On returns policies with exogenous price

We contribute to current research on single-period returns policies by making a clear distinction between models in which transfer price is exogenous and models in which one dominant party unilaterally declares a price. We compare the equilibrium contracts that result from these two approaches and derive conditions for the equilibrium returns policy to be Pareto-efficient when transfer price is exogenous. Our main result is distribution free, but we make some interesting observations on channel performance when demand is uniformly distributed.     

Modeling financial asset returns with shot noise processes

We introduce a new class of continuous time processes for modeling the rate of returns of financial assets. The statistical characterization is based on the so-called shot noise processes. The probabilistic structure of the shot noise process provides a very realistic framework for asset returns modeling of the stock price processes. Our class of processes exhibits the natural phenomena well known in empirical financial studies:

1. (a) fat-tail distribution function for the asset returns,

2. (b) dependence of the returns,

3. (c) nonstationarity in time.

Financial asset returns in new emerging markets such as those of Eastern European countries exhibit a highly volatile behavior. Statistical investigations of the unconditional distribution of returns of stocks, commodities, exchange rates, etc., show extremely heavy tails and steep peaks around the expectation. We use a class of shot noise processes with Poissonian times and Brownian magnitudes for modeling this phenomenon.     

Optimal ordering decisions with returns and excess inventory

Recycling is one of the most efficient ways to protect our environment. In recent years, inventory management with product returns has drawn attention from many researchers. This study considers a two-echelon inventory system with returns and shortage backordering, and its objective is to minimize the total cost of the system. In addition, we examine a situation when stock increases will result in more consumption. Numerical examples are provided to illustrate the theory.     

Analysis of the impact of price-sensitivity factors on the returns policy in coordinating supply chain

This paper analyzes the impact of price-sensitivity factors on characteristics of returns policy contracts in a single-period product supply chain. The contract considers stochastic and price-dependent demand. We present an analytical model and then use numerical methods with the Stackelberg game to identify the contract properties. We numerically show that a returns policy indeed improves supply chain performance. However, the benefits earned from the returns policy, under price-sensitive and variable demand, are different for different supply chain partners. First, when price-sensitivity is high, profit of the manufacturer decreases with increase in demand variability. Second, when price-sensitivity is sufficiently high and demand variability increases, the manufacturer has to surrender part of the profits to the retailer, in order to continue sales. However, even after surrendering part of the profits to the retailer, the manufacturer still earns profits that are higher than those available in a wholesale price contract. Last, from the perspective of division of channel profits, the retailer is always worse off in case of returns policies than in a wholesale price contract. Therefore, to apply this form of incentive in practice, managements should consider the impact of price-sensitivity on the returns policy and its performance.     

Mixed effect models for absolute log returns of ultra high frequency data

Considering absolute log returns as a proxy for stochastic volatility, the influence of explanatory variables on absolute log returns of ultra high frequency data is analysed. The irregular time structure and time dependency of the data is captured by utilizing a continuous time ARMA(p,q) process. In particular, we propose a mixed effect model class for the absolute log returns. Explanatory variable information is used to model the fixed effects, whereas the error is decomposed in a non‐negative Lévy driven continuous time ARMA(p,q) process and a market microstructure noise component. The parameters are estimated in a state space approach. In a small simulation study the performance of the estimators is investigated. We apply our model to IBM trade data and quantify the influence of bid‐ask spread and duration on a daily basis. To verify the correlation in irregularly spaced data we use the variogram, known from spatial statistics. Copyright © 2006 John Wiley & Sons, Ltd.     

Measuring returns to scale in DEA models when the firm is regulated

In the standard framework of data envelopment analysis (DEA) models, the returns to scale are fully characterized using the multiplier on the convexity constraint of inefficient decision making units (DMU) using the projection of the input–output vector on the frontier. In this note, we investigate how the returns to scale measurements in DEA models are affected by the presence of regulatory constraints. These additional constraints change the role played by the convexity constraint. In order to avoid biased estimation of the returns to scale, we show that the interaction between the regulatory and the convexity constraints has to be taken into account.     

Moment condition failure in stock returns: UK evidence

We examine the issue of moments existence in the UK stock market. It is found that the second moment of stock returns is finite, and therefore, the infinite variance stable distribution is ruled out as a candidate for modelling stock returns. In contrast with the US evidence, we cannot rule out the possibility that the fourth moment is finite.     

Measurement of returns to scale using non-radial DEA models

There are some specific features of the non-radial data envelopment analysis (DEA) models which cause some problems for the returns to scale measurement. In the scientific literature on DEA, some methods were suggested to deal with the returns to scale measurement in the non-radial DEA models. These methods are based on using Strong Complementary Slackness Conditions from optimization theory. However, our investigation and computational experiments show that such methods increase computational complexity significantly and may generate as optimal, solutions contradicting optimization theory. In this paper, we propose and substantiate a direct method for the returns to scale measurement in the non-radial DEA models. Our computational experiments documented that the proposed method works reliably and efficiently on the real-life data sets.     

