Breaking the Cycle: How the News and Markets Created a Negative Feedback Loop in COVID-19
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
Adapted from “Global Value Chains in Developing Countries: A Relational Perspective from Coffee and Garments,” by Laura Boudreau of Columbia Business School, Julia Cajal Grossi of the Geneva Graduate Institute, and Rocco Macchiavello of the London School of Economics.
Adapted from “Online Advertising as Passive Search,” by Raluca M. Ursu of New York University Stern School of Business, Andrey Simonov of Columbia Business School, and Eunkyung An of New York University Stern School of Business.
This paper from Columbia Business School, “Meaning of Manual Labor Impedes Consumer Adoption of Autonomous Products,” explores marketing solutions to some consumers’ resistance towards autonomous products. The study was co-authored by Emanuel de Bellis of the University of St. Gallen, Gita Johar of Columbia Business School, and Nicola Poletti of Cada.
Co-authored by John B. Donaldson of Columbia Business School, “The Macroeconomics of Stakeholder Equilibria,” proposes a model for a purely private, mutually beneficial financial agreement between worker and firm that keeps decision-making in the hands of stockholders while improving the employment contract for employees.
At Columbia Business School, our faculty members are at the forefront of research in their respective fields, offering innovative ideas that directly impact the practice of business today. A quick glance at our publication on faculty research, CBS Insights, will give you a sense of the breadth and immediacy of the insight our professors provide.
As a student at the School, this will greatly enrich your education. In Columbia classrooms, you are at the cutting-edge of industry, studying the practices that others will later adopt and teach. As any business leader will tell you, in a competitive environment, being first puts you at a distinct advantage over your peers. Learn economic development from Ray Fisman, the Lambert Family Professor of Social Enterprise and a rising star in the field, or real estate from Chris Mayer, the Paul Milstein Professor of Real Estate, a renowned expert and frequent commentator on complex housing issues. This way, when you complete your degree, you'll be set up to succeed.
Columbia Business School in conjunction with the Office of the Dean provides its faculty, PhD students, and other research staff with resources and cutting edge tools and technology to help push the boundaries of business research.
Specifically, our goal is to seamlessly help faculty set up and execute their research programs. This includes, but is not limited to:
All these activities help to facilitate and streamline faculty research, and that of the doctoral students working with them.
An optimal coupling is a bivariate distribution with specified marginals achieving maximal correlation. We show that optimal couplings are totally positive and, in fact, satisfy a strictly stronger condition we call the nonintersection property. For discrete distributions we illustrate the equivalence between optimal coupling and a certain transportation problem. Specifically, the optimal solutions of greedily-solvable transportation problems are totally positive, and even nonintersecting, through a rearrangement of matrix entries that results in a Monge sequence.
This paper surveys the literature on option pricing, from its origins to the present. An extensive review of valuation methods for European- and American-style claims is provided. Applications to complex securities and numerical methods are surveyed. Emphasis is placed on recent trends and developments in methodology and modeling.
This paper considers an overbooking problem with multiple reservation and inventory classes, in which the multiple inventory classes may be used as substitutes to satisfy the demand of a given reservation class (perhaps at a cost). The problem is to jointly determine overbooking levels for the reservation classes, taking into account the substitution options. Such problems arise in a variety of revenue management contexts, including multicabin aircraft, back-to-back scheduled flights on the same leg, hotels with multiple room types, and mixed-vehicle car rental fleets.
We analyze an airline yield management problem on a single flight leg in which the buyers' choice of fare classes is modeled explicitly. The choice model we use is very general and includes a wide range of discrete choice models of practical interest. The optimization problem is to find, at each point in time, the optimal subset of fare classes to offer. We characterize the optimal policy for this problem exactly and show it has a surprisingly simple form.
Any simulation procedure has difficulty achieving accuracy for rare events that lie in the tails of the probability distributions one is simulating from. But that is where the outcomes that produce defaults in a credit portfolio occur, making pricing and risk management for CDOs and similar instruments difficult and (computer) time-consuming. In this article, Glasserman introduces several approximation procedures for estimating the tails of the distribution of default risk exposure for a credit portfolio.
