Hybrid performance evaluation of sustainable service and manufacturing supply chain management: An integrated approach of fuzzy dematel and fuzzy inference system

The aim of this paper is to propose a comprehensive framework for simultaneously measuring the performance of sustainable service and manufacturing supply chain management. Application of the proposed approach also results in reduced uncertainty of the performance measurement process caused by qualitative criteria evaluation. The proposed approach consists of two main steps. First, the fuzzy decision‐making trial and evaluation laboratory (DEMATEL) method has been used to determine important criteria by avoiding low influences; and then a Mamdani fuzzy inference system model has been adopted and applied for performance evaluation of sustainable supply chain management (SSCM). This model is employed in order to cope with the vagueness that exists in the SSCM performance investigation due to the vagueness intrinsic in the evaluation of criteria. In the proposed model, human reasoning has been modelled with fuzzy inference rules and has been set in the system, which is an advantage compared with those models in which fuzzy set theory and multicriteria decision‐making models are integrated. The proposed approach has been implemented in the pipe and fitting industry in order to highlight its application in real life. Sensitivity analysis has been carried out to determine the influence of service and manufacturing criteria on SSCM performance. The findings reveal that sustainable manufacturing criteria compared with sustainable service criteria have more effect on the performance of SSCM.     

Portfolio optimization with stochastic dominance constraints

We consider the problem of constructing a portfolio of finitely many assets whose return rates are described by a discrete joint distribution. We propose a new portfolio optimization model involving stochastic dominance constraints on the portfolio return rate. We develop optimality and duality theory for these models. We construct equivalent optimization models with utility functions. Numerical illustration is provided.     

International portfolio selection and efficiency analysis

In the risk-return tradeoff, the traditional mean-variance analysis has been widely used for studies of international portfolio efficiency and diversification. Without prior knowledge about either the parametric structure of assets' return distributions or the form of investors' preference functions, the variance may no longer serve as a suitable risk proxy. This article examines international portfolio efficiency and diversification effects through mean-variance and various distribution-free (or less restrictive) risk-return measures. We show empirically that the mean-variance model is appropriate for large or well-diversified portfolios, but may provide biased results for single assets and less diversified portfolios. While stochastic dominance stands as theoretically the most appropriate method of international portfolio selection and efficiency analysis, the lack of optimal search algorithms reduces its practical usefulness. Very little gain is obtained by using the Gini-mean-difference risk measure as compared to the semivariance measure. The semivariance measure is a powerful and convenient discriminator of risky prospects, while stochastic dominance can serve as a benchmark to justify portfolio efficiency.     

Macroeconomic influences on optimal asset allocation

We develop a tactical asset allocation strategy that incorporates the effects of macroeconomic variables. The joint distribution of financial asset returns and the macroeconomic variables is modelled using a VAR with a multivariate GARCH (M-GARCH) error structure. As a result, the portfolio frontier is time varying and subject to contagion from the macroeconomic variable. Optimal asset allocation requires that this be taken into account. We illustrate how to do this using three risky UK assets and inflation as a macroeconomic factor. Taking account of inflation generates portfolio frontiers that lie closer to the origin and offers investors superior risk-return combinations.     

A credit risk model for large dimensional portfolios with application to economic capital

In this paper we develop a multi-period and multi-state portfolio credit risk model which is applicable to large dimensional portfolios like for example retail and mortgage portfolios. The model includes a methodology for estimation and simulation of systematic transition risk through a model for stochastic migration, a methodology for the modelling of recoveries in the case of stochastic collaterals as well as an approach to dimension reduction of the portfolio. One important application of our model is economic capital (EC) and a concept of EC based on the analogy with classical risk theory is introduced and the questions of allocation as well as risk-adjusted pricing based on the allocation of EC are structured and described. The model is illustrated by an extensive numerical example giving a concretization of the model as well as of several of the concepts introduced.     

Portfolio selection using hierarchical Bayesian analysis and MCMC methods

This paper contributes to portfolio selection methodology using a Bayesian forecast of the distribution of returns by stochastic approximation. New hierarchical priors on the mean vector and covariance matrix of returns are derived and implemented. Comparison’s between this approach and other Bayesian methods are studied with simulations on 25 years of historical data on global stock indices. It is demonstrated that a fully hierarchical Bayes procedure produces promising results warranting more study. We carried out a numerical optimization procedure to maximize expected utility using the MCMC (Monte Carlo Markov Chain) samples from the posterior predictive distribution. This model resulted in an extra 1.5 percentage points per year in additional portfolio performance (on top of the Hierarchical Bayes model to estimate μ and Σ and use the Markowitz model), which is quite a significant empirical result. This approach applies to a large class of utility functions and models for market returns.     

