ABSTRACT We study whether option-implied conditional expectation of market loss due to tail events, or tail loss measure, contains information about future returns, especially the negative ones. Our tail loss measure predicts future market returns, magnitude, and probability of the market crashes, beyond and above other option-implied variables. Stock-specific tail loss measure predicts individual expected returns and magnitude of realized stock-specific crashes in the cross-section of stocks. An investor, especially the one who cares about the left tail of her wealth distribution (e.g., disappointment-averse), benefits from using the tail loss measure as an information variable to construct managed portfolios of a risk-free asset and market index. The tail loss measure is motivated by the results of the extreme value theory, and it is computed from observed prices of out-of-the-money put as the risk-neutral expected value of a loss beyond a given relative threshold.

In 1950 Markowitz first formalized the portfolio optimization problem in terms of mean return and variance. Since then, the mean-variance model has played a crucial role in single-period portfolio optimization theory and practice. In this paper we study the optimal portfolio selection problem in a multi-period framework, by considering fixed and proportional transaction costs and evaluating how much they affect a re-investment strategy. Specifically, we modify the single-period portfolio optimization model, based on the Conditional Value at Risk (CVaR) as measure of risk, to introduce portfolio rebalancing. The aim is to provide investors and financial institutions with an effective tool to better exploit new information made available by the market. We then suggest a procedure to use the proposed optimization model in a multi-period framework. Extensive computational results based on different historical data sets from German Stock Exchange Market (XETRA) are presented.

This paper compares two groups of stocks to analyse the efficiency of having an ethical portfolio in comparison with a conventional portfolio. Therefore, efficiency test by second-order stochastic dominance (SSD) approach is applied on both groups, which consist of 12 stocks. Ethical portfolio is chosen from the stocks complying with the participation banking rules. Conventional portfolio is selected from Borsa Istanbul with choosing the corresponding stocks for each ethical stock according to the sector and market capitalization. All the stocks of both groups are SSD pairwise compared. Both groups of 12 stocks are inefficient portfolios, however a group of 7 stocks constitute an efficient ethical portfolio with the total weight of 69% among the set of 12 ethical stocks. On the other hand, a group of 6 stocks constitute an efficient conventional portfolio with the total weight of 64% among the set of 12 conventional stocks. By pairwise SSD comparison of corresponding stocks from both groups; despite none of the conventional stocks dominate ethical stocks, 4 ethical ones dominated the conventional ones.

In this paper we develop a framework for the study of financial equilibrium in the case of sectors in the economy, each of which is faced with two objectives/criteria in his portfolio selection decision making. In particular, we first present the bicriteria decision model of an individual financial sector, who seeks an optimal portfolio composition, given that the wishes to

Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnover rate, Treynor index and Sharpe index. Semivariance is used as a downside risk measure. The proposed model of this paper uses fuzzy variables for return rate and semivariance. A multi-objective fuzzy mean-semivariance portfolio optimization model is implemented and fuzzy programming technique is adopted to solve the resulted problem. The proposed model of this paper has gathered the information of mutual fund traded on NASDAQ from 2007 to 2009 and Pareto optimal solutions are obtained considering different weights for objective functions. The results of asset allocation, rate of return and risk of each cluster are also determined and they are compared with the results of two clustering methods.

This paper deals with the issue of buy-in thresholds in portfolio optimization using the Markowitz approach. Optimal values of invested fractions calculated using, for instance, the classical minimum-risk problem can be unsatisfactory in practice because they lead to unrealistically small holdings of certain assets. Hence we may want to impose a discrete restriction on each invested fraction y i such as y i > y min or y i = 0. We shall describe an approach which uses a combination of local and global optimization to determine satisfactory solutions. The approach could also be applied to other discrete conditions-for instance when assets can only be purchased in units of a certain size (roundlots). Keywords Portfolio selection • buy-in thresholds • roundlots • global optimization • mixed-integer optimization The authors are grateful to the anonymous referees for comments and suggestions which have helped to strengthen this paper. Particular thanks are due to Don Jones for generous advice about our discussion and use of DIRECT.