This paper characterizes the arbitrage-free dynamics of interest rates, in the presence of both jumps and diffusion, when the term structure is modeled through simple forward rates (i.e., through discretely compounded forward rates evolving continuously in time) or forward swap rates.
Many telephone call centers that experience cyclic and random customer demand adjust their staffing over the day in an attempt to provide a consistent target level of customer service. The standard and widely used staffing method, which we call the stationary independent period by period (SIPP) approach, divides the workday into planning periods and uses a series of stationary independent Erlang-c queuing models—one for each planning period—to estimate minimum staffing needs.
Many telephone call centers that experience cyclic and random customer demand adjust their staffing over the day in an attempt to provide a consistent target level of customer service. The standard and widely used staffing method, which we call the stationary independent period by period (SIPP) approach, divides the workday into planning periods and uses a series of stationary independent Erlang-c queuing models—one for each planning period—to estimate minimum staffing needs.
This paper develops formulas for pricing caps and swaptions in LIBOR market models with jumps. The arbitrage-free dynamics of this class of models were characterized in Glasserman and Kou [9] in a framework allowing for very general jump processes. For computational purposes, it is convenient to model jump times as Poisson processes; however, the Poisson property is not preserved under the changes of measure commonly used to derive prices in the LIBOR market model framework.
Abstract: In many of the numerical methods for pricing American options based on the dynamic programming approach, the most computationally intensive part can be formulated as the summation of Gaussians. Though this operation usually requires O(NN') work when there are N' summations to compute and the number of terms appearing in each summation is N, we can reduce the amount of work to O(N+N') by using a technique called the fast Gauss transform.
This paper is concerned with dynamic control of stochastic processing networks. Specifically, it follows the so called heavy traffic approach, where a Brownian approximating model is formulated, an associated Brownian optimal control problem is solved, the solution of which is then used to define an implementable policy for the original system. A major challenge is the step of policy translation from the Brownian to the discrete network. This paper addresses this problem by defining a general and easily implementable family of continuous-review tracking policies.
For many years, average bed occupancy level has been the primary measure that has guided hospital bed capacity decisions at both policy and managerial levels. Even now, the common wisdom that there is an excess of beds nationally has been based on a federal target of 85% occupancy that was developed about 25 years ago. This paper examines data from New York sate and uses queueing analysis to estimate bed unavailability in intensive care units (ICUs) and obstetrics units. Using various patient delay standards, units that appear to have insufficient capacity are identified.
This paper considers pricing and capacity sizing decisions, in a single-class Markovian model motivated by communication and information services. The service provider is assumed to operate a finite set of processing resources that can be shared among users; however, this shared mode of operation results in a service-rate degradation. Users, in turn, are sensitive to the delay implied by the potential degradation in service rate, and to the usage fee charged for accessing the system.
This paper considers pricing and capacity sizing decisions, in a single-class Markovian model motivated by communication and information services. The service provider is assumed to operate a finite set of processing resources that can be shared among users; however, this shared mode of operation results in a service-rate degradation. Users, in turn, are sensitive to the delay implied by the potential degradation in service rate, and to the usage fee charged for accessing the system.
We consider a two-echelon distribution system in which a supplier distributes a product to N competing retailers. The demand rate of each retailer depends on all of the retailers' prices, or alternatively, the price each retailer can charge for its product depends on the sales volumes targeted by all of the retailers. The supplier replenishes his inventory through orders (purchases, production runs) from an outside source with ample supply. From there, the goods are transferred to the retailers.
The Pfaat protein family alignment annotation tool is a Java-based multiple sequence alignment editor and viewer designed for protein family analysis. The application merges display features such as dendrograms, secondary and tertiary protein structure with SRS retrieval, subgroup comparison, and extensive user-annotation capabilities.
Motivated by the problem of efficient estimation of expected cumulative rewards or cashflows, this paper proposes and analyzes a variance reduction technique for estimating the expectation of the sum of sequentially simulated random variables. In some applications, simulation effort is of greater value when applied to early time steps rather than shared equally among all time steps; this occurs, for example, when discounting renders immediate rewards or cashflows more important than those in the future. This suggests that deliberately stopping some paths early may improve efficiency.