Pricing of index options under a minimal market model with log-normal scaling

Abstract

This paper describes a two-factor model for a diversified market index using the growth optimal portfolio with a stochastic and possibly correlated intrinsic timescale. The index is modelled using a time transformed squared Bessel process with a log-normal scaling factor for the time transformation. A consistent pricing and hedging framework is established by using the benchmark approach. Here the numeraire is taken to be the growth optimal portfolio. Benchmarked traded prices appear as conditional expectations of future benchmarked prices under the real world probability measure. The proposed minimal market model with log-normal scaling produces the type of implied volatility term structures for European call and put options typically observed in real markets. In addition, the prices of binary options and their deviations from corresponding Black–Scholes prices are examined.     

Underwriting profit margin of P/L insurance in the fuzzy-ICAPM

The ICAPM is used to study the underwriting profit margin of the P/L insurance company, including the insurances of automobile damage, automobile liability and fire, in which the parameters are the symmetric or non-symmetric triangular fuzzy numbers. From the ten-year data of a company in Taiwan we determine the lower and upper limits associated with the various α-level of the fuzzy numbers. Our results show that the best-fitting parameters of the model from our data are the asymmetric triangular fuzzy numbers. The skew factors in each insurance are determined, which could be used to perform the forecasting of the underwriting profit margin. Our results show that the systematic risk in the fuzzy environment (with best-fitting value of skew factor) becomes larger than that in the crisp environment. However, the insurance underwriting leverage and insurance financial leverage in the fuzzy environment are smaller than those in the crisp environment. JEL Classification G22 · G32 The author is grateful to the two anonymous reviewers for The Geneva Risk and Insurance Review for their comments on an earlier version of this paper.     

Predicting Long‐Term Financial Returns: VAR versus DSGE Model—A Horse Race

This paper considers a U.S. institutional investor who is implementing a long‐term portfolio allocation using forecasts of financial returns. We compare the predictive performance of two competing macrofinance models—an unrestricted vector autoRegression (VAR) and a fully‐structural dynamic stochastic general equilibrium (DSGE) model—for horizons up to 15 years. Although the performances are similar for short horizons, the DSGE model outperforms the VAR at forecasting financial returns in the long term. This model also generates substantially higher Sharpe ratios. Although it contains fewer unknown parameters, it benefits from economically grounded restrictions that help anchor financial returns in the long term.     

HARA utility maximization in a Markov-switching bond–stock market

We present a flexible multidimensional bond–stock model incorporating regime switching, a stochastic short rate and further stochastic factors, such as stochastic asset covariance. In this framework we consider an investor whose risk preferences are characterized by the hyperbolic absolute risk-aversion utility function and solve the problem of optimizing the expected utility from her terminal wealth. For the optimal portfolio we obtain a constant-proportion portfolio insurance-type strategy with a Markov-switching stochastic multiplier and prove that it assures a lower bound on the terminal wealth. Explicit and easy-to-use verification theorems are proven. Furthermore, we apply the results to a specific model. We estimate the model parameters and test the performance of the derived optimal strategy using real data. The influence of the investor’s risk preferences and the model parameters on the portfolio is studied in detail. A comparison to the results with the power utility function is also provided.     

Dynamics of Private and Public Real Estate Markets

Using five assets (T-bills, bonds, stocks, and both public and private real estate), this study investigates how cointegration of capital markets affects the dynamics of public and private real estate markets. The results show that the price indices of the five assets are nonstationary and cointegrated. Some implications for the long-term equilibrium relationship for portfolio diversification, price discovery and prediction are discussed. In a Granger causality framework, error-correction augmented VAR models (VECM) and unrestricted VAR models are compared with respect to the conclusion regarding the interaction between public and private real estate returns. VECM is also shown to improve the prediction of private real estate returns relative to an unrestricted VAR model. These results raise questions about previous research studies regarding the dynamics between public and private real estate returns. It is shown that the long-term equilibrium relationship establishes a feedback between the two real estate markets, but the private market seems to informationally lead the public one. Possible explanations are also explored.     

Robust portfolio selection under downside risk measures

We investigate a robust version of the portfolio selection problem under a risk measure based on the lower-partial moment (LPM), where uncertainty exists in the underlying distribution. We demonstrate that the problem formulations for robust portfolio selection based on the worst-case LPMs of degree 0, 1 and 2 under various structures of uncertainty can be cast as mathematically tractable optimization problems, such as linear programs, second-order cone programs or semidefinite programs. We perform extensive numerical studies using real market data to reveal important properties of several aspects of robust portfolio selection. We can conclude from our results that robustness does not necessarily imply a conservative policy and is indeed indispensable and valuable in portfolio selection.     

Responses of REITs index and commercial property prices to economic uncertainties: A VAR analysis

We use market participants’ perceived uncertainty to investigate the response of real estate investment trusts index (REITs Index) and commercial property prices to shocks in economic uncertainty. Using US quarterly data and applying a vector autoregression (VAR) model, our results show that an increase in market participants’ perceived uncertainty leads to a significant drop in the REITs Index and commercial property prices. In addition, we show that the REITs Index responds quicker to the uncertainty shocks than the commercial property prices. Our findings provide important implications for investors.     