This paper used complementary panel data models that are fixed effect regression model and panel vector auto regression model. The study was motivated by the hypothesis that both macroeconomic and microeconomic variables have an effect on the loan quality. The first part of the research was to determine the specific macro and microeconomic variables that give rise to the non-performing loans (NPLs) using fixed effect regression model. The empirical findings of this study provide evidence that nonperforming loans depends on macro and micro economic variables, the trend analysis of Zimbabwean commercial banks' shows an upward movement of over the period of study. The study found out that Gross domestic product (GDP), Inflation, loan deposit ratio and bank size had a statistical significant effect on the level of non-performing loans (NPLs). The second part was mainly to model the dynamic relationship of all the variables that were found to affect nonperforming loans (NPLs); this was done through impulse response analysis based on PANEL VAR model. One standard shock to credit growth will be greatly felt in the sixth year, whereas of size of the bank will have a great negative impulse in the seventh year.

The problem of selecting a portfolio has been largely faced in terms of minimizing the risk, given the return. While the complexity of the quadratic programming model due to Markowitz has been overcome by the recent progress in algorithmic research, the introduction of linear risk functions has given rise to the interest in solving portfolio selection problems with real constraints. In this paper we deal with the portfolio problem with minimum transaction lots. We show that in this case the problem of ®nding a feasible solution is, independently of the risk function, NP-complete. Moreover, given the mixed integer linear model, new heuristics are proposed which starting from the solution of the relaxed problem allow to ®nd a solution close to the optimal one. The algorithms are based on the construction of mixed integer subproblems (using only a part of the securities available) formulated using the information obtained from the solution of the relaxed problem. The heuristics have been tested with respect to two disjoint time periods, using real data from the Milan Stock Exchange. Ó

We address a portfolio optimization problem in a semi-Markov modulated market. We study both the terminal expected utility optimization on finite time horizon and the risk-sensitive portfolio optimization on finite and infinite time horizon. We obtain optimal portfolios in relevant cases. A numerical procedure is also developed to compute the optimal expected terminal utility for finite horizon problem.

In the classical discrete-time mean-variance context, a method for portfolio optimisation using conditioning information was introduced in 2001 by Ferson and Siegel ([1]). The fact that there are many possible signals that could be used as conditioning information, and a number of empirical studies that suggest measurable relationships between signals and returns, causes this type of portfolio optimisation to be of practical as well as theoretical interest. Ferson and Siegel obtain analytical formulae for the basic unconstrained portfolio optimisation problem. We show how the same problem, in the presence of a riskfree asset and given a single conditioning information time series, may be expressed as a general constrained infinite-horizon optimal control problem which encompasses the results in [1] as a special case. Variants of the problem not amenable to closed-form solutions can then be solved using standard numerical optimal control techniques. We extend the standard finite-hori...

Index tracking aims at replicating a given benchmark with a smaller number of its constituents. Different quantitative models can be set up to determine the optimal index replicating portfolio. In this paper, we propose an alternative based on imposing a constraint on the q-norm, 0 < q < 1, of the replicating portfolios' asset weights: the q-norm constraint regularises the problem and identifies a sparse model. Both approaches are challenging from an optimisation viewpoint due to either the presence of the cardinality constraint or a non-convex constraint on the q-norm. The problem can become even more complex when non-convex distance measures or other real-world constraints are considered. We employ a hybrid heuristic as a flexible tool to tackle both optimisation problems. The empirical analysis on real-world financial data allows to compare the two index tracking approaches. Moreover, we propose a strategy to determine the optimal number of constituents and the corresponding optimal portfolio asset weights.

... Since the pri-mary focus of this research pertains to the project screening process, in which go/no-go decisions are being made early in the acquisition lifecycle, this linear assumption seems appropriate. ... REFERENCES [1] KM Al-Subhi Al-Harbi, “Application of the AHP in ...

Stochastic differential equations have been shown useful in describing random continuous time processes. Biomedical experiments often imply repeated measurements on a series of experimental units and differences between units can be represented by incorporating random effects into the model. When both system noise and random effects are considered, stochastic differential mixed-effects models ensue. This class of models enables the simultaneous representation of randomness in the dynamics of the phenomena being considered and variability between experimental units, thus providing a powerful modelling tool with immediate applications in biomedicine and pharmacokinetic/pharmacodynamic studies. In most cases the likelihood function is not available, and thus maximum likelihood estimation of the unknown parameters is not possible. Here we propose a computationally fast approximated maximum likelihood procedure for the estimation of the non-random parameters and the random effects. The method is evaluated on simulations from some famous diffusion processes and on real data sets.

