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## Pricing Models (pricing + models)
Kinds of Pricing Models
## Selected Abstracts## AN EQUILIBRIUM GUIDE TO DESIGNING AFFINE PRICING MODELS MATHEMATICAL FINANCE, Issue 4 2008Bjørn ErakerThe paper examines equilibrium models based on Epstein,Zin preferences in a framework in which exogenous state variables follow affine jump diffusion processes. A main insight is that the equilibrium asset prices can be computed using a standard machinery of affine asset pricing theory by imposing parametric restrictions on market prices of risk, determined inside the model by preference and model parameters. An appealing characteristic of the general equilibrium setup is that the state variables have an intuitive and testable interpretation as driving the consumption and dividend dynamics. We present a detailed example where large shocks (jumps) in consumption volatility translate into negative jumps in equilibrium prices of the assets as agents demand a higher premium to compensate for higher risks. This endogenous "leverage effect," which is purely an equilibrium outcome in the economy, leads to significant premiums for out-of-the-money put options. Our model is thus able to produce an equilibrium "volatility smirk," which realistically mimics that observed for index options. [source] ## Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach EUROPEAN FINANCIAL MANAGEMENT, Issue 3 2008Manuel AmmannG12 Abstract We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors-in-variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and theFama and French (1993)three-factor model. We find that time-variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three-factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm-specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three-factor model, the unconditional CAPM, and the unconditional three-factor model. [source] ## Using Expectations to Test Asset Pricing Models FINANCIAL MANAGEMENT, Issue 3 2005Alon BravAsset pricing models generate predictions relating assets' expected rates of return and their risk attributes. Most tests of these models have employed realized rates of return as a proxy for expected return. We use analysts' expected rates of return to examine the relation between these expectations and firm attributes. By assuming that analysts' expectations are unbiased estimates of market-wide expected rates of return, we can circumvent the use of realized rates of return and provide evidence on the predictions emanating from traditional asset pricing models. We find a positive, robust relation between expected return and market beta and a negative relation between expected return and firm size, consistent with the notion that these are risk factors. We do not find that high book-to-market firms are expected to earn higher returns than low book-to-market firms, inconsistent with the notion that book-to-market is a risk factor. [source] ## Can Asset Pricing Models Price Idiosyncratic Risk in U.K. Stock Returns? FINANCIAL REVIEW, Issue 4 2007Jonathan FletcherG12 Abstract I examine how well different linear factor models and consumption-based asset pricing models price idiosyncratic risk in U.K. stock returns. Correctly pricing idiosyncratic risk is a significant challenge for many of the models I consider. For some consumption-based models, there is a clear tradeoff in the performance of the models between correctly pricing systematic risk and idiosyncratic risk. Linear factor models do a better job in most cases in pricing systematic risk than consumption-based models but the reverse is true for idiosyncratic risk. [source] ## Testing Option Pricing Models with Stochastic Volatility, Random Jumps and Stochastic Interest Rates INTERNATIONAL REVIEW OF FINANCE, Issue 3-4 2002George J. JiangIn this paper, we propose a parsimonious GMM estimation and testing procedure for continuous-time option pricing models with stochastic volatility, random jump and stochastic interest rate. Statistical tests are performed on both the underlying asset return model and the risk-neutral option pricing model. Firstly, the underlying asset return models are estimated using GMM with valid statistical tests for model specification. Secondly, the preference related parameters in the risk-neutral distribution are estimated from observed option prices. Our findings confirm that the implied risk premiums for stochastic volatility, random jump and interest rate are overall positive and varying over time. However, the estimated risk-neutral processes are not unique, suggesting a segmented option market. In particular, the deep ITM call (or deep OTM put) options are clearly priced with higher risk premiums than the deep OTM call (or deep ITM put) options. Finally, while stochastic volatility tends to better price long-term options, random jump tends to price the short-term options better, and option pricing based on multiple risk-neutral distributions significantly outperforms that based on a single risk-neutral distribution. [source] ## Specification Analysis