Linear Pricing Research Articles

Optimal granularity for portfolio choice

Abstract Many optimization-based portfolio rules fail to beat the simple 1/N rule out-of-sample because of parameter uncertainty. In this paper we suggest a grouping strategy in which we first form groups of equally weighted stocks and then optimize over the resulting groups only. This strategy aims at balancing the trade-off between the benefits from optimization and the losses from estimation risk. We rely on Monte-Carlo simulations to illustrate the performance of the strategy, and we derive the optimal group size for a simplified setup. Furthermore, we show that estimation risk also has an impact via the criterion by which the assets are sorted into groups (like the expected excess returns or betas), but does not negate the grouping approach. We relate our work to linear asset pricing models, and we conduct out of sample back-tests in order to confirm the validity of our grouping strategy empirically.

An Energy Imbalance Settlement Mechanism Considering Decision-Making Strategy of Retailers Under Renewable Portfolio Standard

Before the establishment of the spot electricity market in China, an energy imbalance settlement (EIS) mechanism has been proposed as a transitional solution to address the imbalances between actual consumption and contracted energy in the forward electricity market. It plays a crucial role in cultivating electricity retailers, maintaining the stability of the balancing account, and promoting a smooth transition to market-oriented reforms. Given this background, a novel EIS mechanism with a piecewise linear penalty pricing scheme is proposed, learning from the performance-based regulation (PBR) in distribution systems. For optimizing the parameters in the proposed EIS mechanism, a stochastic bilevel model is presented considering two kinds of stakeholders involved, namely the power exchange (PX) and retailers. In the upper-level model, an optimal parameter setting model for policymakers to minimize the variance of deposit in the balancing account of PX is presented. In the lower-level model, a decision-making tool for retailers under the renewable portfolio standard (RPS) is developed. As a self-balance measure of retailers, flexible demands are incorporated into the lower-level problem. Consumer psychology is applied to quantify customer consumption adjustments in response to the financial incentives given by retailers. The risk faced by the retailers is modeled using conditional value-at-risk (CVaR), taking into account the uncertainties associated with renewable energy production and customer demand. Simulation results of a provincial electricity market in China show that the proposed method can effectively motivate retailers to improve their imbalance management capability and assist policymakers in determining the parameters of the EIS mechanism. Besides, the proposed method provides insights into the impacts of parameter setting of the EIS mechanism on the behavior and performance of retailers.

Linear price equilibria in a non-exclusive insurance market

We consider a competitive insurance market in which agents can privately enter into multi-contractual insurance relationships and undertake hidden actions. We study the existence of a linear equilibrium when insurance companies have no restrictions on their pricing rules. For CARA utility functions, we show that a linear equilibrium always exists. For DARA utility functions, we provide sufficient conditions under which a linear equilibrium exists. We show that the linear equilibrium is unique, actuarially unfair, and induces partial insurance coverage. Lastly, we show that the linear equilibrium is always third-best efficient.

Relationship between liveweights, linear body measurements and cost prices of small ruminants sold in and around mubi environs, adamawa state, Nigeria

This study was conducted to investigate the relationship between body length BL heights at withers HW girth circumference GC Height at rump HR abdominal circumference AC neck length NL neck circumference NC with live weights and nbsp cost nbsp prices nbsp of nbsp animals nbsp Data nbsp obtained nbsp were nbsp subjected nbsp to nbsp Analysis nbsp of nbsp Variance nbsp ANOVA Multiple Correlation and Regression Analysis using SAS package Where nbsp significant nbsp differences nbsp occurred nbsp among nbsp means nbsp Duncan nbsp Multiple nbsp Range nbsp Test nbsp was used to separate them It was found that length of animal LA girth circumference GC and neck circumference NC had highly positive significant P lt influences on nbsp weight nbsp while nbsp heights nbsp at nbsp rump nbsp HR nbsp and nbsp abdominal nbsp circumference nbsp AC nbsp had nbsp significant nbsp P lt nbsp influences nbsp on nbsp weight nbsp of nbsp the nbsp animals nbsp However nbsp there nbsp was nbsp no significant P gt correlation between height at wither HW and neck length NL with nbsp weight nbsp The nbsp R nbsp value nbsp was nbsp nbsp all nbsp their nbsp F ratios nbsp were nbsp highly nbsp significant nbsp at nbsp P lt confirming the significance of these variables on the prices of the animals It could be concluded that the price of sheep and goat breeds is subject to the weight The price of sheep and goats in an open market can best be predicted from a combination of leg length and loin girth Body conditions and market demand mainly determined goat and sheep price estimation in the areas

Competition Between Linear Pricing and All-Units Discounts Under Capacity Asymmetry

In many recent abuse of dominance antitrust cases, the dominant supplier adopts pricing schemes involving conditional rebates, whereas its smaller competitors often use simple linear pricing. We provide a game-theoretic justification for the observed asymmetry in pricing practices by studying a model in which a supplier with full capacity faces a capacity-constrained rival. The asymmetry in capacity between the suppliers (or more generally be interpreted as factors such as distribution channels or ability to access to buyers), which gives rise to the “non-contestable” market, allows the dominant supplier to take advantage of the quantity commitment through all-units discounts while the capacity-constrained rival is induced to offer simple linear pricing.

