centuries, in recent years their structure and management have become substantially more involved. The products are particularly complicated due to the wide discretion they afford insurers in determining the bonuses policyholders receive. In this paper, we study the problem of an insurance firm attempting to structure the portfolio underlying its with-profits fund. The resulting optimization problem, a non-linear program with stochastic variables, is presented in detail. Numerical results show how the model can be used to analyze the alternatives available to the insurer, such as different bonus policies and reserving methods.