A Fuzzy Set Approach for Generalized CRR Model: An Empirical Analysis of S&;P 500 Index Options

This paper applies fuzzy set theory to the Cox, Ross and Rubinstein (CRR) model to set up the fuzzy binomial option pricing model (OPM). The model can provide reasonable ranges of option prices, which many investors can use it for arbitrage or hedge. Because of the CRR model can provide only theoretical reference values for a generalized CRR model in this article we use fuzzy volatility and fuzzy riskless interest rate to replace the corresponding crisp values. In the fuzzy binomial OPM, investors can correct their portfolio strategy according to the right and left value of triangular fuzzy number and they can interpret the optimal difference, according to their individual risk preferences. Finally, in this study an empirical analysis of S&P 500 index options is used to find that the fuzzy binomial OPM is much closer to the reality than the generalized CRR model.This project has been supported by NSC 93-2416-H-009-024.JEL Classification:     

Real-world jump-diffusion term structure models

This paper considers interest rate term structure models in a market attracting both continuous and discrete types of uncertainty. The event-driven noise is modelled by a Poisson random measure. Using as numeraire the growth optimal portfolio, interest rate derivatives are priced under the real-world probability measure. In particular, the real-world dynamics of the forward rates are derived and, for specific volatility structures, finite-dimensional Markovian representations are obtained. Furthermore, allowing for a stochastic short rate in a non-Markovian setting, a class of tractable affine term structures is derived where an equivalent risk-neutral probability measure may not exist.     

Multiple Regimes and Volatility Transmission in Securitized Real Estate Markets

We examine the dynamics and transmission of conditional volatilities with multiple structural changes in return volatility using Bai and Perron (2003)’s methodology, across five major securitized real estate markets as well as employing a multivariate regime-dependent asymmetric dynamic covariance methodology (MRDADC) that allows the conditional matrix to be both time- and state-varying. Our results imply that a multiple-regime time varying asymmetric variance and covariance approach is important in modeling real estate securities valuation and selection and portfolio optimization, and is consistent with popular beliefs that market volatility changes over time. Our MRDADC models detect the presence of significant mean-volatility linkages across the five major securitized real estate markets under different volatility regimes and would have implications for global investor in terms of estimating a dynamic risk-minimizing hedge ratio in international portfolio management.     

The real risk in pension forecasting

The purpose of this paper is to shed light on some of the flaws in the forecasting approach undertaken by the pension industry. Specifically, it considers the treatment of inflation and shows that the current modeling framework is too simplistic. I identify the flaws of the existing regulatory framework and provide an alternative full model framework constructed around the three-factor diffusion model recently proposed by the Danish Society of Actuaries. By use of a simulation study I compare the deterministic inflation scheme applied in the industry to a stochastic scheme and show that the real value of the pension saver’s investment portfolio at retirement is highly dependent on the inflation scheme. As the deterministic scheme does not take state variable correlations into account it overestimates the expected portfolio value in real terms compared to the stochastic scheme. Moreover, the deterministic scheme gives rise to a more heavy-tailed distribution implying a misestimation of downside risk and upside potential. Finally, it is shown in a realistic case study that the pension saver’s expected retirement payout profile is heavily affected.     

Small caps in international equity portfolios: the effects of variance risk

We show that predictable covariances between means and variances of stock returns may have a first order effect on portfolio composition. In an international asset menu that includes both European and North American small capitalization equity indices, we find that a three-state, heteroskedastic regime switching VAR model is required to provide a good fit to weekly return data and to accurately predict the dynamics in the joint density of returns. As a result of the non-linear dynamic features revealed by the data, small cap portfolios become riskier in bear markets, i.e., display negative co-skewness with other stock indices. Because of this property, a power utility investor ought to hold a well-diversified portfolio, despite the high risk premium and Sharpe ratios offered by small capitalization stocks. On the contrary, small caps command large optimal weights when the investor ignores variance risk, by incorrectly assuming joint normality of returns.      

Portfolio optimization under model uncertainty and BSDE games

We consider robust optimal portfolio problems for markets modeled by (possibly non-Markovian) Itô–Lévy processes. Mathematically, the situation can be described as a stochastic differential game, where one of the players (the agent) is trying to find the portfolio that maximizes the utility of her terminal wealth, while the other player (“the market”) is controlling some of the unknown parameters of the market (e.g., the underlying probability measure, representing a model uncertainty problem) and is trying to minimize this maximal utility of the agent. This leads to a worst case scenario control problem for the agent. In the Markovian case, such problems can be studied using the Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation, but these methods do not work in the non-Markovian case. We approach the problem by transforming it into a stochastic differential game for backward stochastic differential equations (a BSDE game). Using comparison theorems for BSDEs with jumps we arrive at criteria for the solution of such games in the form of a kind of non-Markovian analogue of the HJBI equation. The results are illustrated by examples.