A Matter of Equality: Linear Pricing in Combinatorial Exchanges

Combinatorial exchanges that allow for package offers to address nonconvexities in demand or supply typically employ linear and anonymous prices because they are simple, tractable, and fair. Despite their prevalence, linear anonymous prices do not necessarily correspond to Walrasian competitive equilibrium prices in such settings, and their impact is not well understood. This paper is the first to analyze the effect of different pricing rules on the efficiency of combinatorial exchanges, using both analytic methods and numerical experiments. Our analysis is motivated by a combinatorial fishery-rights exchange designed to reform the fishing industry in New South Wales (NSW), Australia. We find that when linearity and anonymity are required for only one side of the market, the average efficiency loss is negligible. In contrast, with a single linear price vector for both sides, the efficiency loss is substantial, especially when the market is small. In a formal model, we show that efficiency losses decrease when the number of buyers grows or the size of the submitted packages decreases. Besides the reform of the NSW fishing industry, our results have important implications for other cap-and-trade programs as well as other industries where demand or cost complementarities play a role.

An Efficient Incentive Mechanism for Device-to-Device Multicast Communication in Cellular Networks

With a growing demand for mobile data usage, cellular networks are facing the challenge of severe traffic overload. Device-to-Device (D2D) multicast communication, a proximity communication technique that leverages the spatial–temporal locality of mobile data usage to achieve one-to-many simultaneous transmission, provides an efficient solution to offloading heavy traffic. However, D2D multicast communication relies on users’ sharing behavior, and multicasting data incur costs such as energy, which prevents the popularity of such user-driven technique. Thus, in this paper, we study the problem of incentive design for promoting D2D multicast communication in cellular networks. Specifically, we propose a contract-based incentive mechanism to optimize the operator’s expected profit from motivating D2D multicast communication for content sharing with guaranteed service quality. We consider both complete and incomplete information scenarios. The proposed mechanism can provide efficient incentives under information asymmetry by delivering contracts, which satisfy nice properties such as individual rationality and incentive compatibility . Greedy algorithms with low complexity are developed based on local optimization to obtain fast solutions for contract design. A Lagrange multiplier method based iterative algorithm that can be proved to obtain optimal contracts under information asymmetry is also proposed. Numerical results show that the proposed mechanism can handle information asymmetry better and has a better performance than linear and step pricing schemes, increasing the expected profit by up to 2.49 times and 1.8 times, respectively.

Too good to be true? Fallacies in evaluating risk factor models

This paper is concerned with statistical inference and model evaluation in possibly misspecified and unidentified linear asset pricing models estimated by maximum likelihood. Strikingly, when spurious factors (that is, factors that are uncorrelated with the returns on the test assets) are present, the model exhibits perfect fit, as measured by the squared correlation between the model’s fitted expected returns and the average realized returns. Furthermore, factors that are spurious are selected with high probability, and factors that are useful are driven out of the model. While ignoring potential misspecification and lack of identification can be very problematic for models with macroeconomic factors, empirical specifications with traded factors (e.g., Fama and French, 1993; Hou et al., 2015) do not suffer from the identification problems shown in this study.

Thousands of Alpha Tests

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation.

Analysis of renewable generation’s integration using multi-objective fashion for multistage distribution network expansion planning

In this paper, the integration of intermittent distributed generation (wind and solar) is dealt with by proposing a multi-objective multistage distribution network expansion planning formulation based on ε-constraint method. This approach allows the planners to obtain pareto-optimal solutions based on the two objective functions: (1) installation costs of distributed generation, substations, feeders, maintenance costs of assets, costs of energy purchased from substations, cost of unserved energy and (2) utilization of renewable distributed generation. To represent the intermittent nature of load and renewable distributed generation, several time blocks are utilized, and its effectiveness is compared with an existing approach. The overall problem is formulated as a mixed-integer linear programming and a linear equation-based substation price is utilized within the optimization framework to reduce the size of the problem. The proposed method is applied to the modified 24-nodes test network for different cases to analyse the utilization of renewable distributed generation from different point of views and it is inferred from the results that the proposed model yields a variety of planning solutions which are better to increase the utilization of renewable distributed generation.

