bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electricity Forward Contracts
Authors: Benth Fred E. 
Published: May 2010

Abstract
Electricity is by nature a nonstorable commodity, and this raises questions on the exact link between spot and forward/futures prices. From arbitrage theory the discounted forward price in frictionless markets is today's spot price. This is a result of the buy‐and‐hold hedging strategy, which is infeasible for electricity because of no‐storability of the power. We discuss how to obtain forward prices for electricity based on spot models and view some recent results in this regard.
Commodity Risk
Authors: Kleindorfer Paul R. 
Published: May 2010

Abstract
This article surveys the underlying theory related to the use of options and other derivative contract forms in support of commodity hedging in supply management. Such derivatives, on both capacity and output, play an important role in integrating long‐term and short‐term contracting between multiple buyers and sellers in such markets. We focus on the case of a single buyer interested in hedging commodity risk exposure through contracts and derivative instruments, the latter assumed to be defined on a competitive spot market. Sellers compete to supply the buyer in a market in which, in the short run, capacities and technologies are fixed. The buyer can reserve capacity through contracts for physical delivery obtained from any seller and the buyer can also hedge these contracts through financial contracts on the same (or correlated) underlying indices from financial intermediaries. Output on the day can be obtained either by executing such contracts or in the associated spot market. Determining optimal Value‐at‐Risk (VaR)‐constrained portfolios of sourcing and hedging options across contracts and spot market opportunities is the key problem addressed.
Commodity Trading
Authors: Falbo Paolo , Felletti Daniele , Stefani Silvana 
Published: May 2010

AbstractMean‐reverting processes find large applications in commodities and energy markets as well as in large sectors of financial markets. In this article, we face the problem of defining optimal strategies for a trader wishing to take a speculative position based on the locally predictable trend that mean‐reverting processes possess. For this purpose, we develop two different trading strategies: one optimizes the expected return for a fixed risk level (fixed risk strategy, FRS) and the other just maximizes the expected return (risk neutral—maximum return strategy, MRS). An empirical application is performed on several commodity price series.
Bid–Ask Spreads
Authors: Bessembinder Hendrik , Venkataraman Kumar 
Published: May 2010

AbstractBid‐ask spreads, which measure trade execution costs, and reflect the price concessions necessary to complete transactions quickly, are important as indicators of market quality and in determining traders' actual investment results. Execution costs arise because it is costly to provide liquidity, and can be estimated on the basis of comparisons of trade prices with proxies for underlying security value, with the most common proxy being the quote midpoint. Comparisons can be of trade prices to midpoints at or before the time of the trade, as in effective spread measures, or to midpoints after the trade, as in realized spread measures. Recent research indicates that trade execution costs have declined in US markets in recent years, and documents substantial variation in average trading costs across international equity markets.
Liquidity Premium
Authors: Amihud Yakov , Mendelson Haim 
Published: May 2010

Abstract

An asset is liquid if it can be traded quickly and at low cost. In addition to risk, liquidity affects asset prices and returns. Because investors want to be compensated for bearing the costs of illiquidity,
asset returns are increasing in illiquidity
. Thus, asset prices should depend on two asset characteristics: risk and liquidity. This article surveys research on the effects of liquidity on asset prices and returns. We find that liquidity is an important factor in capital asset pricing.
Adverse Selection
Authors: Parlour Christine A. , Rajan Uday 
Published: May 2010

Abstract
Adverse selection arises when a trader has private information related to the value of an asset. Strategic behavior by the informed trader reduces the payoff of an uninformed counterparty. In quote‐driven markets, adverse selection directly leads to a spread between bid and ask prices for the asset. In limit order markets, a limit order is subject to adverse selection, since the value of the asset may change before a market order comes in. Market orders from uninformed traders also face adverse selection, since limit orders may themselves have been submitted by informed traders.
Price Impact
Authors: Bouchaud Jean‐Philippe 
Published: May 2010

AbstractWe define what “price impact” means, and how it is measured and modeled in the recent literature. Although this notion seems to convey the idea of a forceful and intuitive mechanism, we discuss why things might not be that simple. Empirical studies show that while the correlation between signed order flow and price changes is strong, the impact of trades on prices is neither linear in volume nor permanent. Impact allows private information to be reflected in prices, but by the same token, random fluctuations in order flow must also contribute to the volatility of markets
Inventory Effects
Authors: Moulton Pamela C. 
Published: May 2010

