equilibrium in an ambiguity averse mean variance investors market abstract in a financial market composed of n risky assets and a riskless asset where short sales are allowed and mean variance investors can be ambiguity averse i e diffident about mean return estimates where confidence is represented using ellipsoidal uncertainty sets we derive a closed form portfolio rule based on a worst case max min criterion then in a market where all investors are ambiguity averse mean variance investors with access to given mean return and variance covariance estimates we investigate conditions regarding the existence of an equilibrium price system and give an explicit formula for the equilibrium prices in addition to the usual equilibrium properties that continue to hold in our case we show that the diffidence of investors in a homogeneously diffident with bounded diffidence mean variance investors market has a deflationary effect on equilibrium prices with respect to a pure mean variance investors market in equilibrium deflationary pressure on prices may also occur if one of the investors in an ambiguity neutral market with no initial short position decides to adopt an ambiguity averse attitude we also establish a capm like property that reduces to the classical capm in case all investors are ambiguity neutral

a general mean variance approximation to expected utility for short holding periods the mean variance model is precisely consistent with the expected utility hypothesis only in the special cases of normally distributed security returns or quadratic utility functions there is little evidence however that security returns follow normal distributions see 13 for references and quadratic preferences can be shown to generate implausible results exhibiting increasing absolute risk aversion in the pratt ll arrow 1 2 sense and displaying negative marginal utility after some finite wealth level in addition hakansson 4 has shown that single period mean variance efficient portfolios can have disastrous consequences over time even when return distributions are stationary such criticisms of the mean variance approach within the von neumann morgenstern framework have prompted several writers to suggest that investors maximize the expected value of utility functions with more realistic properties while others have criticized the single period focus of the model one popular alternative utility function is the logarithmic function which exhibits decreasing absolute risk aversion and conveniently leads to myopic decision processes through time i e investors treat each period as if it were the last basing investment decisions on that period s wealth and return distributions only 8 4 other utility functions with constant relative risk aversion such as the power function also imply myopic decision rules within a multiperiod setting

multi objective stochastic programming for portfolio selection abstract generally in the portfolio selection problem the decision maker dm considers simultaneously conflicting objectives such as rate of return liquidity and risk multi objective programming techniques such as goal programming gp and compromise programming cp are used to choose the portfolio best satisfying the dm s aspirations and preferences in this article we assume that the parameters associated with the objectives are random and normally distributed we propose a chance constrained compromise programming model cccp as a deterministic transformation to multi objective stochastic programming portfolio model cccp is based on cp and chance constrained programming ccp models the proposed program is illustrated by means of a portfolio selection problem from the tunisian stock exchange market

international portfolio choice and political instability risk a multi objective approach abstract the benefits derived from international portfolio diversification into foreign nations including the less developed countries are well documented yet this practice is discouraged due to market imperfections such as political instability in practice nations may be differentiated further by many aspects such as border controls or political and social trends which constrain private transactions and financial decisions this paper attempts to examine 1 whether the home asset bias in a portfolio holding is associated with higher political instability risk and 2 to what extent international diversification among stocks in the presence of such risk outperforms domestic stock portfolios using alternative instability risk proxies in the context of a discrete time version of mean variance framework we corroborate the impact of this type of risk on international portfolio investment decisions

stochastic modelling of renewable energy sources from operators point of view a survey abstract high penetration of renewable energy sources especially weather dependent sources has increased the power systems uncertainties for any analysis in power systems such as planning and operation it is essential to confront the stochastic nature of these sources in order to get much more precise results since operators need proper strategies and methods to decline negative effects of the stochastic behaviour of renewable power generators such as total operation cost growth this paper provides a review of different state of the art approaches from the operator s viewpoint for handling the stochastic behaviour of renewable sources hence in this paper three different strategies are categorized for stochastic analysis of these sources the first strategy is mathematical modelling including stochastic dependency and independency multi dimensional dependence forecast and scenarios afterwards demand side management which is one of the other approaches for dealing with these uncertainties is investigated and different demand response programs and some methods to model them are presented finally the effect of different electricity market schemes and relevant optimization methods to mitigate the variations of renewable energy sources are discussed the study demonstrates that an operator should choose one or a combination of these three approaches based on its requirements

financial portfolio management through the goal programming model current state of the art abstract since markowitz 1952 formulated the portfolio selection problem many researchers have developed models aggregating simultaneously several conflicting attributes such as the return on investment risk and liquidity the portfolio manager generally seeks the best combination of stocks assets that meets his her investment objectives the goal programming gp model is widely applied to finance and portfolio management the aim of this paper is to present the different variants of the gp model that have been applied to the financial portfolio selection problem from the 1970s to nowadays

demand response with locational dynamic pricing to support the integration of renewables abstract electricity production from centralised and decentralised renewable energy resources in europe is gaining significance resulting in operational challenges in the electricity system although these challenges add to the locational and time dependency of the underlying cost of operating the system this variability in time and location is not reflected in residential tariff schemes consequently residential users are not incentivised to react to varying system conditions and to help the integration of renewable energy resources therefore this paper provides a theoretical framework for designing a locational dynamic pricing scheme this can be used to assess existing tariff structures for consumption and injection and can serve as a theoretical background for developing new tariff schemes starting from the underlying costs this paper shows that the potential for locational dynamic pricing depends on the locational and time dependency of its cost drivers when converting costs into tariffs the tariff design should be determined this includes the advance notice of sending tariffs to users and the length of price blocks and price patterns this tariff design should find a balance between tariff principles related to costs practicality and social acceptability on the one hand and the resulting demand response incentive on the other