... 27-51, 1970. 23. Jagannathan, R. and Ma, T., &amp;amp;amp;amp;amp;amp;quot;Risk reduction in large portfolios: Why imposing the wrong constraints helps,&amp;amp;amp;amp;amp;amp;quot; J. Finance, v58, pp. 1651-1684, 2003. ... Lorenzo Garlappi: colleagues. Francisco J. Nogales: colleagues. Raman Uppal: colleagues. ...

The paper approaches the potential of risk-adjusted performance indicators in life insurance, with special reference to a structured policy. The final issue is the computation of risk adjusted indicators as a tool to evaluate the portfolio given a policy structure. The computation of such indicator could be suitable for the appraisal of both portfolio optimization and potential profits of the structured policy. The selection tool is put into an asset and liability management decision making context, where the relationship between expected surplus and capital at risk are compared. The analysis is applied to a structured temporary annuity and is treated by means of Monte Carlo simulations.

This paper examines the portfolio optimization of energy futures by using the STARR ratio that can evaluate the risk and return relationship for skewed distributed returns. We model the price returns for energy futures by using the ARMA(1,1)-GARCH(1,1)-PCA model with stable distributed innovations that reflects the characteristics of energy: mean reversion, heteroskedasticity, seasonality, and spikes. Then, we propose the method for selecting the portfolio of energy futures by maximizing the STARR ratio, what we call "Winner portfolio". The empirical studies by using energy futures of WTI crude oil, heating oil, and natural gas traded on the NYMEX compare the price return models with stable distributed innovations to those with normal ones.

The popularity of downside risk among investors is growing and mean return±downside risk portfolio selection models seem to oppress the familiar mean±variance approach. The reason for the success of the former models is that they separate return¯uctuations into downside risk and upside potential. This is especially relevant for asymmetrical return distributions, for which mean±variance models punish the upside potential in the same fashion as the downside risk.

The selection of optimal portfolios is the central problem of financial investment decisions. Mathematically speaking, portfolio selection refers to the formulation of an objective function that determines the weights of the portfolio invested in each asset as to maximize return and minimize risk. This paper applies the method of genetic algorithm (GA) to obtain an optimal portfolio selection. However, the GA parameters are of great importance in the procedure of convergence of this algorithm towards the optimal solution such as crossover. While, a five stock portfolio example is used in this paper to illustrate the applicability and efficiency of genetic algorithm method, GA method can also be used however for a larger number of portfolio compositions. The results obtained confirm previous research studies about the validity and efficiency of genetic algorithm in selecting optimal portfolios.

The main objective of a hedge strategy is to generate positive returns irrespective of market conditions. This paper presents a classic hedge fund strategy: an investment vehicle whose key objective is to minimize investment risk in an attempt to deliver profits under all market circumstances. The hedge is designed to have minimal correlation with the market and, irrespective of market direction, the fund seeks to generate positive alpha. A significant difference between this model and more traditional hedge fund strategies is that portfolio optimization is based upon the cointegration of prices rather than the correlation of returns.

This work presents a new prediction-based portfolio optimization model that can capture short-term investment opportunities. We used neural network predictors to predict stocks' returns and derived a risk measure, based on the prediction errors, that have the same statistical foundation of the mean-variance model. The efficient diversification effects holds thanks to the selection of predictors with low and complementary pairwise error profiles.

This paper will investigate the optimum portfolio for an investor, taking into account 5 criteria. The mean variance model of portfolio optimization that was introduced by Markowitz includes two objective functions; these two criteria, risk and return do not encompass all of the information about investment; information like annual dividends, S&P star ranking and return in later years which is estimated by using data from a longer history. Thus portfolio selection is a typical multi-objective decision making (MODM) problem. It is well known that Goal Programming (GP), based on preemptive priorities and target values, has been successful in solving MODM problems. In this paper we rank objectives of the MODM model according to weights elicited from Decision Maker’s (DM) preferences. Then we obtain goals from DM’s opinion. As a guidance for DM, we revise these goals consistent with ranking of objectives by a Linear Programming model in a way that new goals remain as close as possible t...