Abstract
Traditional economic models of price‐setting focus on call‐auction markets in which all trading occurs simultaneously, at preestablished discrete times, with no market makers involved. Such models leave no role for any of the three sources of friction found in modern models of market liquidity: inventory, order‐processing costs, and adverse selection. With the development of market microstructure research, researchers studying the links between market‐maker inventories and liquidity have shed considerable light on how market makers (often modeled as dealers) resolve temporal imbalances in the continuous trading environment that characterizes most financial markets. In general, inventory models predict that market makers set ask prices above bid prices, that they lower their quotes when they have very large inventory positions, and that they may or may not change the magnitude of their quoted spreads as their inventory changes, depending on whether they are capital constrained or not. Models in which market makers face capital constraints also offer an explanation of flight to quality, in which the riskiest securities suffer the greatest liquidity declines. Multidealer inventory models predict that relative inventory positions give rise to interdealer trading and determine which dealers have the best (lowest ask or highest bid) quotes in a market. These predictions have been tested and largely borne out in empirical studies to date.
High‐Frequency Data
Authors: Lillo Fabrizio , MiccichÈ Salvatore 
Published: May 2010

AbstractWe introduce some of the most common types of high‐frequency financial data: tick‐by‐tick data, trade and quote data, order book data, and market member data. We describe the types of variables that are usually available in the most popular high‐frequency financial databases. We discuss the issues related to the handling of these data, including cleaning protocols, timing issues, and issues related to data size. We then briefly consider the issues related to the stylized facts detected in the empirical analysis of high‐frequency data. Specifically, we consider (i) the irregular temporal spacing of the events at high frequency and its relevance for the econometric modeling of financial variables, (ii) the discreteness of the financial variables under investigation, (iii) the problems related to proper definition of financial variables, (iv) their daily periodicity, that is, typical intraday patterns, (v) their temporal correlations, for example, the bid–ask bounce and long memory properties, and (vi) problems related to the specificity of the market structure and rules.
Order Flow
Authors: Lillo Fabrizio 
Published: May 2010

AbstractWe present the definition of the different components of the order flow in a double auction market characterized by a limit order book. We present some statistical regularities of order flow observed by separately considering market orders, limit orders, and cancellations. The unconditional regularities include the fat‐tailed distribution of order volume and the long memory of order signs. For limit orders and cancellations, we also present unconditional statistical regularities on the limit price of the order. We then discuss how the submission rate of different types of orders is dependent on the size of the bid–ask spread and on the depth of the market. We discuss the selective liquidity taking mechanism, that is, how market order volumes are influenced by the volume at the best. Finally, we discuss some empirical results on commonalities of order flow across a portfolio of stocks.
Probability of Informed Trading
Authors: Hvidkjaer Soeren 
Published: May 2010

Abstract

The extent of private information in markets is a critical determinant in the price formation process. By modeling the trading process and the market maker's learning process, we can use trade data to make inferences about this private information or, more precisely, about the probability of informed trading (PIN). This article presents the PIN model, shows how to estimate the model
via
maximum likelihood, and discusses applications in market microstruture and asset pricing.
Call Auction Markets
Authors: Schwartz Robert A. , Davis Paul L. 
Published: May 2010

Abstract
A call auction batches orders together for simultaneous execution in a multilateral trade, at a single price, at a single point in time. Order batching amasses liquidity, tempers price volatility, lowers execution costs for participants, and sharpens price discovery for the broad market. Accordingly, calls are being widely used to open and to close trading in electronic exchange markets (for instance, London, Frankfurt, Paris, Zurich, and Nasdaq). The New York Stock Exchange also opens and closes its markets with calls but, as of this writing, its calls are not fully electronic. Despite their desirable attributes, however, a call's ability to attract critical mass order flow is not foreordained but depends critically on its specific architectural design. This article describes how orders are handled and clearing prices are set in call auction trading. It further considers the advent of electronic calls; their use at market openings and closings to temper volatility and sharpen price discovery; and their particular importance for trading mid‐ and small‐cap stocks and for handling the large orders of institutional investors. Lastly, the properties of limit and market orders in a call auction are contrasted with their attributes in a continuous trading environment.
Specialist Markets
Authors: Saar Gideon 
Published: May 2010