This paper develops efficient methods for computing portfolio value-at-risk (VAR) when the underlying risk factors have a heavy-tailed distribution. In modeling heavy tails, we focus on multivariate t distributions and some extensions thereof. We develop two methods for VAR calculation that exploit a quadratic approximation to the portfolio loss, such as the delta-gamma approximation. In the first method, we derive the characteristic function of the quadratic approximation and then use numerical transform inversion to approximate the portfolio loss distribution.
The results of an analysis of sales and price data from a speciality retailer of women's apparel are reported. The data set contains 184 styles sold during the Spring 1993 season. A demand model similar to those in the existing literature is hypothesised, fit to the data, and then analysed to obtain estimates of revenues under various pricing policies. Both full information and adaptive policies are considered. The optimal prices suggested by the models are compared with those of the study company and the revenues generated by various policies are estimated.
We consider the problem of managing inventories and dynamically adjusting retailer prices in distribution systems with geographically dispersed retailers. More specifically, we analyze the following single item, periodic review model. The distribution of demand in each period, at a given retailer, depends on the item's price according to a stochastic demand function. These stochastic demand functions may vary by retailer and by period. The replenishment process consists of two phases: In some or all periods, a distribution center may place an order with an outside supplier.
We analyze a dynamic auction, in which a seller with C units to sell faces a sequence of buyers separated into T time periods. Each group of buyers has independent, private values for a single unit. Buyers compete directly against each other within a period, as in a traditional auction, and indirectly with buyers in other periods through the opportunity cost of capacity assessed by the seller. The number of buyers in each period, as well as the individual buyers' valuations, are random.
We analyze a dynamic auction, in which a seller with C units to sell faces a sequence of buyers separated into T time periods. Each group of buyers has independent, private values for a single unit. Buyers compete directly against each other within a period, as in a traditional auction, and indirectly with buyers in other periods through the opportunity cost of capacity assessed by the seller. The number of buyers in each period, as well as the individual buyers' valuations, are random.
We analyze a dynamic auction, in which a seller with C units to sell faces a sequence of buyers separated into T time periods. Each group of buyers has independent, private values for a single unit. Buyers compete directly against each other within a period, as in a traditional auction, and indirectly with buyers in other periods through the opportunity cost of capacity assessed by the seller. The number of buyers in each period, as well as the individual buyers' valuations, are random.
Supply chain management is a complex process that requires a high-level dedicated governing body or steering committee to meet the various needs of the supply chain's multiple customers. Because a supply chain system is complex and nonlinear, there may be multiple reasons for poor performance. Supply chains can fall victim to feedback loops that reinforce negative actions and behaviors. Improving a supply chain requires understanding functional deficiencies and, also, how such deficiencies interact with one another to degrade overall performance.
We investigate the security of quantum key distribution with entangled photons, focusing on the two-photon variation of the Bennett-Brassard 1984 (BB84) protocol proposed in 1992 by Bennett, Brasard, and Mermin (BBM92). We present a proof of security which applies to realistic sources, and to untrustable sources which can be placed outside the labs of the two receivers. The proof is restricted to individual eavesdropping attacks, and assumes that the detection apparatus is trustable.
Objective. To develop insights on the impact of size, average length of stay, variability, and organization of clinical services on the relationship between occupancy rates and delays for beds. Data Sources. The primary data source was Beth Israel Deaconess Medical Center in Boston. Secondary data were obtained from the United Hospital Fund of New York reflecting data from about 150 hospitals.
We address multi-item inventory systems with random and seasonally fluctuating, and possibly correlated, demands. The items are produced in two stages, each with its own lead-time; in the first stage a common intermediate product is manufactured. The production volumes in the first stage are bounded by given capacity liits. We develop an accurate lower bound and close-to-optimal heuristic strategies of simple structure. The gap between them, evaluated in an extensive numerical study, is on average only 0.45%.
The subject of this paper is autoregressive (AR) modeling of a stationary, Gaussian discrete time process, based on a finite sequence of observations. The process is assumed to admit an AR(∞) representation with exponentially decaying coefficients. We adopt the nonparametric minimax framework and study how well the process can be approximated by a finite-order AR model. A lower bound on the accuracy of AR approximations is derived, and a nonasymptotic upper bound on the accuracy of the regularized least squares estimator is established.