We provide a computational study of the problem of optimally allocating wealth among multiple stocks and a bank account, to maximize the infinite horizon discounted utility of consumption. We consider the situation where the transfer of wealth from one asset to another involves transaction costs that are proportional to the amount of wealth transferred. Our model allows for correlation between the price processes, which in turn gives rise to interesting hedging strategies. This results in a stochastic control problem with both drift-rate and singular controls, which can be recast as a free boundary problem in partial differential equations. Adapting the finite element method and using an iterative procedure that converts the free boundary problem into a sequence of fixed boundary problems, we provide an efficient numerical method for solving this problem. We present computational results that describe the impact of volatility, risk aversion of the investor, level of transaction costs, and correlation among the risky assets on the structure of the optimal policy. Finally we suggest and quantify some heuristic approximations.

This paper introduces an interactive approach to support multi-criteria decision analysis of project portfolios. In high-scale strategic decision domains, scientific studies suggest that the Decision Maker (DM) can find help by using many-objective optimisation methods, which are supposed to provide values in the decision variables that generate highquality solutions. Even so, DMs usually wish to explore the possibility of reaching some levels of benefits in some objectives. Consequently, they should repeatedly run the optimisation method. However, this approach cannot perform well-in an interactive way-for large instances under the presence of many objective functions. We present a mathematical model that is based on compromise programming and fuzzy outranking to aid DMs in analysing multi-criteria project portfolios on the fly. This approach allows relaxing the problem of rapidly optimising portfolios while preserving the beneficial properties of the DM's preferences expressed by outranking relations. Our model supports the decision analysis on two instance benchmarks: for the first one, a better compromise solution was generated 84% of the runs; for the second one, this ranged from 93% to 97%. Our model was also applied to a real-world problem involving social projects, obtaining satisfactory results.

This study aims to analyzethe contributions of the factors that influence the movement of the yield curve of government securities (SUN) in Indonesia as integratedpaper of Sihombing et al. (2014).Fundingof Indonesian governmentcontinues to grow through the domestic bond market thatindicated by the issuance of bonds that tends to increase overtime since 2005. The conclusions on yield curve studies usually only imposedthe effect of macroeconomic fundamentals, such as interest rates, inflation, economic growth, money supply and the exchange rate. However, researches on the determinants of the yield curve beyond macroeconomic factors, especially in developing countries such as Indonesia is still limited. This study is using the Vector Error Correction Model (VECM), which is a restricted form of Vector Autoregressive (VAR) to analysis the contributions given by factors that affect the fluctuations of a yield curve.The findings of this study found that the movements of interest rates and stock indices contribute to slope fluctuations. Meanwhile, curvature fluctuations are contributed by the movement of interest rates. Finally, the movements of the exchange rate, inflation, foreign ownership and foreign reserves contribute significantly to the movement of level.

The 2018 report of the Intergovernmental Panel on Climate Change on limiting global warming to 1.5 °C highlights the importance of access to capital for reaching this target. As directly or indirectly government-owned and -controlled investment vehicles with a an intrinsically long-term perspective, sovereign wealth funds have an self-interest in preventing climate change and its long-term impacts on the world economy and their broader portfolios. Other investors may choose to look upon climate change as an externality as long as they are not forced to take it into account. By contrast, sovereign wealth funds are perhaps the investor class for whom it makes most sense to internalize the consequences of climate change, as their long-term investment horizon makes them directly vulnerable to its consequences. Nonetheless, the number of sovereign wealth funds that engage in such investments and the proportion of their capital that is directed towards green financing remains small. This chapter discusses the operational aspects that make sovereign wealth funds good candidates of public green financing and the limitations that they face in this process. The discussion concludes with useful policy and governance considerations.

Herewith a small Excel and VBA demonstration of the Mean Absolute Deviation (MAD) Portfolio Optimization Model using LP Simplex methods. It is based on the recent paper by Mike Fox on (a) Further Reduction of the Konno-Yamazaki Mean-Absolute Deviation Portfolio Optimization Model http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2508777

Developing a successful asset allocation strategy requires the construction of diversified portfolios, able to perform well out-of-sample. During the 2007/08 financial crisis, many investment portfolios lost substantial amounts of capital because they were based on Markowitz's portfolio optimisation, a scheme which is highly sensitive to estimation errors and to the presence of multicollinearity, that increases especially during crisis periods. This paper applies two popular techniques used in statistical regularisation, LASSO and RIDGE, that are designed to avoid overfitting (a side effect of multicollinearity) and improve the portfolio estimation risk. It is found that these techniques outperform the classical Markowitz optimisation scheme and lead to more stable asset allocations.

