PRICING SWAPTIONS AND COUPON BOND OPTIONS IN AFFINE TERM STRUCTURE MODELS

We propose an approach to find an approximate price of a swaption in affine term structure models. Our approach is based on the derivation of approximate swap rate dynamics in which the volatility of the forward swap rate is itself an affine function of the factors. Hence, we remain in the affine framework and well-known results on transforms and transform inversion can be used to obtain swaption prices in similar fashion to zero bond options (i.e., caplets). The method can easily be generalized to price options on coupon bonds. Computational times compare favorably with other approximation methods. Numerical results on the quality of the approximation are excellent. Our results show that in affine models, analogously to the LIBOR market model, LIBOR and swap rates are driven by approximately the same type of (in this case affine) dynamics.     

Towards a new way to measure power: Applying conjoint analysis to group decisions

This paper presents some preliminary development towards a methodology for measuring power in a group purchase decision. We define power as the capacity of an individual to change the probability of an alternative being chosen by expressing an opinion about that alternative. We present a linear model that relates the group members' preferences to the probability that a given alternative will be chosen. The model parameters reflect specific measures of power and can be estimated by conjoint analysis. We present the results of a pilot field study, conducted on a sample of purchasing managers, which favorably assesses certain psychometric properties of the resulting measures for dyads.     

THE AFFINE LIBOR MODELS

We provide a general and flexible approach to LIBOR modeling based on the class of affine factor processes. Our approach respects the basic economic requirement that LIBOR rates are nonnegative, and the basic requirement from mathematical finance that LIBOR rates are analytically tractable martingales with respect to their own forward measure. Additionally, and most importantly, our approach also leads to analytically tractable expressions of multi‐LIBOR payoffs. This approach unifies therefore the advantages of well‐known forward price models with those of classical LIBOR rate models. Several examples are added and prototypical volatility smiles are shown. We believe that the CIR process‐based LIBOR model might be of particular interest for applications, since closed form valuation formulas for caps and swaptions are derived.     

Pricing the Quality Option In Treasury Bond Futures1

This article develops a model for pricing the quality option embedded in the Treasury bond futures contract. Since the option value is set relative to a large family of deliverable bond prices, it is important for the theoretical bond prices to match up to the observed prices. Hence an arbitrage-based model is used where the forward rate process is initialized at its current observable value. A model for valuing the quality option in an otherwise identical forward contract is also established. This permits the quality option and marking to market costs to be separately quantified. Support is provided for the common practice of pricing Treasury bond futures contracts as forward contracts with an embedded forward quality option.     

Cumulative Discrete Choice

We present a discrete choice model in which a consumer's impression of each alternative is based on her memory of past experience with this choice, and is stochastically updated whenever the alternative is chosen. The consumer remembers a cumulative utility index per alternative, and, when an alternative is chosen, the index is updated by the addition of a random variable, interpreted as instantaneous utility. We prove that the frequencies of choice converge, with probability 1, to limit frequencies, which can be computed from the model's parameters.     

A two‐mean reverting‐factor model of the term structure of interest rates

This article presents a two‐factor model of the term structure of interest rates. It is assumed that default‐free discount bond prices are determined by the time to maturity and two factors, the long‐term interest rate, and the spread (i.e., the difference) between the short‐term (instantaneous) risk‐free rate of interest and the long‐term rate. Assuming that both factors follow a joint Ornstein‐Uhlenbeck process, a general bond pricing equation is derived. Closed‐form expressions for prices of bonds and interest rate derivatives are obtained. The analytical formula for derivatives is applied to price European options on discount bonds and more complex types of options. Finally, empirical evidence of the model's performance in comparison with an alternative two‐factor (Vasicek‐CIR) model is presented. The findings show that both models exhibit a similar behavior for the shortest maturities. However, importantly, the results demonstrate that modeling the volatility in the long‐term rate process can help to fit the observed data, and can improve the prediction of the future movements in medium‐ and long‐term interest rates. So it is not so clear which is the best model to be used. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23: 1075–1105, 2003     

Modeling preference evolution in discrete choice models: A Bayesian state-space approach

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics. We show how the state space approach can include state dependence, unobserved heterogeneity, and more importantly, temporal variability in preferences using a correlated sequence of population distributions. The proposed model is very general and nests commonly used choice models in the literature as special cases. We use Markov chain monte carlo methods for estimating model parameters and apply our methodology to a scanner data set containing household brand choices over an eight-year period. Our analysis indicates that preferences exhibit significant variation over the time-span of the data and that incorporating time-variation in parameters is crucial for appropriate inferences regarding the magnitude and evolution of choice elasticities. We also find that models that ignore time variation in parameters can yield misleading inferences about the impact of causal variables. This paper is based on the first author's doctoral dissertation.     