comonotonic approximation to periodic investment problems under stochastic drift abstract we investigate periodic investment problems under a black scholes market with stochastic drift the decision maker invests a series of positive amounts at finitely predetermined time spots to maximize the expected terminal wealth while controlling its downside risk as measured by the condition value at risk cvar it turns out that the increment for unit wealth on the whole path can be divided into two parts the increment corresponding to the stochastic drift and that corresponding to the brownian motion a comonotonic approximation is proposed for the second part and an upper bound is provided for the cvar of the first part which construct together a closed form approximation of the terminal wealth under the risk measure of cvar we further decompose the problem into a sequence of sub problems whose optimal solutions are explicit and follow fractional kelly strategy numerical and empirical results illustrate the performance of our methodology

risk neutral and risk averse approaches to multistage renewable investment planning under uncertainty abstract strategies for investing in renewable energy projects present high risks associated with generation and price volatility and dynamics existing approaches for determining optimal strategies are based on real options theory that often simplify the uncertainty process or on stochastic programming approaches that simplify the dynamic aspects in this paper we bridge the gap between these approaches by developing a multistage stochastic programming approach that includes real options such as postponing hedging with fixed forward contracts and combination with other sources the proposed model is solved by a procedure based on the stochastic dual dynamic programming sddp method the framework is extended to the risk averse setting a specific case study in investment in hydro and wind projects in the brazilian market is used to illustrate that the investment strategies generated by the proposed approach are efficient

social protection food security and asset formation summary the last two decades have seen a rapid rise in social protection programs and studies that assess their impacts on a large number of domains we construct a new database of studies of these programs that report impacts on food security outcomes and asset formation our meta analysis finds that social protection programs improve both the quantity and quality of food consumed by beneficiaries the magnitudes of these effect sizes are meaningful the average social protection program increases the value of food consumed expenditure by 13 and caloric acquisition by 8 food expenditure rises faster than caloric acquisition because households use transfers to improve the quality of their diet most notably increasing their consumption of calories from animal source foods since the consumption of animal source foods in these populations is low and because there are significant nutritional benefits to increasing the consumption of these this is a positive outcome our meta analysis also finds that social protection programs lead to increased asset holdings as measured by livestock non farm productive assets farm productive assets and savings there is no impact on land holdings though the number of studies that assess these is small

a minimax portfolio selection strategy with equilibrium a new minimax model on optimal portfolio selection with uncertainty of both randomness and estimation in inputs is established and the corresponding optimal portfolio is derived analytically based on this result a sufficient condition for the existence and uniqueness of a nonnegative equilibrium price system under which the total demand and supply of each asset are equal is provided and an explicit formula for such a price system is obtained furthermore some properties of the equilibrium are discussed

portfolio and contract design for demand response resources abstract electricity generation from renewable energy sources has seen significant growth in recent years to improve grid integration of these volatile energy resources demand side flexibility can be operationalized through intelligent scheduling of flexible electrical loads prior research has mainly focused on determining dispatch schedules for exogenously given portfolios of flexible electricity demand we expand on this literature by considering both contracting and dispatch of flexible loads to this end we formulate a demand aggregator s problem of designing and dispatching a portfolio of supply volatile renewable and conventional generators and demand inflexible base shiftable and curtailable load assets as a stochastic program to account for different contracting regimes we consider three market scenarios 1 a liquid flexibility market where the energy retailer is a price taker 2 an opaque market where non strategic customers will accept any individually rational contract offer and 3 a bi level model with strategic customers optimally self selecting into available contracts in the last case we also explore the effect of price discrimination versus uniform pricing we evaluate these models using empirical load and generation data contrary to conventional wisdom we find that flexibility contracting is not monotone increasing in renewable generation capacities our research also suggests that curtailable load is a closer substitute to supply flexibility than shiftable load finally we find that contracting of curtailing flexibility will be harder to implement if the aggregator has limited market power

poverty reduction during 1990 2013 did millennium development goals adoption and state capacity matter abstract while poverty reduction remains central in the post 2015 agenda its determinants remain debated in the literature especially the role of structural conditions related to governance this paper provides an assessment of two key dimensions the global adoption of mdgs and state capacity we do so by studying whether they facilitated convergence in income poverty measures using cross section and panel methods with data on 89 developing economies for the period 1990 2013 we find that poverty headcount and gap measures tended to decrease faster in countries with initially higher income poverty such convergence accelerated after 2000 suggesting that mdgs adoption was instrumental to poverty reduction however this still leaves unexplained substantial variation in poverty reduction performance across countries such variation is explained by state capacity countries with greater ability to administer their territories in 1990 experienced faster income poverty reduction and were more likely to have achieved the mdg target this result is insensitive to robust regression methods and to a large set of controls initial level of income dependence on natural resources education and health inputs dependence on foreign aid ethnic fractionalization regional effects and a set of governance variables as good governance and effective institutions are included in the sustainable development goals this result provides empirical justification for this move suggesting that more effective states could be crucial to sustain the development progress achieved so far

normality solvency and portfolio choice this paper examines whether investors with power utility functions choose mean vari ance mv efficient portfolios when returns are approximately normally distributed and there is borrowing or lending at a riskless interest rate the results show that the unlevered portfolios of power utility investors plot very closely to the mv efficient frontier how ever there are marked differences in the mix of risky assets regardless of whether the portfolios are highly concentrated or widely diversified such differences allow power investors to remain solvent even when they lever their optimal portfolios to a greater ex tent than less risk averse mv investors who risk bankruptcy it is concluded that the investment policies of power utility and mv investors with similar risk aversion measures are not as similar as is commonly believed this is particularly true for high power inves tors unless explicit solvency constraints are imposed on the mv problem and for low power investors when quadratic utility approximations are made to the power utility func tions these differences in the investment policies of power utility and mv investors lead us to question the widely accepted assertion that the assumptions of homogeneous beliefs normality a riskless asset and risk averse investors imply the simple mv capm where all investors including power utility investors hold combinations ofthe market portfolio and the riskless asset