AbstractA specialist market system is defined as a hybrid market structure that includes an auction component (e.g., a floor auction or a limit order book) together with one or more designated market makers (“specialists”) who trade as dealers for their own account. The designated market makers have some responsibility for the market; for example, specialists are usually obligated to continuously post quotes with reasonable depth. In return, they get a preferential treatment, be it access to order flow, information about the state of the market, discounted trading fees, or direct monetary compensation from the exchange or the listed firm.While market microstructure theory shows that the hybrid structure gives rise to complex strategic interaction between limit order traders and specialists, it also suggests that the presence of specialists is more beneficial for thinly traded stocks. Preferential treatment of specialists is required for such stocks because the potential for profitable dealing is small relative to the cost of maintaining a market presence. An interesting trend over the past two decades has been the adoption of this market structure by derivatives exchanges and stock markets around the world for the trading of less active securities. Empirical research documents improvements in market quality when these securities move from a pure limit order book structure to a hybrid specialist structure.
Simulation‐Based Estimation
Authors: Monfort Alain 
Published: May 2010

Abstract
In many dynamic financial models involving latent variables, the exact computation of the likelihood function is impossible. To define simulation‐based estimation methods, three kinds of simulations have been introduced: the unconditional simulations, the sequentially conditional simulations, and the globally conditional simulations. The estimation methods are also of three kinds: those which change the likelihood function into another objective function, like the indirect inference method, those which keep the likelihood function, and the Bayesian methods. The indirect inference method is described in some detail.
Extreme Value Theory
Authors: Deheuvels Paul 
Published: May 2010

AbstractWe describe the limiting behaviour of extreme values generated by a finite sequence (or time‐series) of random variables when the number of observations becomes increasingly large. Under the assumptions that the sequence components are independent and identically distributed, with the proper degree of distributional smoothness, the asymptotic limit laws reduce to a specific class of probability distributions. These are usually named after some of the most prominent scientists who have contributed to this theory, namely Gumbel, Fréchet and Weibull. This fundamental result is very often invoked to fit models to the distributions of extreme values from finite sets of observations, even though the latter do not necessary fulfil the required idealistic assumptions. Such techniques are hazardous, since, even when the conditions of independence and stationarity are satisfied, limiting distributions of extremes may not exist, because of insufficient smoothness of the component distributions. We discuss the statistical treatment of real‐life observations, by stressing the huge practical difficulties of selecting the right extremal models on the basis of small samples. Tail estimation through Hill‐type estimators is introduced as an example of such a statistical methodology relating to extreme values. It illustrates the numerous problems of modeling distributions with Pareto‐type components. Multivariate extremes are also evoked in relation with copulas. Our general conclusion is that, in most cases of interest, the application of extreme‐value theory is rendered highly difficult because of the unavoidable smallness of the available data sets.
Jump Processes
Authors: Eberlein Ernst 
Published: May 2010

Abstract
As a consequence of the distributional deficiencies of the classical models that are driven by Brownian motions, advanced models in finance are now based on processes that include jumps or that consist entirely of jumps. Starting from a semimartingale and Lévy process perspective, we discuss properties of jump processes such as finiteness of moments, finite and infinite activity, and finite and infinite variation. The standard examples of jump processes are discussed. This includes counting processes, compound Poisson processes as well as generalized hyperbolic, stable, and CGMY Lévy processes.
Time Change
Authors: Veraart Almut E.D. , Winkel Matthias 
Published: May 2010

Abstract
The mathematical operation of time‐changing continuous‐time stochastic processes can be regarded as a standard method for building financial models. We briefly review the theory on time‐changed stochastic processes and relate them to stochastic volatility models in finance. Popular models, including time‐changed Lévy processes, where the time‐change process is given by a subordinator or an absolutely continuous time change, are presented. Finally, we discuss the potential and the limitations of using such processes for constructing multivariate financial models.
Stochastic Volatility Models: Extremal Behavior
Authors: Klüppelberg Claudia , Lindner A. 
Published: May 2010

AbstractStochastic volatility determines, as a rule, the extreme risk in price fluctuations. We review some of the most important stochastic volatility models concerning their extreme behavior. This includes the tail behavior as well as the cluster possibilities of such models. The following pattern is common for discrete‐time and continuous‐time models. In linear models the volatility inherits the tails from the driving noise sequence, and clusters occur basically through the driving noise, whenever it is heavy tailed. In nonlinear models like the GARCH and COGARCH models, heavy tails and clusters in extremes occur from the feedback structure of the underlying random recurrence equation.
Securitization
Authors: Barrieu Pauline 
Published: May 2010

Abstract
Securitization can be seen as any process by which tradable securities are created and introduced into capital markets. Such a process generally involves typically the isolation of a pool of assets and their repackaging into securities. Securitization enables the originator of such a transaction to manage and diversify its risk, to optimize the use of capital, while investors in these new securities are looking for diversification and higher return. Various types of risks have been securitized and different structures have been issued in the capital markets.
ABS
Indices
Authors: Li David X. 
Published: May 2010

