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The recent financial crisis triggered the greatest recession since the 1930s and had a devastating impact on households’ wealth and on their capacity to reduce their indebtedness. In the aftermath, it became clear that there is significant room for improvement in property risk management. While there has been innovation in the management of corporate finance risk, real estate has lagged behind. Now is the time to expand the range of tools available for hedging households’ risks and, thus, to advance the democratization of finance. Property equity represents the major asset in households’ portfolios in developed and undeveloped countries. The present paper analyzes a set of potential innovations in real estate risk management, such as price level-adjusted mortgages, property derivatives, and home equity value insurance. Financial institutions, households, and governments should work together to improve the performance of the financial instruments available and, thus, to help mitigate the worst impacts of economic cycles.

This study analyses the determinants of the value of a portfolio of real options and explores implications for strategic management. It focuses the analysis on four elements: the number of real options in the portfolio, constraints on the number of options that can be exercised, the volatility of underlying assets, and the correlation between underlying assets. These elements are articulated around a trade-off between growth options and switching options and are applied to different strategic situations of technological, market, and macroeconomic uncertainty.

Portfolios of mortgage loans played an important role in the Great Recession and continue to compose a material part of bank assets. This chapter investigates how cross-sectional dependence in the underlying properties flows through to the loan returns, and thus, the risk of the portfolio. At one extreme, a portfolio of foreclosed mortgage loans becomes a portfolio of real estate whose returns exhibit substantial cross-sectional and spatial dependence. Near the other extreme, almost all loans perform and yield constant returns, which do not correlate with other performing loan returns. This suggests that loan performance effectively censors the random returns of the underlying properties. Following the statistical properties of the correlations among censored variables, the authors build off this foundation and show how the loan return correlations will rise as economic conditions deteriorate and the defaulting loans reveal the underlying housing correlations. In this chapter, the authors (1) adapt tools from spatial statistics to document substantial cross-sectional dependence across house price returns and examine the spatial structure of this dependence, (2) investigate the nonlinear nature of correlations among loan returns as a function of the default rate and the underlying house price correlations, and (3) conduct a simulation exercise using parameters from the empirical data to show the implications for holding a portfolio of mortgages.

Industrial feudalism is a socio-economic formation of advanced capitalist countries in which society becomes stratified into closed, hierarchically-defined social groups. In the writings of Ludwik Krzywicki and Oskar Lange, industrial feudalism is associated with the dominance of monopoly finance capital. The chapter extends this analysis of twenty-first century capitalism in which social groups are differentiated by the kind of property that they own and hence the kind of credit to which they have access to prevent becoming déclassé. However asset inflation then inhibits upward social mobility, confining households to their inherited social class. This inhibits labour mobility. But the availability of credit for the propertied classes also defines attitudes towards state welfare provision.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.