how can safety nets contribute to economic growth the paper provides an up to date and selective review of the literature on how social safety nets contribute to growth the evidence is carefully chosen to show how safety nets have the potential to overcome constraints on growth linked to market failures and is organized into 4 distinct pathways i encouraging asset accumulation by changing incentives and by addressing imperfections in financial markets caused by constraints in obtaining credit and from information asymmetries overcoming such failures helps households to invest into their human capital or productive assets ii failures in insurance markets especially in low income setting safety nets are assisting in managing risk both ex post and ex ante iii safety nets are overcoming failure to create assets and other local economy complementary factors to household level investments iv safety nets are shown to relax political constraints on policy safety nets have a dual objective of directly alleviating poverty through transfers to the poor and of triggering higher growth for the poor however the trade off between the dual objectives of equity and growth is not eliminated by the potential for productive safety nets this remains critical for designing social policies

do social protection transfers reduce poverty and vulnerability to poverty in pakistan household level evidence from punjab empirical studies in different developing countries have investigated the impact of social protection sp on ex post poverty however few studies analysed the impact of sp on ex ante vulnerability to poverty vtp this paper contributes to the literature by evaluating the impact of sp on vulnerability to poverty vtp and poverty rates among households in the punjab province of pakistan a hierarchical modelling approach is used to analyse the multiple indicator cluster survey mics 2011 data of about 90 000 households matching methods and simultaneous endogenous switching regression are used to control for potential selection bias and estimate average treatment effects our results show that sp has a positive impact in reducing household poverty and vtp but this impact is mainly driven by a short term flood relief cash transfer programme pension of government employees and households regular purchase from utility store network

on the sensitivity of mean variance efficient portfolios to changes in asset means some analytical and computational results tbis paper investigates the sensitivity of meanvariance mv efficientportfolios to changes in the means of individual assets when only a budget constraint is imposed on the investment problem the analytical results indicate that an mv efficient portfolio s weights mean and variance can be extremely sensitive to cbanges in asset means when nonnegativity constraints are also imposed on the problem the computational results confirm that a positively weigbted mv efficient portfolio s weigbts are extremely sensitive to cbanges in asset means but the portfolio s returns are not a surprisingly small increase in the mean of just one asset drives balf the securities from the portfolio yet the portfolio s expected return and standard deviation are virtually uncbanged this paper supersedes should investors hold well diversified portfolios financial support from the natural science and engineering research council of canada is gratefully acknowledged we also acknowledge the most capable assistance of ruth cornale simon ng jean marc potier and frederick shen the paper was presented at simon fraser university and the western finance association meetings in san diego we thank the participants especially stephen brown john herzog john heaney ray koopman and bruce lehmann and the editors jon ingersoll and chester spatt for

bringing wind energy to market wind energy is a rapidly growing source of renewable energy generation however the current extra market approach to its assimilation into the electric grid will not scale at deep penetration levels in this paper we investigate how an independent wind power producer might optimally offer its variable power into a competitive electricity market for energy starting with a stochastic model for wind power production and a model for a perfectly competitive two settlement market we derive explicit formulae for optimal contract offerings and the corresponding optimal expected profit as wind is an inherently variable source of energy we explore the sensitivity of optimal expected profit to uncertainty in the underlying wind process we also examine the role of forecast information and recourse markets in this setting we quantify the role of reserves in increasing reliability of offered contracts and obtain analytical expressions for marginal profits resulting from investments in improved forecasting and local auxiliary generation the formulae make explicit the relationship between price signals and the value of such firming strategies

benefits and challenges of electrical demand response a critical review advances in it control and forecasting capabilities have made demand response a viable and potentially attractive option to increase power system flexibility this paper presents a critical review of the literature in the field of demand response providing an overview of the benefits and challenges of demand response these benefits include the ability to balance fluctuations in renewable generation and consequently facilitate higher penetrations of renewable resources on the power system an increase in economic efficiency through the implementation of real time pricing and a reduction in generation capacity requirements nevertheless demand response is not without its challenges the key challenges for demand response centre around establishing reliable control strategies and market frameworks so that the demand response resource can be used optimally one of the greatest challenges for demand response is the lack of experience and the consequent need to employ extensive assumptions when modelling and evaluating this resource this paper concludes with an examination of these assumptions which range from assuming a fixed linear price demand relationship for price responsive demand to modelling the highly diverse distributed and uncertain demand response resource as a single centralised negative generator adopting fixed characteristics and constraints

evaluating the impact of inequality constraints and parameter uncertainty on optimal portfolio choice we present new analytical results for the impact of portfolio weight constraints on an investor s optimal portfolio when parameter uncertainty is taken into account while it is well known that parameter uncertainty and imposing weight constraints results in reduced certainty equivalent returns in the general case there are no analytical results in a special case commonly used in the funds management literature we derive analytical expression for the certainty equivalent loss that does not depend on the risk aversion parameter we illustrate our theoretical results using hedge fund data from the perspective of a fund of fund manager our contribution is to formalize the framework to investigate this problem as well as providing tractable analytical solutions that can be implemented using either simulated or asset manager returns

mean variance instability portfolio analysis a case of taiwan s stock market this paper applies talpaz harpaz and penson s thp talpaz h a harpaz j b penson jr 1983 risk and spectral instability in portfolio analysis eur j oper res 14 262 269 mean variance instability portfolio selection model to eight selected taiwan stocks during 1980 89 to demonstrate how instability preference affects the traditional mean variance frontier in contrast to thp s finding the empirical results show that taiwan s high frequency stocks have high not low variance this indicates that taiwan investors unlike u s investors prefer to speculate in high variance stocks the empirical results also show that short selling may increase the risk of the portfolio when the investor is instability preferred