Dynamics of Link Failure Events in Network Markets

We present a computational model and simulation results on the dynamics of local link failures in markets with network structure. Bandwidth markets are inherently networked, so we focus on telecommunications here. The objective of this paper is to test whether or not network failures will have serious economic consequences. We measure economic consequences by looking at changes in expected bandwidth prices, changes in value-at-risk (VAR) and in conditional-value-at-risk (CVAR). Bandwidth markets may be particularly sensitive to network failures because bandwidth is a non-storable commodity. On the other hand alternative paths with equivalent quality of service (QoS) are perfect substitutes so this may limit sensitivity. Non-storability has contributed to enormous volatility in deregulated electricity prices and observations of enormous price spikes. Bandwidth is a true network commodity in that links in the network itself are the traded commodities. Thus a local failure can affect alternative equivalent paths and this can have a knock-on effect in turn. We used a spot market model incorporating non-storability and alternative path selection on price grounds and limited by QoS-equivalence. Spike models are incorporated based on empirical data. We found that for a realistic large-scale market topology if there are, say, four failures per link per year, half of which are long enough to affect the market, then: expected link prices are increased 12%; VAR is increased by 30%; and CVAR by 40%. This is even with a spike size (×3) that is modest compared to observations in electricity markets (×10–×100). For market participants with capacity positions in such a market these consequences are likely to be serious. Thus if failures occur at this rate their consequence must be included in planning. Furthermore, whilst at low failure intensities the network acts as a dampening factor, at higher intensities it acts as an amplifier and thus cannot be neglected. We believe this amplification to be an emergent phenomenon of any market with network structure, although clearly more important for markets with no storage.     

THE TERM STRUCTURE OF INTEREST RATES AS A GAUSSIAN RANDOM FIELD

A simple model of the term structure of interest rates is introduced in which the family of instantaneous forward rates evolves as a continuous Gaussian random field. A necessary and sufficient condition for the associated family of discounted zero-coupon bond prices to be martingales is given, permitting the consistent pricing of interest rate contingent claims. Examples of the pricing of interest-rate caps and the situation when the Gaussian random field may be viewed as a deterministic time change of the standard Brownian sheet are discussed.     

What's in a change in an industrial bond rating?

Bond quality rating changes (BQRC) for industrial bonds are analyzed using both univariate statistical methods and discriminant analysis to find significant variables and their relationship with the changes. The single most important explanatory variable is found to be the rate of return on assets (ROA), followed by the trend in the return on assets (ROATREND). The univariate analysis found six of the seven proposed explanatory variables significant beyond the 0.01 level. The two-group discriminant analysis model achieved a correct classification rate of over 77%. The paper shows how the results of the two-group discriminant analysis can be used for a three-way prediction (upgrade, downgrade, or no change of bond ratings). The results of this study show that models based on financial statement data can predict rating changes with good accuracy and therefore may be a useful tool for rating agencies, at least as an initial screening device.     

GDP growth,terms-of-trade effects,and total factor productivity

The purpose of this paper is to assess the contribution of each one of the major factors explaining Australian nominal GDP growth: technological change, movements in the terms of trade, increases in the endowments of labour and capital, and changes in domestic output prices. We use an index number technique as well as an econometric approach. Moreover, we look at several methods to decompose total factor productivity growth into secular and unexpected components. All our empirical results have a tight theoretical foundation, being based on the GDP function approach to modelling the production sector of an open economy.     

A SCHUMPETERIAN GROWTH MODEL WITH EQUILIBRIUM UNEMPLOYMENT

We introduce efficiency‐wage unemployment in a model of growth with endogenous technical change. Our research aim is twofold. First, we try to provide an analytically tractable model of growth with efficiency‐wage unemployment that can be viewed as alternative to the standard models of growth and search unemployment. Second, we try to analyze the steady‐state effects of some labor market policies on unemployment and growth. We find that a positive relationship between growth and unemployment exists and that the effectiveness of any labor market policy aimed at improving the performance of the labor market crucially depends on how individuals discount future income.     

基于Heston's SV模型下带有违约风险的最优再保险-投资策略

对于模糊厌恶型保险公司，在可违约金融市场中，考虑其比例再保险-投资问题。假设在任意时刻保险公司可购买比例再保险和投资无风险资产、风险资产和可违约债券，其中风险资产价格服从Heston's SV (Heston's Stochastic Volatility) 模型。首先，考虑模型不确定性，采用与参考模型概率测度等价的概率测度描述替代模型。利用Girsanov变换得到保险公司在替代模型下的财富过程，并通过动态规划原理建立了相应的HJB (Hamilton-Jacob-Bellman) 方程，其中，文章用含状态依赖的不同偏好参数度量模型不确定性的模糊度。其次，分别在违约前和违约后的情况下，针对CARA (Constant Absolute Risk Aversion) 效用函数求解HJB方程，得到了最优稳键的再保险-投资策略，并给出了数值模拟和经济学解释。结果表明：相比较使用同一偏好参数的模型结果，文章的最优策略的表达式更精确，考虑的模型更符合实际金融环境。     