The present work overviews the application of recom-mender systems in various financial domains. The relevant literature is investigated based on two directions. First, a domain-based cate-gorization is discussed focusing on those recommendation problems, where the existing literature is significant. Second, the application of various recommendation algorithms and data mining techniques is summarized. The purpose of this paper is providing a basis for further scientific research and product development in this field.

Portfolio optimization with private equity is based on one of three different indices: listed private equity indices, transaction-based private equity indices, and appraisal value based private equity indices. We show that none of these indices are appropriate for portfolio optimization. We introduce a new benchmark index for buyouts and venture capital. Our benchmark is updated monthly, adjusted for autocorrelation (desmoothing) and available contemporaneously. We show our benchmark enables superior quantitative portfolio optimization.

We propose a framework for studying optimal market making policies in a limit order book (LOB). The bid-ask spread of the LOB is modelled by a Markov chain with finite values, multiple of the tick size, and subordinated by the Poisson process of the tick-time clock. We consider a small agent who continuously submits limit buy/sell orders at best bid/ask quotes, and may also set limit orders at best bid (resp. ask) plus (resp. minus) a tick for getting the execution order priority, which is a crucial issue in high frequency trading. By trading with limit orders, the agent faces an execution risk since her orders are executed only when they meet counterpart market orders, which are modelled by Cox processes with intensities depending on the spread and on her limit prices. By holding non-zero positions on the risky asset, the agent is also subject to the inventory risk related to price volatility. Then the agent can also choose to trade with market orders, and therefore get immediate execution, but at a least favorable price because she has to cross the bid-ask spread.

Benati and Rizzi [S. Benati, R. Rizzi, A mixed integer linear programming formulation of the optimal mean/Value-at-Risk portfolio problem, European Journal of Operational Research 176 (2007) 423-434], in a recent proposal of two linear integer programming models for portfolio optimization using Value-at-Risk as the measure of risk, claimed that the two counterpart models are equivalent. This note shows that this claim is only partly true. The second model attempts to minimize the probability of the portfolio return falling below a certain threshold instead of minimizing the Value-at-Risk. However, the discontinuity of real-world probability values makes the second model impractical. An alternative model with Value-at-Risk as the objective is thus proposed.

The selection of optimal portfolios is the central problem of financial investment decisions. Mathematically speaking, portfolio selection refers to the formulation of an objective function that determines the weights of the portfolio invested in each asset as to maximize return and minimize risk. This paper applies the method of genetic algorithm (GA) to obtain an optimal portfolio selection. However, the GA parameters are of great importance in the procedure of convergence of this algorithm towards the optimal solution such as crossover. While, a five stock portfolio example is used in this paper to illustrate the applicability and efficiency of genetic algorithm method, GA method can also be used however for a larger number of portfolio compositions. The results obtained confirm previous research studies about the validity and efficiency of genetic algorithm in selecting optimal portfolios.

In this paper we use a stochastic programming approach to develop currency option hedging models which can address problems with multiple random factors in an imperfect market. The portfolios considered in our model are rebalanced at the end of each time period, and reinvestments are allowed during the hedging process. These sequential decisions (reinvestments) are based on the evolution of random parameters such as exchange rates, interest rates, etc. We also allow the inclusion of a variety of instruments in the hedging portfolio, including short term derivative securities, short term options, and futures. These instruments help generate strategies that provide good liquidity and low trade intensity. One of the important features of the model is that it incorporates constraints on sensitivity measures such as Delta and Gamma. By ensuring that these hedge parameters track a desired trajectory (e.g., the parameters of a target option), the new model provides investment strategies that are robust with respect to the perturbations measured by Delta and Gamma. In order to manage the explosion of scenarios due to multiple random factors, we incorporate sampling within a scenario aggregation algorithm. We illustrate that when compared with other myopic hedging methods in imperfect markets, the new stochastic programming model can provide better performance. Our examples also illustrate stochastic programming as a practical computational tool for realistic hedging problems.