a half century of returns on levered and unlevered portfolios of stocks bonds and bills with and without small stocks in two earlier papers grauer and hakansson 1982 grauer and hakansson 1984 we applied the multiperiod portfolio theory of mossin 1968 hakansson 1971 1974 leland 1972 ross 1974 and huberman and ross 1983 to the construction and rebalancing of portfolios composed of u s stocks corporate bonds government bonds and a risk free asset borrowing was ruled out in the first article while margin purchases were permitted in the second the probability distributions used were naively estimated from past realized returns in ibbotson this paper applies multiperiod portfolio theory to the construction and rebalancing of portfolios composed of u s stocks corporate bonds government bonds and a risk free asset with small stocks included as a separate investment vehicle probability assessments are based on the past joint empirical distribution our principal findings are 1 small stocks while totally ignored at times entered even the most riskaverse portfolios most of the time and 2 small stocks when chosen tended to replace common stocks except in the 1970s and early 1980s when they were primarily held in lieu of the risk free asset presented at the australian graduate school of management and at macquarie university both in sydney southern methodist university duke university the university of southern california the london graduate school of business and the annual meeting of the western financo association in scottsdale arizona the authors thank the participants of these seminars especially michael brennan and ehud ronn for helpful comments financial support from the social sciences and humanities research council of canada and the financial research foundation of canada is gratefully acknowledged the authors also wish to thank changwoo lee for research assistance and are greatly indebted to frederick shen for computational assistance

portfolio optimization under a minimax rule this paper provides a new portfolio selection rule the objective is to minimize the maximum individual risk and we use an l function as the risk measure we provide an explicit analytical solution for the model and are thus able to plot the entire efficient frontier our selection rule is very conservative one of the features of the solution is that it does not explicitly involve the covariance of the asset returns

socially responsible investment a multicriteria approach to portfolio selection combining ethical and financial objectives abstract in a context of socially responsible investment sri this paper deals with portfolio selection for investors interested in ethical policies in the opportunity set there are ethical assets and other assets which are not characterized as ethical two goals are considered the traditional financial goal in the classical utility theory under uncertainty and an ethical goal in the same utility framework a new financial ethical bi criteria model is proposed with absolute risk aversion coefficients and targets depending on the investor s ethical profile this approach is relevant as an increasing number of mutual funds are becoming interested in sri strategies from the proposed model an actual case on green investment is developed concerning this case without generalizing to other contexts an analysis of the numerical results shows that efficient portfolios obtained by the traditional e v model outperform the strong green portfolios in terms of expected return and risk but this does not significantly occur with weak green investment

is being flexible advantageous for demands this paper analyzes the impacts of flexible demands on day ahead market outcomes in a system with significant wind power production we use a two stage stochastic market clearing model where the first stage represents the day ahead market and the second stage represents the real time operation on one hand flexibility of demands is beneficial to the system as a whole since such flexibility reduces the operation cost but on the other hand shifts in demands from peak periods to off peak periods may influence prices in such a way that demands may not be willing to provide flexibility specifically we investigate the impacts of different degree of demand flexibility on day ahead prices a number of scenarios modeling the uncertainty associated with wind production at the operation stage and nonconvexities due to start up costs of generators and their minimum power outputs are taken into account

strategic generation investment under uncertainty via benders decomposition we address the generation investment problem faced by a strategic power producer and consider a detailed description of the uncertain parameters involved namely rival producer investment and market offering and demand growth to identify optimal investment decisions we consider a target year and propose a bilevel model whose upper level problem determines investment and offering decisions to maximize expected profit and whose many lower level problems represent market clearing conditions per demand block and scenario since the producer total expected profit is sufficiently convex with respect to investment decisions a benders decomposition approach is proposed that results in a tractable formulation even if hundred of scenarios are used to describe uncertain parameters extensive numerical simulations based on realistic case studies show the good performance of the proposed decomposition approach

three or two stage stochastic market clearing algorithm as the power industry considers moving from a deterministic paradigm to a stochastic one to clear the day ahead market in systems with significant stochastic production the following question arises how should a stochastic clearing algorithm be formulated our analyses indicate that a three stage stochastic approach is clearly superior to a two stage one to show this we propose a model that includes three stages the first one represents the day ahead market the second stage the intraday market and the third one the real time operation the objective is to clear the day ahead market but with a prognosis of the future the intraday market and the real time operation to assess the impact of the intraday market on the day ahead outcomes we compare the results obtained from the proposed model with those of a simpler but more common two stage model which represents the day ahead market and the real time operation

collaborative production planning of supply chain under price and demand uncertainty abstract this research is motivated by an automobile manufacturing supply chain network it involves a multi echelon production system with material supply component fabrication manufacturing and final product distribution activities we address the production planning issue by considering bill of materials and the trade offs between inventories production costs and customer service level due to its complexity an integrated solution framework which combines scatter evolutionary algorithm fuzzy programming and stochastic chance constrained programming are combined to jointly take up the issue we conduct a computational study to evaluate the model numerical results using the proposed algorithm confirm the advantage of the integrated planning approach compared with other solution methodologies the supply chain profits from the proposed approach consistently outperform in some cases up to 13 better the impacts of uncertainty in demand material price and other parameters on the performance of the supply chain are studied through sensitivity analysis we found the proposed model is effective in developing robust production plans under various market conditions

stochastic goal programming a mean variance approach abstract we propose a stochastic goal programming gp model leading to a structure of mean variance minimisation the solution to the stochastic problem is obtained from a linkage between the standard expected utility theory and a strictly linear weighted gp model under uncertainty the approach essentially consists in specifying the expected utility equation corresponding to every goal arrow s absolute risk aversion coefficients play their role in the calculation process once the model is defined and justified an illustrative example is developed

multiple criteria decision making combined with finance a categorized bibliographic study abstract this paper provides a categorized bibliography on the application of the techniques of multiple criteria decision making mcdm to problems and issues in finance a total of 265 references have been compiled and classified according to the methodological approaches of goal programming multiple objective programming the analytic hierarchy process etc and to the application areas of capital budgeting working capital management portfolio analysis etc the bibliography provides an overview of the literature on mcdm combined with finance shows how contributions to the area have come from all over the world facilitates access to the entirety of this heretofore fragmented literature and underscores the often multiple criterion nature of many problems in finance