Mean–variance analysis of a single supplier and retailer supply chain under a returns policy

In the literature, most of the supply chain coordinating policies target at improving the supply chain’s efficiency in terms of expected cost reduction or expected profit improvement. However, optimizing the expected performance alone cannot guarantee that the realized performance measure will fall within a small neighborhood of its expected value when the corresponding variance is high. Moreover, it ignores the risk aversion of supply chain members which may affect the achievability of channel coordination. As a result, we carry out in this paper a mean–variance (MV) analysis of supply chains under a returns policy. We first propose an MV formulation for a single supplier single retailer supply chain with a newsvendor type of product. The objective of each supply chain decision maker is to maximize the expected profit such that the standard deviation of profit is under the decision maker’s control. We study both the cases with centralized and decentralized supply chains. We illustrate how a returns policy can be applied for managing the supply chains to address the issues such as channel coordination and risk control. Extensive numerical studies are conducted and managerial findings are proposed.     

The impacts of the full returns policy on a supply chain with information asymmetry

In contrast to the existing return policies literature assuming that information is symmetrical between the manufacturer and the retailer, we study the full returns policy’s impact on supply chains with information asymmetry. We first study the case that the base level of the demand follows a discrete distribution with two states. We find that the retailer benefits from the full returns policy in all circumstances, while the manufacturer and the supply chain are better off under some conditions. We then consider the situation in which the base level of the demand is a type of AR(1) process.     

CED model for asset returns and fractal market hypothesis

A new general model for asset returns is studied in the framework of the Fractal Market Hypothesis (FMH). To accommodate markets with arbitrage opportunities, it concerns capital market systems in which the Conditionally Exponential Dependence (CED) property can be attached to each investor on the market. Employing the limit theorem for the CED systems, the universal characteristics for the distribution of asset returns are derived. This explains the special role of the Weibull distribution in modeling of global asset returns for market with no arbitrage and the two-power laws property of the density of global returns, evident in the empirical data. Finally, the link with two-parameter Pareto distributions is established.     

Measurement of returns to scale using a non-radial DEA model: A range-adjusted measure approach

This research theoretically explores the measurement of returns to scale (RTS), using a non-radial DEA (data envelopment analysis) model. A range-adjusted measure (RAM) is used as a representative of such non-radial models. Historically, a type of RTS has been discussed within an analytical framework of radial models. The radial-based RTS measurement is replaced by the non-radial RAM/RTS measurement in this study. When discussing the non-radial RAM/RTS measurement, this study finds a problem of multiple projections that cannot be found in the radial measurement. In this research, a new linear programming approach is proposed to identify all efficient DMUs (decision making units) on a reference set. The important feature of the proposed approach is that it can deal with a simultaneous occurrence of (a) multiple reference sets, (b) multiple supporting hyperplanes and (c) multiple projections. All of the three difficulties are handled by the proposed RAM/RTS measurement. In particular, we discuss both when the three different types of multiple solutions occur on the RAM/RTS measurement and how to deal with such difficulties. Our research results make it possible to measure not only the type of RTS but also the magnitude of RTS in the RAM measurement.     

CAPM with fuzzy returns and hypothesis testing

Over the last four decades, several estimation issues of the beta have been discussed extensively in many articles. An emerging consensus is that the betas are time-dependent and their estimates are impacted by the return interval and the length of the estimation period. These findings lead to the prominence of the practical implementation of the Capital Asset Pricing Model. Our goal in this paper is two-fold. After studying the impact of the return interval on the beta estimates, we analyze the sample size effects on the preceding estimation. Working in the framework of fuzzy set theory, we first associate the returns based on closing prices with the intraperiod volatility for the representation by the means of a fuzzy random variable in order to incorporate the effect of the interval period over which the returns are measured in the analysis. Next, we use these fuzzy returns to estimate the beta via fuzzy least square method in order to deal efficiently with outliers in returns, often caused by structural breaks and regime switches in the asset prices. A bootstrap test is carried out to investigate whether there is a linear relationship between the market portfolio fuzzy return and the given asset fuzzy return. Finally, the empirical results on French stocks suggest that our beta estimates seem to be more stable than the ordinary least square (OLS) estimates when the return intervals and the sample size change.     

