Efficient Nash Equilibrium under Adverse Selection

This paper revisits the problem of adverse selection in the insurance market of Rothschild and Stiglitz [28]. We propose a simple extension of the game-theoretic structure in Hellwig [14] under which Nash-type strategic interaction between the informed customers and the uninformed firms results always in a particular separating equilibrium. The equilibrium allocation is unique and Pareto-efficient in the interim sense subject to incentive-compatibility and individual rationality. In fact, it is the unique neutral optimum in the sense of Myerson [22].

Why is it so Hard to Value Intangibles? Evidence from Investments in High-Technology Start-Ups

The paper uses a range of primary-source empirical evidence to address the question: ‘why is it to hard to value intangible assets?’ The setting is venture capital investment in high technology companies. While the investors are risk specialists and financial experts, the entrepreneurs are more knowledgeable about product innovation. Thus the context lends itself to analysis within a principal-agent framework, in which information asymmetry may give rise to adverse selection, pre-contract, and moral hazard, post-contract. We examine how the investor might attenuate such problems and attach a value to such high-tech investments in what are often merely intangible assets, through expert due diligence, monitoring and control. Qualitative evidence is used to qualify the more clear cut picture provided by a principal-agent approach to a more mixed picture in which the ‘art and science’ of investment appraisal are utilised by both parties alike

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Harmful competition in the insurance markets

There is a general presumption that competition is a good thing. In this paper we show that competition in the insurance markets can be bad and that adverse selection is in general worse under competition than under monopoly. The reason is that monopoly can exploit its market power to relax incentive constraints by cross-subsidization between different risk types. Cream-skimming behavior, on the contrary, prevents competitive firms from using implicit transfers. In effect monopoly is shown to provide better coverage to those buying insurance but at the cost of limiting participation to insurance. Performing simulation for di erent distributions of risk, we find that monopoly in general performs (much) better than competition in terms of the realization of the gains from trade across all traders in equilibrium. However, most of the surplus is retained by the firm and, as a result, most individuals prefer competitive markets notwithstanding their performance is generally poorer than monopoly.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Using VARs and TVP-VARs with Many Macroeconomic Variables

This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.

Contagious Illiquidity I: Contagion through Time

This paper is an investigation into the dynamics of asset markets with adverse selection a la Akerlof (1970). The particular question asked is: can market failure at some later date precipitate market failure at an earlier date? The answer is yes: there can be "contagious illiquidity" from the future back to the present. The mechanism works as follows. If the market is expected to break down in the future, then agents holding assets they know to be lemons (assets with low returns) will be forced to hold them for longer - they cannot quickly resell them. As a result, the effective difference in payoff between a lemon and a good asset is greater. But it is known from the static Akerlof model that the greater the payoff differential between lemons and non-lemons, the more likely is the market to break down. Hence market failure in the future is more likely to lead to market failure today. Conversely, if the market is not anticipated to break down in the future, assets can be readily sold and hence an agent discovering that his or her asset is a lemon can quickly jettison it. In effect, there is little difference in payoff between a lemon and a good asset. The logic of the static Akerlof model then runs the other way: the small payoff differential is unlikely to lead to market breakdown today. The conclusion of the paper is that the nature of today's market - liquid or illiquid - hinges critically on the nature of tomorrow's market, which in turn depends on the next day's, and so on. The tail wags the dog.

Suppliers of Priors: A Theory of Retailing Inspired by the Market for Chinese Antiquities

Adverse selection may thwart trade between an informed seller, who knows the probability p that an item of antiquity is genuine, and an uninformed buyer, who does not know p. The buyer might not be wholly uninformed, however. Suppose he can perform a simple inspection, a test of his own: the probability that an item passes the test is g if the item is genuine, but only f < g if it is fake. Given that the buyer is no expert, his test may have little power: f may be close to g. Unfortunately, without much power, the buyer's test will not resolve the difficulty of adverse selection; gains from trade may remain unexploited. But now consider a "store", where the seller groups a number of items, perhaps all with the same quality, the same probability p of being genuine. (We show that in equilibrium the seller will choose to group items in this manner.) Now the buyer can conduct his test across a large sample, perhaps all, of a group of items in the seller's store. He can thereby assess the overall quality of these items; he can invert the aggregate of his test results to uncover the underlying p; he can form a "prior". There is thus no longer asymmetric information between seller and buyer: gains from trade can be exploited. This is our theory of retailing: by grouping items together - setting up a store - a seller is able to supply buyers with priors, as well as the items themselves. We show that the weaker the power of the buyer�s test (the closer f is to g), the greater the seller�s profit. So the seller has no incentive to assist the buyer � e.g., by performing her own tests on the items, or by cleaning them to reveal more about their true age. The paper ends with an analysis of which sellers should specialise in which qualities. We show that quality will be low in busy locations and high in expensive locations.