decision support models for sustainable mining networks fundamentals and challenges abstract natural resource endowments have the potential of transforming the prospects of many developing economies however a nation s mineral resources can only generate prosperity if specific technology assets are employed in a way as to effectively develop its resource sector capture value from it and transform that value into long term benefits the roadmap to such an ambitious goal lies in effective management supported by consistent formal decision making methods yet integrating environmental and social goals into strategic tactical and operational decisions is a complex challenge often addressed without adequate analytical rigour we provide a systematic analysis of the literature devoted to the development and application of quantitative decision support methods with sustainability considerations in the mining industry by establishing a framework based on the fundamental elements inherent to decision making processes pertinent to mining operations we identify several opportunities for advancing research and practice in particular we find important gaps in elements such as project portfolio optimization operations and waste management and mine closure and rehabilitation and even more so when social targets and impacts are considered it is our belief that insights from this discussion could be of significant value to both academics and practitioners interested in promoting sustainable socio economic development throughout the mining industry

a single settlement energy only electric power market for unpredictable and intermittent participants we discuss a stochastic programming based method for scheduling electric power generation subject to uncertainty such uncertainty may arise from either imperfect forecasting or moment to moment fluctuations and on either the supply or the demand side the method gives a system of locational marginal prices that reflect the uncertainty and these may be used in a market settlement scheme in which payment is for energy only we show that this scheme is revenue adequate in expectation

a participatory budget model under uncertainty abstract participatory budgets are becoming increasingly popular in many municipalities all over the world the underlying idea is to allow citizens to participate in the allocation of a fraction of the municipal budget there are many variants of such processes however in most cases they assume a fixed budget based upon a maximum amount of money to be spent this approach seems lacking especially in times of crisis when public funding suffers high volatility and widespread cuts in this paper we propose a model for participatory budgeting under uncertainty based on stochastic programming

safety first portfolio choice based on financial and sustainability returns the aim of this paper is to expand the methodological spectrum of socially responsible investing by introducing stochastic sustainability returns into safety first models for portfolio choice we provide a foundation of the notion of sustainability in portfolio theory and establish a general model for generalized safety first portfolio management with probabilistic constraints and three specifications of it moreover we prove theorems about conditions for unique optimal solutions and for the constraints of one model being more restrictive than those of another in an empirical part we calculate the costs of investing according to our approach in terms of less financial return

social protection and poverty reduction global patterns and some targets summary this paper argues that social protection needs to be on the post 2015 agenda as a key element of the discourse based on a global social protection data set it estimates that social protection programs are currently preventing 150 million people from falling into poverty even if all low income countries could achieve the very best targeting efficiency observed in the world only 50 could halve the poverty gap through social protection for half of low income countries and for over a fifth of all countries in the sample the issue is one of budgetary adequacy not targeting efficiency

arbitraging intraday wholesale energy market prices with aggregations of thermostatic loads we investigate the potential for aggregations of residential thermostatically controlled loads tcls such as air conditioners to arbitrage intraday wholesale electricity market prices via non disruptive load control we present two arbitrage approaches 1 a benchmark that gives us an optimal policy but requires local computation or real time communication and 2 an alternative based on a thermal energy storage model which relies on less computation communication infrastructure but is suboptimal we find that the alternative approach achieves around 60 80 of the optimal wholesale energy cost savings we use this approach to compute practical upper bounds for savings via arbitrage with air conditioners in california s intraday energy market we investigate six sites over four years and find that the savings range from 2 37 per tcl per year and depend upon outdoor temperature statistics and price volatility

the value of intra day markets in power systems with high wind power penetration in this paper the value of intra day markets in managing wind power uncertainty in competitive electricity markets is analyzed a competitive electricity market model consisting of a day ahead market and a number of intra day markets is considered real time operation adjustment is also taken into account stochastic programming is used to model decision making under wind power uncertainty numerical simulations based on two test systems are presented

the influence of perceived stock value price histories in the mean variance instability model abstract the model introduced by h talpaz a harpaz and j b penson 1984 european journal of operational research 14 262 269 extends the mean variance model introducing the concept of instability in this way it is possible to see an investor s attitude towards predicted instability in this paper we show how optimisation procedures based on penalty or preferred weighted instability matrices can be interpreted in terms of real time utility functions which depend on an actual and a remembered time series due to fading memory this approach justifies some bounded normalised functions used to represent the investors preference between the irregular frequency fluctuations

portfolio selection in a data rich environment we model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables this approach hereafter diffusion index approach offers the opportunity to exploit a much richer information base to improve portfolio selection we use factor analysis to estimate the space spanned by the factors this provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity we consider an empirical application to illustrate the practical usefulness of our approach the results indicate that the diffusion index approach helps to improve the portfolio performance

impossible frontiers a key result of the capital asset pricing model capm is that the market portfolio the portfolio of all assets in which each asset s weight is proportional to its total market capitalization lies on the mean variance efficient frontier the set of portfolios having mean variance characteristics that cannot be improved upon therefore the capm cannot be consistent with efficient frontiers for which every frontier portfolio has at least one negative weight or short position we call such efficient frontiers impossible and show that impossible frontiers are difficult to avoid in particular as the number of assets n grows we prove that the probability that a generically chosen frontier is impossible tends to one at a geometric rate in fact for one natural class of distributions nearly one eighth of all assets on a frontier is expected to have negative weights for every portfolio on the frontier we also show that the expected minimum amount of short selling across frontier portfolios grows linearly with n and even when short sales are constrained to some finite level an impossible frontier remains impossible using daily and monthly u s stock returns we document the impossibility of efficient frontiers in the data

