The impact of dynamic technical inefficiency on investment decision of Spanish olive farms

This study analyzes Spanish olive sector investment decision under irreversibility and uncertainty taking into consideration the technical efficiency as a relevant element that could impact that decision by integrating the Real Option Approach (ROA) and a dynamic Stochastic Frontier Model (SFM) estimation.Peer ReviewedPostprint (published version

The impact of dynamic technical inefficiency on investment decision of Spanish olive farms

Spain occupies a first ranking position in worldwide production and exportation for olive oil and table olives. Such position is enforced by the positive evolution of investment demonstrated by an increase of approximately 5% of area dedicated to this cultivation during the last 6 years. This study analyzes Spanish olive sector investment decision taking into consideration the technical efficiency as a relevant element that could impact that decision by integrating the real option approach and a dynamic stochastic frontier model. This analysis has been applied to a 158 Spanish olive farms using FADN data set. The results show that the technical inefficiency persistence parameter is fairly low to unity, which means that small technical inefficiency is transmitted to the next time period. The olive groves investment is irreversible and characterized by uncertainty on price and discount rate. An increase of discount rate means that the farmers take the decision to postpone investment. An increase on price along with a decrease of discount rate leads to the decision to invest with no option value of waiting to invest. The results suggest that the decision of investment in Spanish olive depends also on technical inefficiency and it persistence. The increase of farms inefficiency means that the decision is to wait to invest. Consequently, the inefficient farmers take time and wait to invest, while a smaller persistence parameter leads to the decision to invest.Investment, olive sector, dynamic technical efficiency., Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety,

Decomposing Integrated Assessment Climate Change

We present a decomposition approach for integrated assessment modeling of climate policy based on a linear approximation of the climate system. Our objective is to demonstrate the usefulness of decomposition for integrated assessment models posed in a complementarity format. First, the complementarity formulation cum decomposition permits a precise representation of post-terminal damages thereby substantially reducing the model horizon required to produce an accurate approximation of the infinite-horizon equilibrium. Second, and central to the economic assessment of climate policies, the complementarity approach provides a means of incorporating second-best effects that are not easily represented in an optimization model. --integrated assessment,decomposition,terminal constraints,optimal taxation

Essays on Basket Options Hedging and Irreversible Investment Valuation

Basket options are one of well-known newly-generated exotic options. As its name implies, it is an option on a portfolio of several assets. As the underlying basket offers more diversification, basket options gain increasing popularity in world financial markets as a fundamental instrument to manage portfolio risks. Examples thereof are equity index options which are traded on the exchange and usually contingent on at least 15 stocks, as well as currency basket options traded over the counter and written on over two currencies. Obviously, the unique feature of basket options is the basket underlying and a complex correlation structure therefore involved. It provides investors a couple of benefits like high diversification, a lower price against a portfolio of single options and so on, and meanwhile complicates the evaluation of basket options. The inherent challenge in pricing and hedging basket options stems primarily from the analytical intractability of the distribution of the basket. Moreover, the correlation between underlying assets is observed to be volatile over time. Due to the lack of standardized basket options traded in the market, the correlation structure can be only estimated from historical time series or from scarce option data. This further prevents us from exactly pricing basket options, and more importantly, perfectly hedging basket options. As a result, a partial- or super-hedge is often pursued in the literature when hedging basket options. Apart from these difficulties, we address another difficulty resulted from a great number of underlying assets in the basket while hedging basket options. If following the standard hedging method, a hedging portfolio for basket options should be related to all underlying assets in the basket. Clearly, if the number of the underlying assets is over 15, such a dynamic hedging strategy would be not only hardly implementable in many practical situations but also create a large transaction cost. In this sense, a static or buy-and-hold hedge strategy has its advantage in cost saving and hence hedge performance. As a result, the first part of this dissertation aims to design a static hedging strategy for European-style basket options and to analyze its hedging result. The newly developed static hedging strategies consist of traded plain-vanilla options on only subset of underlying assets. The optimal hedge is either super-- or partial-replicating, depending on the objective function taken in the numerical optimization. Considering the numerical challenge in the optimization with constraints on the initial capital (or some other hedging requirements) and the maximal number of hedging assets, hedging portfolios are suggested in this thesis to be obtained in two steps, namely pre-selection of the sub-hedge-basket and determination of optimal hedging instruments, more precisely, the optimal strikes of available plain-vanilla options on the chosen subset of the basket. Especially, a multivariate statistical technique, Principal Components Analysis, is introduced to identify dominant assets in the basket by taking into account all the coefficients that greatly influence the basket value, such as weight, volatility and correlation. As demonstrated by numerical examples, such hedging portfolios work satisfactorily, generating a reasonably small hedging error though by using only several assets. Real options are defined in the literature as to describe opportunities of investment in non-financial assets with some degree of freedom in decision making against the underlying uncertainty. As many other researchers, we are also interested in this topic and are going to study irreversible investment valuation in the second part of this dissertation. An extensive literature investigates the irreversible investment problem under uncertainty Despite a high reputation in academics, the real options theory is not widely adopted by corporate managers and practitioners due to the lack of transparency and simplicity of the standard real options approaches, i.e., the contingent claim analysis and the dynamic programming method. The second part of this dissertation first develops a Shadow Net Present Value rule by using a new approach in the real options theory. The method starts with identifying the expected present value from the investment and comes to the final conclusion via representing the expected present revenue in terms of the expected present value of the running supremum of the shadow revenue of the investment. By aiming at the net profit of the investment which is the mere concern of investors, this approach thus facilitates an intuitive understanding of the real options theory and also a wider application into the practice. Meanwhile, it generalizes the elegant explicit characterization of the investment decision rule to all exponential Levy processes: The optimal investment policy is a trigger strategy such that the investment is initiated at the first time when the value of the investment project comes to a critical threshold. As two extensions, this technique is then applied to two more complicated and practical models taking into consideration gradual capacity generation and risk neutrality, respectively. In each model, both qualitative and quantitative analysis is given on the investment feature and its relationship with related parameters.</p