Dynamic Option Adjusted Spread and the Value of Mortgage Backed Securities (Draft 1)

We extend a reduced form model for pricing pass-through mortgage backed securities (MBS) and provide a novel hedging tool for investors in this market. To calculate the price of an MBS, traders use what is known as option-adjusted spread (OAS). The resulting OAS value represents the required basis points adjustment to reference curve discounting rates needed to match an observed market price. The OAS suffers from some drawbacks. For example, it remains constant until the maturity of the bond (thirty years in mortgage-backed securities), and does not incorporate interest rate volatility. We suggest instead what we call dynamic option adjusted spread (DOAS). The latter allows investors in the mortgage market to account for both prepayment risk and changes of the yield curve.

Labour Market Imperfections, International Integration and Selection

Although a large body of literature has focused on the effects of intra-firm differences on export performance, relatively little attention has been devoted to the interaction between firms' selection and international performance and labour market institutions - in contrast with the centrality of the latter to current policy and public debates on the implications of economic globalisation for national policies and institutions. In this paper, we study the effects of labour market unionisation on the process of competitive selection between heterogeneous firms and analyse how the interaction between the two is affected by trade liberalisation between countries with different unionisation patterns.

Dynamic Option Adjusted Spread and the Value of Mortgage Backed Securities (Draft 2)

We extend a reduced form model for pricing pass-through mortgage backed securities (MBS) and provide a novel hedging tool for investors in this market. To calculate the price of an MBS, traders use what is known as option-adjusted spread (OAS). The resulting OAS value represents the required basis points adjustment to reference curve discounting rates needed to match an observed market price. The OAS suffers from some drawbacks. For example, it remains constant until the maturity of the bond (thirty years in mortgage-backed securities), and does not incorporate interest rate volatility. We suggest instead what we call dynamic option adjusted spread (DOAS), which allows investors in the mortgage market to account for both prepayment risk and changes of the yield curve.

Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model.

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

IInflation and Globalisation: A Dynamic Factor Model with Stochastic Volatility

National inflation rates reflect domestic and international (regional and global) influences. The relative importance of these components remains a controversial empirical issue. We extend the literature on inflation co-movement by utilising a dynamic factor model with stochastic volatility to account for shifts in the variance of inflation and endogenously determined regional groupings. We find that most of inflation variability is explained by the country specific disturbance term. Nevertheless, the contribution of the global component in explaining industrialised countries’ inflation rates has increased over time.

The regional electricity generation mix in Scotland: A portfolio selection approach

Standalone levelised cost assessments of electricity supply options miss an important contribution that renewable and non-fossil fuel technologies can make to the electricity portfolio: that of reducing the variability of electricity costs, and their potentially damaging impact upon economic activity. Portfolio theory applications to the electricity generation mix have shown that renewable technologies, their costs being largely uncorrelated with non-renewable technologies, can offer such benefits. We look at the existing Scottish generation mix and examine drivers of changes out to 2020. We assess recent scenarios for the Scottish generation mix in 2020 against mean-variance efficient portfolios of electricity-generating technologies. Each of the scenarios studied implies a portfolio cost of electricity that is between 22% and 38% higher than the portfolio cost of electricity in 2007. These scenarios prove to be “inefficient” in the sense that, for example, lower variance portfolios can be obtained without increasing portfolio costs, typically by expanding the share of renewables. As part of extensive sensitivity analysis, we find that Wave and Tidal technologies can contribute to lower risk electricity portfolios, while not increasing portfolio cost.