weekly self scheduling forward contracting and pool involvement for an electricity producer an adaptive robust optimization approach abstract this paper addresses the optimization under uncertainty of the self scheduling forward contracting and pool involvement of an electricity producer operating a mixed power generation station which combines thermal hydro and wind sources and uses a two stage adaptive robust optimization approach in this problem the wind power production and the electricity pool price are considered to be uncertain and are described by uncertainty convex sets to solve this problem two variants of a constraint generation algorithm are proposed and their application and characteristics discussed both algorithms are used to solve two case studies based on two producers each operating equivalent generation units differing only in the thermal units characteristics their market strategies are investigated for three different scenarios corresponding to as many instances of electricity price forecasts the effect of the producers approach whether conservative or more risk prone is also investigated by solving each instance for multiple values of the so called budget parameter it was possible to conclude that this parameter influences markedly the producers strategy in terms of scheduling profit forward contracting and pool involvement these findings are presented and analyzed in detail and an attempted rationale is proposed to explain the less intuitive outcomes regarding the computational results these show that for some instances the two variants of the algorithms have a similar performance while for a particular subset of them one variant has a clear superiority

walrasian market clearing for demand response exchange this paper presents the design and evaluation of an effective market clearing scheme for trading demand response dr in a deregulated power system the proposed scheme is called demand response exchange drx in which dr is treated as a public good to be exchanged between two groups of participating agents namely dr buyers and dr sellers while buyers require dr and are willing to pay for it sellers have the capacity to curtail customer loads to supply dr on request the drx market clearing scheme uses walrasian auctions where agents update their dr quantity bids in response to prices adjusted by the market operator this auction is repeated iteratively until market equilibrium is obtained at the point where the market outcome is proven to be pareto optimal the proposed scheme is tested on a small power system and its effectiveness substantiated

stochastic dominance and expected utility survey and analysis while stochastic dominance has been employed in various forms as early as 1932 it has only been since 1969 1970 that the notion has been developed and extensively employed in the area of economics finance agriculture statistics marketing and operations research in this survey the first second and third order stochastic dominance rules are discussed with an emphasis on the development in the area since the 1980s

sensitivity analysis for mean variance portfolio problems this paper shows how to perform sensitivity analysis for mean variance mv portfolio problems using a general form of parametric quadratic programming the analysis allows an investor to examine how parametric changes in either the means or the right hand side of the constraints affect the composition mean and variance of the optimal portfolio the optimal portfolio and associated multipliers are piecewise linear functions of the changes in either the means or the right hand side of the constraints the parametric parts of the solution show the rates of substitution of securities in the optimal portfolio while the parametric parts of the multipliers show the rates at which constraints are either tightening or loosening furthermore the parametric parts of the solution and multipliers change in different intervals when constraints become active or inactive the optimal mv paths for sensitivity analyses are piecewise parabolic as in traditional mv analysis however the optimal paths may contain negatively sloping segments and are characterized by types of kinks i e points of nondifferentiability not found in mv analysis

interactive decision system in stochastic multiobjective portfolio selection abstract some investors pursuing a high return at the relatively high risk of losing a part of their money do not always select a preferred portfolio from among the so called mean variance efficient frontier provided by markowitz 1959 portfolio selection wiley new york chue and nagasawa 1996 an investor modeling and interactive method in multiobjective portfolio selection proceedings of the pacific conference on manufacturing 96 constructed their own investor model based on an risk permission maximum return portfolio and proposed an interactive portfolio selection system for selecting a preferred portfolio from the set of candidate portfolios this paper extends the system to the case when the mean and the variance of return of securities have several scenarios with known occurrence probabilities

renewable energy investments under different support schemes a real options approach abstract this paper adopts a real options approach to analyze investment timing and capacity choice for renewable energy projects under different support schemes the main purpose is to examine investment behavior under the most extensively employed support schemes namely feed in tariffs and renewable energy certificate trading we consider both multiple sources of uncertainty under each support scheme and uncertainty with respect to any change of support scheme and we obtain both analytical when possible and numerical solutions in a nordic case study based on wind power we find that the feed in tariff encourages earlier investment nevertheless as investment has been undertaken renewable energy certificate trading creates incentives for larger projects in our baseline scenario and taking the fixed feed in tariff as a base the revenue required to trigger investments is 61 higher with renewable certificates at the same time investment capacity is 61 higher

demand response in smart electricity grids equipped with renewable energy sources a review abstract dealing with renewable energy resources rers requires sophisticated planning and operation scheduling along with state of art technologies among many possible ways for handling rers demand response dr is investigated in the current review because of every other year modifications in dr definition and classification announced by federal energy regulatory commission ferc the latest dr definition and classification are scrutinized in the present work moreover a complete benefit and cost assessment of dr is added in the paper measurement and evolution methods along with the effects of dr in electricity prices are discussed next comes dr literature review of the recent papers majorly published after 2008 eventually successful dr implementations around the world are analyzed

economic valuation of reserves in power systems with high penetration of wind power this paper proposes a methodology to determine the required level of spinning and nonspinning reserves in a power system with a high penetration of wind power the computation of the required reserve levels and their costs is achieved through a stochastic programming market clearing model spanning a daily time horizon this model considers the network constraints and takes into account the cost of both the load shedding and the wind spillage the methodology proposed is illustrated using an example and a realistic case study some conclusions are finally drawn

multi stage robust unit commitment considering wind and demand response uncertainties with the increasing penetration of wind power into the power grid maintaining system reliability has been a challenging issue for isos rtos due to the intermittent nature of wind power in addition to the traditional reserves provided by thermal hydro and gas generators demand response dr programs have gained much attention recently as another reserve resource to mitigate wind power output uncertainty however the price elastic demand curve is not exactly known in advance which provides another dimension of uncertainty to accommodate the combined uncertainties from wind power and dr we allow the wind power output to vary within a given interval with the price elastic demand curve also varying in this paper we develop a robust optimization approach to derive an optimal unit commitment decision for the reliability unit commitment runs by isos rtos with the objective of maximizing total social welfare under the joint worst case wind power output and demand response scenario the problem is formulated as a multi stage robust mixed integer programming problem an exact solution approach leveraging benders decomposition is developed to obtain the optimal robust unit commitment schedule for the problem additional variables are introduced to parameterize the conservatism of our model and avoid over protection finally we test the performance of the proposed approach using a case study based on the ieee 118 bus system the results verify that our proposed approach can accommodate both wind power and demand response uncertainties and demand response can help accommodate wind power output uncertainty by lowering the unit load cost