Pricing financial options often requires Monte Carlo methods. One particular case is that of barrier options, whose payoff may be zero depending on whether or not an underlying asset crosses a barrier during the life of the option. This paper develops variance reduction techniques that take advantage of the special structure of barrier options, and are appropriate for general simulation problems with similar structure. We use a change of measure at each step of the simulation to reduce the variance arising from the possibility of a barrier corssing at each monitoring date.
Recent papers have developed analytical models to explain and quantify the benefits of delayed differentiation and quick response programs. These models assume that while demands in each period are random, they are independent across time and their distribution is perfectly known, i.e., sales forecasts do not need to be updated as time progresses. In this paper, we characterize these benefits in more general settings, where parameters of the demand distributions fail to be known with accuracy or where consecutive demands are correlated.
This article develops precise connections among two general approaches to building interest rate models: a general equilibrium approach using a pricing kernel and the Heath, Jarrow, and Morton framework based on specifying forward rate volatilities and the market price of risk. The connections exploit the observation that a pricing kernel is uniquely determined by its drift. Through these connections we provide, for any arbitrage-free term structure model, a representative-consumer real production economy supporting that term structure model in equilibrium.
This paper evaluates the practice of determining staffing requirements in service systems with random cyclic demands by using a series of stationary queueing models. We consider Markovian models with sinusoidal arrival rates and use numerical methods to show that the commonly used "stationary independent period by period" (SIPP) approach to setting staffing requirements is inaccurate for parameter values corresponding to many real situations.
This paper evaluates the practice of determining staffing requirements in service systems with random cyclic demands by using a series of stationary queueing models. We consider Markovian models with sinusoidal arrival rates and use numerical methods to show that the commonly used "stationary independent period by period" (SIPP) approach to setting staffing requirements is inaccurate for parameter values corresponding to many real situations.
We analyze a model of inventory competition among n firms that provide competing, substitutable goods. Each firm chooses initial inventory levels for their good in a single period (newsboy-like) inventory model. Customers choose dynamically based on current availability, so the inventory levels at one firm affect the demand of all competing firms. This creates a strategic interaction among the firms' inventory decisions. Our work extends earlier work on variations of this problem by Karjalainen (1992), Lippman and McCardle (1997) and Parlar (1988).
This paper integrates pricing and replenishment decisions for the following prototypical two-echelon distribution system with deterministic demands. A supplier distributes a single product to multiple retailers, who in turn sell it to consumers. The retailers serve geographically dispersed, heterogeneous markets. The demand in each retail market arrives continuously at a constant rate, which is a general decreasing function of the retail price in the market. The supplier replenishes its inventory through orders (purchases, production runs) from a source with ample capacity.
We analyze a single-period, stochastic inventory model (newsboy-like model) in which a sequence of heterogeneous customers dynamically substitute among product variants within a retail assortment when inventory is depleted. The customer choice decisions are based on a natural and classical utility maximization criterion. Faced with such substitution behavior, the retailer must choose initial inventory levels for the assortment to maximize expected profits.
This paper describes,analyzes and evaluates an algorithm for estimating portfolio loss probabilities using Monte Carlo simulation. Obtaining accurate estimates of such loss probabilities is essential to calculating value-at-risk,which is a quantile of the loss distribution. The method employs a quadratic ("delta-gamma") approximation to the change in portfolio value to guide the selection of effective variance reduction techniques; specifically importance sampling and stratified sampling. If the approximation is exact,then the importance sampling is shown to be asymptotically optimal.
This paper describes a general approach for dynamic control of stochastic networks based on fluid model analysis, where in broad terms, the stochastic network is approximated by its fluid analog, an associated fluid control problem is solved and, finally, a scheduling rule for the original system is defined by itnerpreting the fluid control policy.
This paper proposes and anlyzes discrete-time approximations to a class of diffusions, with an emphasis on preserving certain important features of the continuous-time processes in the approximations. We start with multivariate diffusions having three features in particular: they are martingales, each of their components evolves within the unit interval, and the components are almost surely ordered.