of Option Pricing Models Based on Time-Changed Lévy Processes THE JOURNAL OF FINANCE, Issue 3 2004Jing-zhi HuangWe analyze the specifications of option pricing models based on time-changed Lévy processes. We classify option pricing models based on the structure of the jump component in the underlying return process, the source of stochastic volatility, and the specification of the volatility process itself. Our estimation of a variety of model specifications indicates that to better capture the behavior of the S&P 500 index options, we need to incorporate a high frequency jump component in the return process and generate stochastic volatilities from two different sources, the jump component and the diffusion component. [source] ## Managing Risks in Multiple Online Auctions: An Options Approach, DECISION SCIENCES, Issue 3 2005Ram GopalABSTRACT The scenario of established business sellers utilizing online auction markets to reach consumers and sell new products is becoming increasingly common. We propose a class of risk management tools, loosely based on the concept of financial options that can be employed by such sellers. While conceptually similar to options in financial markets, we empirically demonstrate that option instruments within auction markets cannot be developed employing similar methodologies, because the fundamental tenets of extant option pricing models do not hold within online auction markets. We provide a framework to analyze the value proposition of options to potential sellers, option-holder behavior implications on auction processes, and seller strategies to write and price options that maximize potential revenues. We then develop an approach that enables a seller to assess the demand for options under different option price and volume scenarios. We compare option prices derived from our approach with those derived from the Black-Scholes model (Black & Scholes, 1973) and discuss the implications of the price differences. Experiments based on actual auction data suggest that options can provide significant benefits under a variety of option-holder behavioral patterns. [source] ## Estimation and Confidence Regions for Parameter Sets in Econometric Models, ECONOMETRICA, Issue 5 2007Victor ChernozhukovThis paper develops a framework for performing estimation and inference in econometric models with partial identification, focusing particularly on models characterized by moment inequalities and equalities. Applications of this framework include the analysis of game-theoretic models, revealed preference restrictions, regressions with missing and corrupted data, auction models, structural quantile regressions, and asset pricing models. Specifically, we provide estimators and confidence regions for the set of minimizers ,I of an econometric criterion function Q(,). In applications, the criterion function embodies testable restrictions on economic models. A parameter value ,that describes an economic model satisfies these restrictions if Q(,) attains its minimum at this value. Interest therefore focuses on the set of minimizers, called the identified set. We use the inversion of the sample analog, Qn(,), of the population criterion, Q(,), to construct estimators and confidence regions for the identified set, and develop consistency, rates of convergence, and inference results for these estimators and regions. To derive these results, we develop methods for analyzing the asymptotic properties of sample criterion functions under set identification. [source] ## Transform Analysis and Asset Pricing for Affine Jump-diffusions ECONOMETRICA, Issue 6 2000Darrell DuffieIn the setting of ,affine' jump-diffusion state processes, this paper provides an analytical treatment of a class of transforms, including various Laplace and Fourier transforms as special cases, that allow an analytical treatment of a range of valuation and econometric problems. Example applications include fixed-income pricing models, with a role for intensity-based models of default, as well as a wide range of option-pricing applications. An illustrative example examines the implications of stochastic volatility and jumps for option valuation. This example highlights the impact on option ,smirks' of the joint distribution of jumps in volatility and jumps in the underlying asset price, through both jump amplitude as well as jump timing. [source] ## Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach EUROPEAN FINANCIAL MANAGEMENT, Issue 3 2008Manuel AmmannG12 Abstract We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors-in-variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and theFama and French (1993)three-factor model. We find that time-variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three-factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm-specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three-factor model, the unconditional CAPM, and the unconditional three-factor model. [source] ## Smiles, Bid-ask Spreads and Option Pricing EUROPEAN FINANCIAL MANAGEMENT, Issue 3 2001Ignacio PeñaGiven the evidence provided