Weekday dependence of German stock market returns

The so‐called ‘Monday effect’ has been found for various stock markets of the world. The empirical finding that Monday returns are significantly smaller than returns measured for the remaining days of the week calls the efficiency hypothesis for pricing processes operating on stock markets into question. Investigating an index series measured at the Frankfurt stock exchange the paper compares estimation results of parametric and non‐parametric autoregressive models with respect to possible weekday dependence of return data. Allowing for heteroskedastic error distributions the wild bootstrap is used to infer against time‐varying means and correlation of return data in parametric models and to obtain confidence bands for non‐parametric estimates. It is shown that time dependence is an important feature describing the dynamics of German stock market returns in the period 1960–1979. Within two subsamples obtained from the period 1980–1997 the evidence in favour of such effects is mitigated substantially. Copyright © 2000 John Wiley & Sons, Ltd.     

On a basic definition of returns to scale

A concept that has sparked considerable interest in DEA is that of returns to scale (RTS). One of the basic and useful definitions of RTS in DEA models is based upon the PPS. In this work, a discussion about the PPS-based definition of RTS is given, leading to a modified definition of RTS which is suitable in the presence of multiple supporting hyperplanes passing through the unit under assessment. The definition provided is a simplification of a definition given by Tone.     

An additive model approach for estimating returns to scale in imprecise data envelopment analysis

In this paper, additive model is used to provide an alternative approach for estimating returns to scale in data envelopment analysis. The proposed model is developed in both stochastic and fuzzy data envelopment analysis. Deterministic (crisp) equivalents are obtained which correspond to the stochastic and fuzzy models. Numerical examples are, also, used to illustrate the proposed approaches.     

Asymmetric extreme interdependence in emerging equity markets

We assess the extent of integration between stock markets during stressful periods using the concept of copulas. Our methodology consists of fitting copulas to simultaneous exceedances of high thresholds, and computing copula‐based measures of interdependence and contagion. Using 21 pairs of emerging stock markets daily returns, we investigate if dependence increases with crisis, and analyse the chances of both markets crashing together. Dependence at joint positive and negative extreme returns levels may differ. This type of asymmetry is captured by the upper and lower tail dependence coefficients. Propagation of crisis may be faster in one direction, and this feature is captured by asymmetric copulas. Copyright © 2005 John Wiley & Sons, Ltd.     

Randomness confidence bands of fractal scaling exponents for financial price returns

The weak-form of the efficient market hypothesis (EMH) establishes that price returns behave as a pure random process and so their outcomes cannot be forecasted. The detrended fluctuation analysis (DFA) has been widely used to test the weak-form of the EMH by showing that time series of price returns are serially uncorrelated. In this case, the DFA scaling exponent exhibits deviations from the theoretical value of 0.5. This work considers the test of the EMH for DFA implementation on a sliding window, which is an approach that is intended to monitor the evolution of markets. Under these conditions, the scaling exponent exhibits important variations over the scrutinized period that can offer valuable insights in the behavior of the market provided the estimated scaling value is kept within strict statistical tests to verify the presence or not of serial correlations in the price returns. In this work, the statistical tests are based on comparing the estimated scaling exponent with the values obtained from pure Gaussian sequences with the length of the real time series. In this way, the presence of serial correlations can be guaranteed only in terms of the confidence bands of a pure Gaussian process. The crude oil (WTI) and the USA stock (DJIA) markets are used to illustrate the methodology.     

DEA congestion and returns to scale under an occurrence of multiple optimal projections

Economic implications of congestion have been recently discussed in many DEA (data envelopment analysis) studies. In addition, several previous research efforts have explored a theoretical linkage between returns to scale (RTS) and the concept of congestion, because the two economic concepts are closely connected to each other. Tone and Sahoo [Tone, K., Sahoo, B.K., 2004. Degree of scale economies and congestion: A unified DEA approach. European Journal of Operational Research 158, 755–772] have published the theoretical linkage in this journal. All of the previous studies, including their research (2004), assume a unique optimal solution in the investigation on DEA-based congestion. When multiple solutions occur in DEA-based congestion measurement, the economic implications of congestion obtained from the previous research are all problematic from both theoretical and practical perspectives. To deal with the issue, this study explores how to deal with the occurrence of multiple solutions in the DEA-based congestion measurement. This study proposes a new approach for the congestion measurement and theoretically compares the proposed approach with Tone and Sahoo (2004).     

Multifractal structure in Latin-American market indices

We study the multifractal nature of daily price and volatility returns of Latin-American stock markets employing the multifractal detrended fluctuation analysis. Comparing with the results obtained for a developed country (US) we conclude that the multifractality degree is higher for emerging markets. Moreover, we propose a stock market inefficiency ranking by considering the multifractality degree as a measure of inefficiency. Finally, we analyze the sources of multifractality quantifying the contributions of two factors, the long-range correlations of the time series and the broad fat-tail distributions. We find that the multifractal structure of Latin-American market indices can be mainly attributed to the latter.