We investigate a simple adaptive approach to optimizing seat protection levels in airline revenue management systems. The approach uses only historical observations of the relative frequencies of certain seat-filling events to guide direct adjustments of the seat protection levels in accordance with the optimality conditions of Brumelle and McGill (1993). Stochastic approximation theory is used to prove the convergence of this adaptive algorithm to the optimal protection levels.
In this paper, we consider American option contracts when the underlying asset has stochastic dividends and stochastic volatility. We provide a full discussion of the theoretical foundations of American option valuation and exercise boundaries. We show how they depend on the various sources of uncertainty which drive dividend rates and volatility, and derive equilibrium asset prices, derivative prices and optimal exercise boundaries in a general equilibrium model.
An important recent development in the pricing of interest rate derivatives is the emergence of models that incorporate lognormal volatilities for forward Libor or forward swap rates while keeping interest rates stable. These market models have three attractive features: they preclude arbitrage among bonds, they keep rates positive, and, most distinctively, they price caps or swaptions according to Black's formula, thus allowing automatic calibration to market data. But these features of continuous-time formulations are easily lost when the models are discretized for simulation.
Supply chain management is the most recently proposed set of tools to replace the total quality paradigm, which itself replaced innumerable previous sets of principles and managerial tools. The fundamentals are unchanged; the principles of managing for quality are quite robust and are easily adaptable to the task of supply chain management. The most obvious element that is new about supply chain management is the unprecedented sophistication of its information technology.
Unlike European-type derivative securities, there are no simple analytic valuation formulas for finite-lived American options, even when the underlying asset price has constant volatility. The early exercise feature considerably complicates the valuation of American contracts. The strategy taken in this paper is to rely on nonparametric statistical methods using market data to estimate the call prices and the exercise boundaries. A comparison is made with parametric constant volatility model-based prices and exercise boundaries.
Consider a queue with a stochastic fluid input process modeled as fractional Brownian motion (fBM).When the queue is stable, we prove that the maximum of the workload process observed over an interval of length t grows like y(log t)1/(2-2H), where H > 1/2 is the self-similarity index (also known as the Hurst parameter) that characterizes the fBM and can be explicitly computed.
A model of molecular diversity is presented. The model, termed "Quantized Surface Complementarity Diversity" (QSCD), defines molecular diversity by measuring molecular complementarity to a fully enumerated set of theoretical target surfaces. Molecular diversity space is defined as the molecular complement to this set of enumerated surfaces. Using a set of known test compounds, the model is shown to be biologically relevant, consistently scoring known actives as similar.
Low inventory, a crucial part of just-in-time (JIT) manufacturing systems, enjoys increasing application worldwide, yet the behavioral effects of such systems remain largely unexplored. Operations research (OR) models of low-inventory systems typically use a simplifying assumption that processing times of individual workers are independent random variables. This leads to predictions that low-inventory systems will exhibit production interruptions leading to lower productivity. Yet empirical results suggest that low-inventory systems do not exhibit the predicted productivity losses.
Stochastic Economic Lot Scheduling Problems (ELSPs) involve settings where several items need to be produced in a common facility with limited capacity, under significant uncertainty regarding demands, unit production times, setup times, or combinations thereof. We consider systems where some products are made-to-stock while another product line is made-to-order. We present a rich and effective class of strategies for which a variety of cost and performance measures can be evaluated and optimized efficiently by analytical methods.
This paper deals with Markov decision processes with a countable state space. We demonstrate that a single, relatively simple condition suffices to guarantee that the value-iteration method converges and that an optimal policy can be computed via this method, once the existence of a solution to the average cost optimality equation has been established via any of the many available sets of existence conditions.
We analyze a randomized version of the deterministic linear programming (DLP) method for computing network bid prices. The method consists of simulating a sequence of realizations of itinerary demand and solving deterministic linear programs to allocate capacity to itineraries for each realization. The dual prices from this sequence are then averaged to form a bid price approximation. This randomized linear programming (RLP) method is only slightly more complicated to implement than the DLP method.