risk and spectral instability in portfolio analysis abstract this study extends the traditional e v portfolio selection model pioneered by markowitz and extended by others to accommodate the investor s attitude towards predicted instability it is argued that investors given the choice between the two equal amplitude net earnings series may either prefer or be averse to investing in that stock with the highest frequency all other things constant an instability aversion preference function was developed in this study that could accomodate alternative attitudes toward instability finally a situation where an investor who is assumed to be averse to predicted instability was given the choice of selecting among alternative combinations of six stocks was analyzed under alternative weights assigned to the importance of instability relative to risk the results showed that the optimal portfolio combination may begin to differ sharply from the traditional e v solution as the investor s aversion toward instability becomes more important relative to his aversion to risk

capital asset pricing compatible with observed market value weights we show that the set of expected return vectors for which an observed portfolio is mean variance mv efficient is a two parameter family we identify ten ways to specify the time series behavior of the two parameters the result highlights a number of inconsistencies involved in mv modelling for each of the cases it permits the inference of the time series of expected return vectors as well as all the other capital asset pticing model capm variables compatible with a known covariance matrix and the observed time series of market value weights the empirical work shows that there are substantial case to case differences in the time series of mean vectors and many of them are quite different from the constant mean vector envisioned in tests of

dea frontier improvement and portfolio rebalancing an application of china mutual funds on considering sustainability information disclosure abstract data envelopment analysis dea has been a widely used methodology for evaluating the relative performance of portfolios extensive work has appeared for realizing the role of dea in the aspect of assessing portfolio performance and deriving the relative rankings however the role of providing realizable benchmarks is not fully explored in the existing literatures most importantly the dea based portfolio performance evaluation methods developed in the existing literatures can hardly provide support for fund managers in pursuing sustainability performance in this paper we first propose a dea frontier improvement approach under the mean variance framework this approach provides investor with a rebalancing strategy as well as an improved dea frontier which approximates the portfolio efficient frontier better than the traditional dea model does then this approach is extended to a general return risk framework our in sample simulation results verify the effectiveness of the proposed approach for a wide range of applications in addition the out of sample tests show that the rebalancing investment strategies can achieve higher sharpe ratios and sortino ratios than those of the original ones finally we apply the proposed approach to evaluate mutual fund performance in china with the consideration of sustainability information disclosure we construct a disclosure index to indicate the extent of sustainable information disclosure of each mutual fund the results show that the proposed approach provides not only investment recommendations but also references for constructing sustainable green funds

international portfolio diversification with estimation risk international portfolio diversification has long been advocated as a way of enhancing average returns while reducing portfolio risk for the investor who considers diversifying into foreign securities this proposition however relies on the assumption that the required inputs to the classical mean variance analysis are known with certainty typically expected returns variances and covariances are simply replaced by their ex post sample values and the optimal portfolio is derived without mentioning the uncertainty inherent in these parameter values but the rational investor should take this uncertainty into account when forming expectations and probably will consider estimators that are less subject to estimation error than the classical sample mean this paper investigates alternative estimators of expected returns and their implications for the alleged gains from diversification an important observation is the crucial influence that errors in estimating expected returns have on portfolio analysis a closer examiprevious studies of international portfolio diversification have relied on ex post meanvariance analysis without considering the problem of estimation risk typically past averages are substituted for expected returns and no allowance is made for the uncertainty inherent in these parameter values first i demonstrate the shortcomings of such an approach and second i investigate another method of estimating expected returns initially proposed by stein by shrinking the sample averages toward a common mean i find that the out of sample performance of the optimal portfolio is substantially increased one of the implications of this method is that the classical conclusions vastly overestimate the possible gains in average returns instead benefits from diversification are more likely to accrue from a reduction in risk this paper draws on part of my dissertation at the university of chicago i am grateful to arnold zellner michael mussa george constantinides and especially john bilson for useful discussions and comments numerous improvements were also suggested by the referee this research received the generous financial support of the college interuniversitaire d etudes doctorales dans les sciences du management

iso s optimal strategies for scheduling the hourly demand response in day ahead markets this paper presents a hierarchical demand response dr bidding framework in the day ahead energy markets which integrates customer dr preferences and characteristics in the iso s market clearing process in the proposed framework load aggregators submit aggregated dr offers to the iso which would centrally optimize final decisions on aggregators dr contributions in wholesale markets the hourly load reduction strategies include load shifting and curtailment and the use of onsite generation and energy storage systems the iso applies mixed integer linear programming milp to the solution of the proposed dr model in the day ahead market clearing problem the proposed model is implemented using a 6 bus system and the ieee rts and several studies are conducted to demonstrate the merits of the proposed dr model

large scale integration of deferrable demand and renewable energy sources we present a stochastic unit commitment model for assessing the impacts of the large scale integration of renewable energy sources and deferrable demand in power systems in terms of reserve requirements we analyze three demand response paradigms for assessing the benefits of demand flexibility the centralized co optimization of generation and demand by the system operator demand bids and the coupling of renewable resources with deferrable loads we motivate coupling as an alternative for overcoming the drawbacks of the two alternative demand response options and we present a dynamic programming algorithm for coordinating deferrable demand with renewable supply we present simulation results for a model of the western electricity coordinating council