Price effects of steel commodities on worldwide stock market returns

We analyze the price effects of steel commodities on stock market returns in emerging and developed economies. These commodities have recently attained increased media exposure due to the rise in the U.S. steel import tariffs, which pose the threat of reducing global demand for steel products and, consequently, lowering prices abroad. However, little has been investigated on the impact of steel commodity prices on worldwide stock market returns. By performing structural VAR and GARCH techniques on a weekly-frequency time series from 2002 to 2015, we find positive and statistically significant effects of linear and non-linear steel commodity price shocks on real stock returns in the commodity markets. In the highly diversified financial markets such as U.S. and Germany, real stock returns do not significantly respond to steel commodity price shocks, although we find highly significant positive responses from developed economies such as Australia, Japan and South Korea. Results are robust to different model specifications. Our evidence suggests that higher tariffs on steel imports represent a larger disadvantage to commodity markets which are more largely impacted by steel commodity prices. We provide economic policy implications based on recent literature.

On the Efficiency of Nash Equilibria in Aggregative Charging Games

Several works have recently suggested to model the problem of coordinating the charging needs of a fleet of electric vehicles as a game, and have proposed distributed algorithms to coordinate the vehicles towards a Nash equilibrium of such game. However, Nash equilibria have been shown to posses desirable system-level properties only in simplified cases. In this work, we use the concept of price of anarchy to analyze the inefficiency of Nash equilibria when compared to the social optimum solution. More precisely, we show that i) for linear price functions depending on all the charging instants, the price of anarchy converges to one as the population of vehicles grows; ii) for price functions that depend only on the instantaneous demand, the price of anarchy converges to one if the price function takes the form of a positive pure monomial; iii) for general classes of price functions, the asymptotic price of anarchy can be bounded. For finite populations, we additionaly provide a bound on the price of anarchy as a function of the number vehicles in the system. We support the theoretical findings by means of numerical simulations.

Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments

Abstract In this paper we address the question of how individuals form expectations and invent, reinforce, and update their forecasting rules in a complex world. We do so by fitting a novel, parsimonious, and empirically validated genetic algorithm learning model with explicit heterogeneity in expectations to a set of laboratory experiments. Agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behavior; (2) explanation of individual and aggregate behavior in three different experimental settings, (3) improved one- and 50-period ahead forecasting of experiments, and (4) characterization of the mean, median, and empirical distribution of forecasting heuristics. The median of the distribution of GA forecasting heuristics can be used in designing or validating simple Heuristic Switching Models.

Liquidity Fluctuations and the Latent Dynamics of Price Impact

Market liquidity is a latent and dynamic variable. We propose a dynamical linear price impact model at high-frequency in which the price impact coefficient is a product of a daily, a diurnal, and an auto-regressive stochastic intraday component. We estimate the model using a Kalman filter on order book data for stocks traded on the NASDAQ in 2016. We show that our price changes estimates conditional on order flow imbalance explain, on average, $82\%$ of real price changes variance. Evidence is also provided on the fact that the conditioning on filtered information improves the estimate of the \text{LOB} liquidity with respect to the one obtained from a static estimation of the price impact. In addition, an out-of-sample analysis shows that our model provides a superior out-of-sample forecast of price impact with respect to historical estimates.

An efficient ascending auction for private valuations

Known dynamic implementations of the Vickrey–Clarke–Groves mechanism in general private-value auction settings utilize non-linear (not additively-separable over goods) and non-anonymous (bidder-specific) prices. The need for non-linear and non-anonymous prices — a complication that is often difficult to implement in practice — arises from limiting attention to elicitation processes based on demand queries (i.e., asking bidders to report their demands at posted prices). In this paper, we relax this restriction and allow the auctioneer to supplement demand queries with marginal value queries (i.e., requests to report value differences between pairs of commodity bundles) as needed. This added flexibility enables an iterative ascending auction design that achieves efficiency despite using linear and anonymous prices.

Optimal Fund Menus

We study the optimal design of a menu of funds by a manager who is required to use linear pricing and does not observe the preferences of investors regarding one of the risky assets. The optimal menu involves bundling of assets and can be explicitly constructed from the solution to a calculus of variations problem that optimizes over the indirect utility that each type of investor receives. We provide a complete characterization of the optimal fund menu and show that the need to maintain incentive compatibility leads the manager to behave as a closet indexer by offering funds that are inefficiently tilted towards the asset which is not subject to the information friction.