Convexity meets replication: Hedging of swap derivatives and annuity options

Convexity correction arises when one computes the expected value of an interest rate index under a probability measure other than its own natural martingale measure. As a typical example, the natural martingale measure of the swap rate is the swap measure with annuity as the numeraire. However, the evaluation of the discounted expectation of the payoff in a constant maturity swap (CMS) derivative is performed under the forward measure corresponding to the payment date. In this study, we propose a generalization of the static replication formula by exploring the linkage between replication, convexity correction, and numeraire change. We illustrate how the static replication of a CMS caplet by a portfolio of payer swaptions is related to convexity correction associated with the bond–annuity numeraire ratio. We also demonstrate the use of the generalized static replication approach for hedging the in‐arrears clean index principal swaps and annuity options © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:659–678, 2011     

Arguing over Goals for Negotiation: Adopting an Assumption-Based Argumentation Decision Support System

Computational models of argumentation has been put forward as a promising approach to support decision making. In this context several recent works have proposed argumentation-based frameworks for decision making. In this paper we describe an application based on an argumentation-based mechanism for decision-making to concede. Adopting the assumption-based approach of argumentation, we propose an argumentation framework in which preferences are attached to goals. Arguments are defined as tree-like structures. Our framework is equipped with a computational counterpart for solving a decision problem, modeling the intuition that high-ranked goals are preferred to low-ranked goals which can be withdrawn. In this way, our framework suggests some decisions and provides an interactive and intelligible explanation of this choice. Our implementation, called MARGO, has been used for service selection within the ArguGRID project. We illustrate our approach with an industrial application, and illustrate the operation of the system with a running example.     

Term Structure Models Driven by General Lévy Processes

As a generalization of the Gaussian Heath–Jarrow–Morton term structure model, we present a new class of bond price models that can be driven by a wide range of Lévy processes. We deduce the forward and short rate processes implied by this model and prove that, under certain assumptions, the short rate is Markovian if and only if the volatility structure has either the Vasicek or the Ho–Lee form. Finally, we compare numerically forward rates and European call option prices in a model driven by a hyperbolic Lévy motion with those in the Gaussian model.     

Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks

The increasing access to large social network data has generated substantial interest in the marketing community. However, due to its large scale, traditional analysis methods often become inadequate. In this paper, we propose a sequential sampling enhanced composite likelihood approach for efficient estimation of social intercorrelations in large-scale networks using the spatial model. Given a known population network, the proposed approach sequentially takes small samples from the network, and adaptively improves model parameter estimates through learnings obtained from previous samples. In comparison to population-based maximum likelihood estimation that is computationally prohibitive when the network size is large, the proposed approach makes it computationally feasible to analyze large networks and provide efficient estimation of social intercorrelations among members in large networks. In comparison to sample-based estimation that relies on information purely from the sample and produces underestimation bias in social intercorrelation estimates, the proposed approach effectively uses information from the population without compromising computation efficiency. Through simulation studies based on simulated networks and real networks, we demonstrate significant advantages of the proposed approach over benchmark estimation methods and discuss managerial implications. We also discuss extension of the proposed approach in the context of an unknown population network structure, as well as in an alternative form of the spatial model.     

An integrative ethical approach to leader favoritism

Relationship building is one of the most important aspects of leadership; however, it can pose ethical challenges. Though particularistic treatment of employees by leaders, that is, leader favoritism, commonly occurs, it is conventionally regarded negatively as fairness norms require leaders to treat followers equally. In this conceptual study, we explore different views on leader favoritism based on different ethical principles. We develop an alternative to the conventional view and suggest that leader favoritism may not necessarily lead to negative outcomes when empathy‐based favoritism is applied. In this vein, we recommend drawing on the ethical principles of a utilitarian approach by balancing particularism and universalism, which is also helpful to build organizational social capital. We contribute to leadership theory by developing an early concept of an integrative ethical approach to leader favoritism.     

EMULATION,PREVENTION AND SOCIAL INTERACTION IN CONSUMPTION DYNAMICS

In this paper we show that the role of diversity, local interactions and global endogenous change at the level of social standards might be crucial in understanding the evolution of consumption patterns in modern economies. We propose an evolutionary model from which consumption dynamics can be analyzed as global properties emerging from the endogenous transformation of a society inhabited by boundedly rational interactive consumers. This work aspires to take a modest step forward in the direction of an evolutionary theory of demand change.     

A purchase decision support model considering consumer personalization about aspirations and risk attitudes

Ranking alternative products to help consumers make better purchase choices is a valuable research topic. Most previous decision support models cannot be well applied to heterogeneous consumers. This paper focuses on establishing a personalized interactive model to assist consumers make better buying decisions with less effort. For the alternative products provided by consumers, we collect online reviews and parameter configurations of alternative products and then obtain the fusing evaluative information. As consumers are dominated by bounded rationality, they only provide partially key attribute weights, based on which, we construct an optimizing model to obtain the optimal attribute weights of customers for products. Then, a satisfaction function is proposed by uniting aspiration levels and risk attitudes of consumers and a compensatory decision rules is established to rank and recommend the brands to consumers. Finally, practicability of this study is illustrated with a real car purchase case. Through the case study, it can be seen that the proposed decision support model generates a personalized list of alternatives based on consumer's own utility function about risk attitudes, aspiration levels, and preferences for product attributes, which further confirms that the proposed model can capture the personalized needs of consumers. Theoretical and managerial implications of this model as well as advantages are further illustrated.