a robust optimization approach to energy and reserve dispatch in electricity markets abstract to a large extent electricity markets worldwide still rely on deterministic procedures for clearing energy and reserve auctions however increasing shares of the production mix consist of renewable sources whose nature is stochastic and non dispatchable as their output is uncertain and cannot be controlled by the operators of the production units stochastic programming models allow the joint determination of the day ahead energy and reserve dispatch accounting for the uncertainty in the output from these sources however the size of these models gets quickly out of hand as a large number of scenarios are needed to properly represent the uncertainty in this work we take an alternative approach and cast the problem as an adaptive robust optimization problem the resulting day ahead energy and reserve schedules yield the minimum system cost accounting for the cost of the redispatch decisions at the balancing real time stage in the worst case realization of the stochastic production within a specified uncertainty set we propose a novel reformulation of the problem that allows considering general polyhedral uncertainty sets in a case study we show that in comparison to a risk averse stochastic programming model the robust optimization approach progressively trades off optimality in expectation with improved performance in terms of risk these differences however gradually taper off as the level of risk aversion increases for the stochastic programming approach computational studies show that the robust optimization model scales well with the size of the power system which is promising in view of real world applications of this approach

approximate portfolio analysis abstract this paper presents a portfolio selection model based on the idea of approximation the model describes a portfolio by its decumulative distribution curve and a preference structure by a family of convex indifference curves it prescribes the optimal portfolio as the one whose decumulative curve has the highest tangent indifference curve the model extends the mean variance model in the sense that it does not restrict the return distributions of assets to be normal while under the assumption of normality the model simplifies to the mean variance model the model has a measure of risk attitudes that resembles the arrow pratt measure while combining both wealth and probability attitudes using this measure we show that the smaller the curvature of a value function and the larger the curvature of a weighting function the more risk averse an agent

value of information in portfolio selection with a taiwan stock market application illustration abstract despite many proposed alternatives the predominant model in portfolio selection is still mean variance however the main weakness of the mean variance model is in the specification of the expected returns of the individual securities involved if this process is not accurate the allocations of capital to the different securities will in almost all certainty be incorrect if however this process can be made accurate then correct allocations can be made and the additional expected return following from this is the value of information this paper thus proposes a methodology to calculate the value of information a related idea of a level of disappointment is also shown how value of information calculations can be important in helping a mutual fund settle on how much to set aside for research is discussed in reference to a taiwan stock exchange illustrative application in which the value of information appears to be substantial heavy use is made of parametric quadratic programming to keep computation times down for the methodology

tri criterion inverse portfolio optimization with application to socially responsible mutual funds we present a framework for inverse optimization in a markowitz portfolio model that is extended to include a third criterion the third criterion causes the traditional nondominated frontier to become a surface until recently it had not been possible to compute such a surface but by using a new method that is able to generate the nondominated surfaces of tri criterion portfolio selection problems we are able to compute via inverse optimization the implied risk tolerances of given funds that pursue an additional objective beyond risk and return in applying this capability to a broad sample of conventional and socially responsible sr mutual funds we find that after the screening process there appears to be no significant di erence between how assets are allocated in socially responsible and conventional mutual funds which is likely to be di erent from what most sr investors would expect

evaluating the impacts of real time pricing on the cost and value of wind generation one of the costs associated with integrating wind generation into a power system is the cost of redispatching the system in real time due to day ahead wind resource forecast errors one possible way of reducing these redispatch costs is to introduce demand response in the form of real time pricing rtp which could allow electricity demand to respond to actual real time wind resource availability using price signals a day ahead unit commitment model with day ahead wind forecasts and a real time dispatch model with actual wind resource availability is used to estimate system operations in a high wind penetration scenario system operations are compared to a perfect foresight benchmark in which actual wind resource availability is known day ahead the results show that wind integration costs with fixed demands can be high both due to real time redispatch costs and lost load it is demonstrated that introducing rtp can reduce redispatch costs and eliminate loss of load events finally social surplus with wind generation and rtp is compared to a system with neither and the results demonstrate that introducing wind and rtp into a market can result in superadditive surplus gains

short sales restrictions and kinks on the mean variance frontier with a short sales restriction there may be switching points along the mean variance frontier corresponding to changes in the set of assets held traditional wisdom holds that each switching point corresponds to a kink while ross has claimed that kinks never occur this paper shows that the truth lies between the two views since the efficient frontier may or may not be kinked at a switching point there is some indication that kinks are rare since a kink corresponds to a portfolio in which all assets have the same expected return

gains from international diversification 1968 85 returns on portfolios of stocks and bonds this paper applies the multi period investment model to a universe of international securities on the basis of the simple probability assessment approach our principal findings are 1 the gains from including non u s asset categories in the universe were remarkably large in some cases statistically significant especially for the highly risk averse strategies 2 the gains from removing the no leverage constraint were more substantial than they were in the absence of non u s securities and 3 there is strong evidence of market segmentation in that the optimal levels of investment in u s securities were mostly zero in the presence of the non u s asset categories

selecting a portfolio with skewness recent evidence from us european and latin american equity markets abstract polynomial goal programming in which investor preferences for skewness can be incorporated is utilized to determine the optimal portfolio from latin american us and european capital markets the empirical findings suggest that the incorporation of skewness into an investor s portfolio decision causes a major change in the resultant optimal portfolio the empirical evidence indicates that investors do trade expected return of the portfolio for skewness

tri criterion modeling for constructing more sustainable mutual funds one of the most important factors shaping world outcomes is where investment dollars are placed in this regard there is the rapidly growing area called sustainable investing where environmental social and corporate governance esg measures are taken into account with people interested in this type of investing rarely able to gain exposure to the area other than through a mutual fund we study a cross section of u s mutual funds to assess the extent to which esg measures are embedded in their portfolios our methodology makes heavy use of points on the nondominated surfaces of many tri criterion portfolio selection problems in which sustainability is modeled after risk and return as a third criterion with the mutual funds acting as a filter the question is how effective is the sustainable mutual fund industry in carrying out its charge our findings are that the industry has substantial leeway to increase the sustainability quotients of its portfolios at even no cost to risk and return thus implying that the funds are unnecessarily falling short on the reasons why investors are investing in these funds in the first place