There has been significant evidence on the forecasting ability of Regime switching regression models. Smart beta or alternative beta indices are gaining wide popularity among investment community. Smart beta indices constructed based on fundamental weighing are proven to outperform cap-weighted portfolios in the long run. At the same time, smart beta indices have significant exposure to risk factors such as size, value, momentum and volatility. The risk factors exhibit different behaviour in different regimes. In this research we examine and present evidence of the presence of regimes in smart beta indices. We also examine the possibility of adding value to a portfolio by switching between regime dependent portfolios of smart beta indices exploiting factor exposures.

The Markowitz mean-variance portfolio theory posits that the optimal portfolio weights can be chosen based off an efficient tradeoff between profit modeled as the mean and risk measured as the variance-covariance matrix. These values must be historically estimated, occasionally leading to poor portfolio performance and asset misallocation. Moreover, as Brodie et. al. point out, the Markowitz optimization scheme, “is empirically unstable,” making, “the classic Markowitz portfolio optimization an ill-posed (or ill-conditioned) inverse problem.”  Consequently, this paper formulates the global minimum variance portfolio as a regression problem with added penalties. This analysis thus utilizes penalty regularization on training data to more efficiently estimate global minimum variance portfolio weights and resolve many of the classic scheme’s issues.

Yüksek volatilite dönemlerinde hisse senedi yatırımı yapmak, volatilitenin nispeten düşük olduğu dönemlere göre yatırımcılar açısından daha zor olmaktadır. Bu dönemlerde, piyasaların etkinlik seviyeleri azalış göstermekte ve yatırımcıların asimetrik bilgilere maruz kalmaları daha fazla söz konusu olmaktadır. Bu nedenle, gerek hisse senedi seçimi yapılırken gerekse seçilen hisse senetleri ile portföy oluştururken birtakım modem yaklaşımlara sahip modellere oldukça ihtiyaç duyulmaktadır.

Bu çalışmada, yüksek volatilitenin hakim olduğu 2008 Küresel Finansal Krizi döneminde, Gri Sistem Teorisi Destekli Markowitz Portföy Optimizasyonu yapılmıştır. Böylece belirsizliklerin hakim olduğu bu dönemde yatırımcıların karar verme süreçlerinde kullanabilecekleri bir modelin geliştirilmesi amaçlanmıştır. Ayrıca geliştirilen bu modelin oluşturulan Melez Portföyler ve Geleneksel Portföyler ile performans karşılaştırmaları yapılmıştır. Çalışm anın sonuçları, geliştirilen modem yaklaşıma sahip modellerin, yüksek volatilite dönemleri için kullanılabilir olduğu ve başarılı performansı gösterebildiği ortaya konulmuştur.

The problem of optimizing a number of simultaneous bets is considered, using primarily log-utility. Stochastic gradient-based algorithms for solving this problem are developed and compared with the simplex method. The solutions may be regarded as a generalization of 'Kelly staking' to the case of many simultaneous bets. Properties of the solutions are examined in two example cases using real odds from sports bookmakers. The algorithms that are developed also have wide applicability beyond sports betting and may be extended to general portfolio optimization problems, with any reasonable utility function.

Portfolio optimization is to build your portfolio in such a way that you maximize potential returns from investments while still not exceeding the amount of risk you’re willing to carry. Creating a balanced portfolio with many different investments, such as stocks, bonds, mutual funds, etc., is the best way to spread out assets to maintain a riskto-reward ratio. The portfolio optimization model has limited impact in practice due to estimation issues when applied with real data. To address this, we adapt two machine learning methods, regularization and cross-validation, for portfolio optimization. First, we introduce performance-based regularization (PBR), where the idea is to constrain the sample variances of the estimated portfolio risk and return, which steers the solution towards one associated with less estimation error in the performance. We consider PBR for both mean-variance and mean-CVaR problems. For the mean-variance problem, PBR introduces a quartic polynomial constraint, for which we make two convex approximations: one based on rank-1 approximation and another based on a convex quadratic approximation

This paper analyzes the performance and risk-return characteristics of three major emerging art markets: Russia, China, and India. According to three national art market indices, built by hedonic regressions based on auction sales prices, the geometric annual returns are 10.00%, 5.70%, and 42.respectively. The Russian art market exhibits positive correlations with most common financial assets and a positive market beta, whereas the Chinese art market demonstrates a negative correlation overall and a negative market beta, and the Indian art index reveals a negative market beta and varying correlation results. Portfolio optimization under a power utility framework suggests limited diversification potential, but with a downside beta of 0.43, investing in Chinese art offers hedging potential during financial market downswings. Investigating the linkages between art and the economy through co-integration and causality analyses proves that emerging art markets share a significant long-term relation with other financial market instruments, but short-term relations are largely absent. j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e m r circumstances that may help answer questions about whether investing in art can function as a shield in an economic downturn.