Essays in Robust and Data-Driven Risk Management

Risk defined as the chance that the outcome of an uncertain event is different than expected. In practice, the risk reveals itself in different ways in various applications such as unexpected stock movements in the area of portfolio management and unforeseen demand in the field of new product development. In this dissertation, we present four essays on data-driven risk management to address the uncertainty in portfolio management and capacity expansion problems via stochastic and robust optimization techniques.The third chapter of the dissertation (Portfolio Management with Quantile Constraints) introduces an iterative, data-driven approximation to a problem where the investor seeks to maximize the expected return of his/her portfolio subject to a quantile constraint, given historical realizations of the stock returns. Our approach involves solving a series of linear programming problems (thus) quickly solves the large scale problems. We compare its performance to that of methods commonly used in finance literature, such as fitting a Gaussian distribution to the returns. We also analyze the resulting efficient frontier and extend our approach to the case where portfolio risk is measured by the inter-quartile range of its return. Furthermore, we extend our modeling framework so that the solution calculates the corresponding conditional value at risk CVaR) value for the given quantile level.The fourth chapter (Portfolio Management with Moment Matching Approach) focuses on the problem where a manager, given a set of stocks to invest in, aims to minimize the probability of his/her portfolio return falling below a threshold while keeping the expected portfolio returnno worse than a target, when the stock returns are assumed to be Log-Normally distributed. This assumption, common in finance literature, creates computational difficulties. Because the portfolio return itself is difficult to estimate precisely. We thus approximate the portfolio re-turn distribution with a single Log-Normal random variable by the Fenton-Wilkinson method and investigate an iterative, data-driven approximation to the problem. We propose a two-stage solution approach, where the first stage requires solving a classic mean-variance optimization model, and the second step involves solving an unconstrained nonlinear problem with a smooth objective function. We test the performance of this approximation method and suggest an iterative calibration method to improve its accuracy. In addition, we compare the performance of the proposed method to that obtained by approximating the tail empirical distribution function to a Generalized Pareto Distribution, and extend our results to the design of basket options.The fifth chapter (New Product Launching Decisions with Robust Optimization) addresses the uncertainty that an innovative firm faces when a set of innovative products are planned to be launched a national market by help of a partner company for each innovative product. Theinnovative company investigates the optimal period to launch each product in the presence of the demand and partner offer response function uncertainties. The demand for the new product is modeled with the Bass Diffusion Model and the partner companies\u27 offer response functions are modeled with the logit choice model. The uncertainty on the parameters of the Bass Diffusion Model and the logic choice model are handled by robust optimization. We provide a tractable robust optimization framework to the problem which includes integer variables. In addition, weprovide an extension of the proposed approach where the innovative company has an option to reduce the size of the contract signed by the innovative firm and the partner firm for each product.In the sixth chapter (Log-Robust Portfolio Management with Factor Model), we investigate robust optimization models that address uncertainty for asset pricing and portfolio management. We use factor model to predict asset returns and treat randomness by a budget of uncertainty. We obtain a tractable robust model to maximize the wealth and gain theoretical insights into the optimal investment strategies