Abstract
We provide an overview of the ABX asset backed securities indices, and a short‐lived tranche market, TABX based on the ABX indices. We provide the background on the index, its construction rule and premium leg and loss leg payments. We then briefly touch upon the modeling of the index and its tranches since there is no market consensus on a standard model for the TABX market.
Modigliani–Miller Theorem
Authors: Braouezec Yann 
Published: May 2010

AbstractThe Modigliani–Miller theorem states that the value of a firm is invariant with respect to its leverage policy in an arbitrage‐free market when there is no corporate income tax and no bankruptcy cost: whether the firm is financed through debt or equity, its value remains the same. We present various forms of this result and contrast it with empirical evidence on capital structure of firms.
Multifractals
Authors: Bacry Emmanuel , Muzy Jean‐Fran 
Published: May 2010

Abstract
We present an overview of multifractal models of asset returns. All the proposed models rely upon the notion of random multiplicative cascades. We focus in more details on the simplest of such models namely the log‐normal multifractal random walk. This model can be seen as a stochastic volatility model where the (log‐) volatility has a peculiar long‐range correlated memory. We briefly address calibration issues of such models and their applications to volatility and Value at Risk (VaR) forecasting.
Random Matrix Theory
Authors: El Karoui Noureddine 
Published: May 2010

AbstractRandom matrix theory is concerned with the study of matrices with random entries. It is relevant in the study of portfolio optimization problems, in particular, when the number of assets in the portfolio is of the same order of magnitude as the number of observations taken to compute the correlations between the assets. The article gives an overview of key results concerning eigenvalue properties of large dimensional sample covariance matrices.
Convexity Adjustments
Authors: Gaspar Raquel M. , Murgoci Agatha 
Published: May 2010

Abstract

A convexity adjustment (or convexity correction) in fixed income markets arises when one uses prices of standard (plain vanilla) products plus an
adjustment
to price nonstandard products. We explain the basic and appealing idea behind the use of convexity adjustments and focus on the situations of particular importance to practitioners: yield convexity adjustments, forward
versus
futures convexity adjustments, timing and quanto convexity adjustments. We claim that the appropriate way to look into any of these adjustments is as a side effect of a measure change. This approach bypasses the need for Taylor approximations or unrealistic assumptions.
Algorithmic Trading
Authors: Kolm Petter N. , Maclin Lee 
Published: May 2010

Abstract

In a broad sense, the term
algorithmic trading
is used to describe trading in an automated manner according to a set of rules. It is often used interchangeably with statistical trading or statistical arbitrage, which may or may not be automated, but is based on signals derived from statistical analyses or models. Smart order routing, program trading, and rules‐based trading are some of the other terms associated with algorithmic trading. More recently, the range of functions and activities associated with algorithmic trading has grown to include market impact modeling, execution risk analytics, cost aware portfolio construction, and the use of market microstructure effects. In this article, we first explain the basic ideas of market impact and optimal execution from both the sell‐ and buy‐side perspectives. We then provide an overview of the most popular algorithmic trading strategies.
Ruin Theory
Authors: Asmussen Søren 
Published: May 2010

Abstract

A ruin probability is defined as the probability that some stochastic process
R
enters some rare set (the set of ruin, typically (−∞, 0]) during some time interval, typically [0, ∞) or [0,
T
]. In the setting of the most basic insurance risk models, a survey is given of the behavior of ruin probabilities and how to calculate/approximate them.
Actuarial Premium Principles
Authors: Goovaerts Marc J. , Dhaene Jan , Rachedi Omar 
Published: May 2010

AbstractAn actuarial premium principle is a method for assigning an appropriate price for an insurance policy. Different classes of premium calculation principles emerge from different axiomatic settings. Several premium principles are presented, with an emphasis on the theories of choice under risk underlying them. Two approaches for deriving premium principles are provided: the first is based on Bühlmann's economic principle, while the second is based on a generalized Markov inequality.
Insurance Risk Models
Authors: Grandell Jan 
Published: May 2010

AbstractAn insurance risk model consists of three parts: a point process describing the occurrence of the claims, a model for the cost of the claims, and a premium. Here Poisson processes, renewal processes, and Cox processes are discussed. Examples of Cox processes which have been used in risk models are given. Classes of distributions for the cost of the claims mentioned are exponentially bounded distributions or light‐tailed distributions, subexponential distributions or heavy‐tailed distributions, and an intermediate case.