Economic and environmental analysis of using metal-oxides/water nanofluid in photovoltaic thermal systems (PVTs)

In the present study, an experimental setup for a PVT system is designed and fabricated to investigate the economic and environmental aspects of using different nano-oxides/water fluids. Selected coolant fluids are: pure water, ZnO/water, Al2O3/water and TiO2/water nanofluids. To make nanofluids, nanoparticles are dispersed in distilled water by 0.2% weight fractions (wt%), separately. Based on the experimental results the amount of annual emission reduction and cost saving are investigated for different coolants. Furthermore, the payback period of the PVTs is compared with that of a conventional PV unit. The results are presented in five different scenarios due to increasing of the electricity price, up to the final price and the government subsidies for the renewable energy sources (1st: 10% annual price increasing, with no subsidy, 2nd: linear increasing price, with no subsidy, 3rd: 10% the annual price increasing, 50% subsidy, 4th: linear price increasing, 50% subsidy and 5th: linear price increasing, 75% subsidy). From the energy viewpoint, by using pure water, PVT/TiO2, PVT/ZnO and PVT/Al2O3, the size reduction of the PVT system in comparison with that of the PV is 21, 32, 33 and 24%, respectively, and from the exergy viewpoint these values are about 5, 6, 7, 6%, respectively.

Adaptive-Price Combinatorial Auctions

This work introduces a novel iterative combinatorial auction that aims to achieve both high efficiency and fast convergence across a wide range of valuation domains. We design the first fully adaptive-price combinatorial auction that gradually extends price expressivity as the rounds progress. We implement our auction design using polynomial prices and show how to detect when the current price structure is insufficient to clear the market, and how to correctly expand the polynomial structure to guarantee progress. An experimental evaluation confirms that our auction is competitive with bundle-price auctions in regimes where these excel, namely multi-minded valuations, but also performs well in regimes favorable to linear prices, such as valuations with pairwise synergy.

Nonlinear Pricing: Evidence of Price Discrimination in the Fluid Milk Market

Firms may use nonlinear price schedules, possibly to the detriment of some consumers, to price discriminate and increase profits. However, it is possible that these nonlinear price schedules may also increase welfare from the consumer's perspective if they are able to serve portions of a market that firms may otherwise ignore. The more extensive theoretical literature on price discrimination shows how firms may use nonlinear price schedules to price discriminate by distorting product characteristics from their efficient levels given heterogeneous preferences among consumers. Additionally, the theoretical literature finds that whether or not this practice is welfare decreasing or increasing from the consumer's perspective depends on a number of factors, thus implying that the welfare implications of price discrimination from the consumer's perspective is an empirical question. Subsequently, some recent empirical papers have found this form of second-degree price discrimination to be welfare decreasing from the consumer's perspective whereas other papers, depending on the analyzed market, have found it to be welfare increasing. In this paper, I study the consumer welfare implications of nonlinear pricing in the market of fluid milk. Variation in the ratio between marginal price per ounce and marginal cost per ounce over the menu of options is evidence that firms are using nonlinear pricing schedules to price discriminate. Once this evidence is shown, I use counterfactual analysis to determine the welfare implications of this practice within the market for fluid milk from the consumer's perspective. I find that firms within this industry are using nonlinear pricing to price discriminate for private label products, of which conservative estimates show that approximately 7.20% of the markup differential between sizes can be explained by price discrimination. However, I find that very little of the markup on Regional branded products, approximately 0.32%, can be attributed to price discrimination. In my most compelling set of counterfactual analyses, I show that consumers are worse off due to price discrimination and my counterfactual analysis shows that households are on average anywhere between $0.0249 to $.0555 to worse off per shopping trip under a forced linear pricing, which corresponds to a total yearly welfare difference between $63 million to $140 million per year within my sample region due to the nonlinear pricing strategies used by firms in the marketplace. Additionally, I estimate and utilize models similar to the status quo in the literature on price discrimination thus far that do not have household demographic characteristics included and compare them my preferred specification which does, showing that ignoring the demographic makeup of the marketplace's individuals and actual consumer decisions leads to potentially biased parameter estimates.

Linear triangular optimization technique and pricing scheme in residential energy management systems

Abstract This paper presents a new linear optimization algorithm for power scheduling of electric appliances. The proposed system is applied in a smart home community, in which community controller acts as a virtual distribution company for the end consumers. We also present a pricing scheme between community controller and its residential users based on real-time pricing and likely block rates. The results of the proposed optimization algorithm demonstrate that by applying the anticipated technique, not only end users can minimise the consumption cost, but it can also reduce the power peak to an average ratio which will be beneficial for the utilities as well.