American options allow early exercise, which yields an additional challenge when optimizing a portfolio of American options, besides the weights of each option. In this work, we construct strategies for an American option portfolio by exercising options at optimal timings with optimal weights determined concurrently. To model such portfolios, a reinforcement learning (Q-learning) algorithm is proposed, combining an iterative progressive hedging method and a quadratic approximation to Q-values by regression. By means of Monte Carlo simulation and empirical experiments using data from the SPY options market we evaluate the quality of our algorithms and examine their performance under various investment assumptions, such as different portfolio settings and distributions of the underlying asset returns. With discretized timings, our strategies work better in a relatively long time horizon and when the portfolio is hedged using the underlying instrument. Due to the highly-leveraged and risky nature of our strategies, overly risk-averse investors are proved not suitable for such investment opportunities.

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio composition of the selected portfolio for a given value of the portfolio return are also investigated for each considered filtering method.

Keywords: Volatility spillover Oil market Stock markets Oil-importing and oil-exporting countries Portfolio and hedging implications Symmetric and asymmetric DCC-GARCH modelsJEL classification: F65 G11 A B S T R A C T This study analyses the volatility spillover between the oil market and the stock market of oil-importing and oil-exporting countries using daily data over the period from January 2010 to December 2016. The study also explores the portfolio and hedging implications based on dynamic conditional correlation (DCC) and corrected DCC (cDCC) GARCH models. For the analysis, we have used symmetric and asymmetric versions of DCC and cDCC models. Specifically, in the symmetric version of DCC and cDCC, the estimations are based on GARCH (1,1), and in the asymmetric version of DCC and cDDC, the estimations are based on GJR-GARCH (1,1), FIGARCH (1,1) and FIEGARCH (1,1) models, and for each case, we have explored the portfolio and hedging implications. Overall, the evidence indicates that oil-importing countries are severely affected by lagged oil price shocks, and there is less evidence of interdependence between stock markets for both oil-importing and oil-exporting countries. Further, we find that the lagged volatility in the oil market and stock market has a statistically significant impact on the current volatility in its respective markets. The results from the asymmetric analysis show that the magnitudes of the negative shocks are higher than those of the positive shocks. The overall results from portfolio optimization reveal that investors in oil-exporting countries should hold more oil assets in the portfolio to hedge the risk.

Security Analysis, Portfolio Management, and Financial Derivatives integrates the many topics of modern investment analysis. It provides a balanced presentation of theories, institutions, markets, academic research, and practical applications, and presents both basic concepts and advanced principles. Topic coverage is especially broad: in analyzing securities, the authors look at stocks and bonds, options, futures, foreign exchange, and international securities. The discussion of financial derivatives includes detailed analyses of options, futures, option pricing models, and hedging strategies. A unique chapter on market indices teaches students the basics of index information, calculation, and usage and illustrates the important roles that these indices play in model formation, performance evaluation, investment strategy, and hedging techniques. Complete sections on program trading, portfolio insurance, duration and bond immunization, performance measurements, and the timing of stock selection provide real-world applications of investment theory. In addition, special topics, including equity risk premia, simultaneous-equation approach for security valuation, and Itô&amp;#39;s calculus, are also included for advanced students and researchers.

This paper is an attempt to conduct a comprehensive survey of relevant literatures in respect of the relationship between board dynamics and firm performance. The investigation reveals that equivocal findings still dominates most of the previous studies on key board dynamics such as Size, composition, CEO Duality and Diversity amongst others. While the need to assess the connection between board characteristics and corporate performance would for a longtime remained a legitimate and interesting area of investigation, the paper recommend that the researchers avoid mistakes of the past. These include over reliance on singular theory, the use of lone performance measure and most importantly the assumption of express relationship between the two variables. The use of more purposeful process based approach that identifies the cause effect of the relationship would be of tremendous benefit to this vital field of study.