Quantifying Flexibility Real Options Calculus

We expose a real options theory as a tool for quantifying the value of the operating flexibility of real assets. Additionally, we have pointed out that this theory is an appropriated methodology for determining optimal operating policies, and provide an example of successful application of our approach to power industries, specifically to valuate the power plant of electricity. In particular by increasing the volatility of prices will eventually lead to higher assets values.real options, Black-Scholes Approach, Wiener processes, stochastic processes, Quantifying Flexibility, volatility

Optimal Trading of a Storable Commodity via Forward Markets

A commodity market participant trading via her inventory has access to both spot and forward markets. To liquidate her inventory, she can sell at the spot price, take a short forward position, or do a combination of both. A trade is proposed in which there is always a hedging forward contract, which can be considered a dynamic cash and carry arbitrage. The trader can adjust the maturity of the forward contract dynamically until the inventory is depleted or a time constraint is reached. In the first setup, the storage contract (to carry inventory) is assumed to have a constant cost and a flexible duration. The risk and return characteristics of an Approximate Dynamic Programming (ADP) and a Forward Dynamic Optimization solution are compared. The trade is contrasted with optimal spot sale among other alternative liquidation strategies. Independent from the underlying stochastic forward price model, it is proved and verified numerically that a partial sale strategy is not optimal. The optimally selected forward maturities are limited to the subset comprising the immediate, next, and last timesteps. Under a more realistic storage contract, which assumes a stochastic cost and a fixed duration, a new ADP approach is developed. The optimal policy shows the tanker rent decision is accompanied by a buy order since the loss from an empty tanker is more than the gain of renting it cheaply yet early. Given the nonadjustable duration of the rent contract, a longer contract generates a higher value by benefiting from a tanker refill option

Production and Spatial Distribution of Switchgrass and Miscanthus in the United States under Uncertainty and Sunk Cost

The U.S. cellulosic biofuel mandate has not been enforced in recent years. Uncertainty surrounding the enforcement of the mandate in addition to high production and harvest cost have contributed to a delay in the widespread planting of bioenergy crops such as switchgrass and miscanthus. Previous literature has shown that under uncertainty and sunk cost, an investment threshold is further increased due to the value associated from holding the investment option. In this paper, we extend the previous literature by applying a real option switching model to bioenergy crop production. First, we calculate the county-level break-even price which triggers a switching away from traditional field crops (corn, soybeans, and wheat) to bioenergy crops under various scenarios differing by commodity prices, production cost and biomass price expectations. We show that the resulting break-even prices at the county-level can be substantially higher than previously estimated due to the inclusion of the option value. In a second step, we identify counties that are most likely to grow switchgrass or miscanthus by simulating a stochastic biomass price over time. Our results highlight two issues: First, switchgrass or miscanthus are not grown in the Midwest under any scenario. Under low agricultural residue removal rates, biomass crops are mostly grown in the Southeast. Second, under the assumption of a high removal rates, bioenergy crops are not grown anywhere in the U.S. since the cellulosic biofuel mandate can be covered by agricultural residues

Integrated Optimization of Procurement, Processing and Trade of Commodities in a Network Environment

We consider the integrated optimization problem of procurement, processing and trade of commodities over a network in a multiperiod setting. Motivated by the operations of a prominent commodity processing firm, we model a firm that operates a star network with multiple locations at which it can procure an input commodity and has processing capacity at a central location to convert the input into a processed commodity. The processed commodity is sold using forward contracts, while the input itself can be traded at the end of the horizon. We show that the single-node version of this problem can be solved optimally when the procurement cost for the input is piecewise linear and convex, and derive closed form expressions for the marginal value of input and output inventory. However, these marginal values are hard to compute because of high dimensionality of the state space and we develop an efficient heuristic to compute approximate marginal values. We also show that the star network problem can be approximated as an equivalent single node problem and propose heuristics for solving the network problem. We conduct numerical studies to evaluate the performance of both the single node and network heuristics. We find that the single node heuristics are near-optimal, capturing close to 90% of the value of an upper bound on the optimal expected profits. Approximating the star network by a single node is effective, with the gap between the heuristic and upper bound ranging from 7% to 14% for longer planning horizonshttp://deepblue.lib.umich.edu/bitstream/2027.42/55417/1/1095-Anupindi.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/55417/4/1095-Anupindi_2010.pd