by Longstaff (1995), and Peña, Rubio and Serna (1999) a serious candidate to explain the pronounced pattern of volatility estimates across exercise prices might be related to liquidity costs. Using all calls and puts transacted between 16:00 and 16:45 on the Spanish IBEX-35 index futures from January 1994 to October 1998 we extend previous papers to study the influence of liquidity costs, as proxied by the relative bid-ask spread, on the pricing of options. Surprisingly, alternative parametric option pricing models incorporating the bid-ask spread seem to perform poorly relative to Black-Scholes. [source] ## Using Expectations to Test Asset Pricing Models FINANCIAL MANAGEMENT, Issue 3 2005Alon BravAsset pricing models generate predictions relating assets' expected rates of return and their risk attributes. Most tests of these models have employed realized rates of return as a proxy for expected return. We use analysts' expected rates of return to examine the relation between these expectations and firm attributes. By assuming that analysts' expectations are unbiased estimates of market-wide expected rates of return, we can circumvent the use of realized rates of return and provide evidence on the predictions emanating from traditional asset pricing models. We find a positive, robust relation between expected return and market beta and a negative relation between expected return and firm size, consistent with the notion that these are risk factors. We do not find that high book-to-market firms are expected to earn higher returns than low book-to-market firms, inconsistent with the notion that book-to-market is a risk factor. [source] ## Price and Volatility Transmission across Borders FINANCIAL MARKETS, INSTITUTIONS & INSTRUMENTS, Issue 3 2006Louis GagnonOver the past forty years, financial markets throughout the world have steadily become more open to foreign investors. With open markets, asset prices are determined globally. A vast literature on portfolio choice and asset pricing has evolved to study the importance of global factors as well as local factors as determinants of portfolio choice and of expected returns on risky assets. There is growing evidence that risk premia are increasingly determined globally. An important outcome of this force of globalization is increased comovement in asset prices across markets. This survey study examines the literature on the dynamics of comovements in asset prices and volatility across markets around the world. The literature began in the 1970s in conjunction with early theoretical developments on international asset pricing models, but it blossomed in the late 1980s and early 1990s with the availability of comprehensive international stock market databases and the development of econometric methodology to model these dynamics. [source] ## A Comparison of Cost of Equity Estimates of Local and Global CAPMs FINANCIAL REVIEW, Issue 4 2001Dev R. MishraG12/G15/G32 Abstract Cost of equity estimates are compared for three pricing models: the traditional local CAPM, the single (market) factor global CAPM, and the two-factor global CAPM, with both market and currency index factors. For 2989 US stocks, the average difference in the cost of equity estimates is about 48 basis points between the local CAPM and the single-factor global CAPM, and is about 61 basis points between the two global models. For 70 developed-market ADRs, the corresponding average differences are 76 and 47 basis points, respectively. For 48 emerging-market ADRs, the corresponding average differences are 57 and 70 basis points. [source] ## A best choice among asset pricing models? ACCOUNTING & FINANCE, Issue 2 2004The Conditional Capital Asset Pricing Model in AustraliaAbstract We use Australian data to test the Conditional Capital Asset Pricing Model (Jagannathan and Wang, 1996). Our results are generally supportive: the model performs well compared with a number of competing asset pricing models. In contrast to the study by Jagannathan and Wang, however, we find that the inclusion of the market for human capital does not save the concept of the time-independent market beta (it remains insignificant). We find support for the role of a small-minus-big factor in pricing the cross-section of returns and find grounds to disagree with Jagannathan and Wang's argument that this factor proxies for misspecified market risk. [source] ## Testing Option Pricing Models with Stochastic Volatility, Random Jumps and Stochastic Interest Rates INTERNATIONAL REVIEW OF FINANCE, Issue 3-4 2002George J. JiangIn this paper, we propose a parsimonious GMM estimation and testing procedure for continuous-time option pricing models with stochastic volatility, random jump and stochastic interest rate. Statistical tests are performed on both the underlying asset return model and the risk-neutral option pricing model. Firstly, the underlying asset return models are estimated using GMM with valid statistical tests for model specification. Secondly, the preference related parameters in the risk-neutral distribution are estimated from observed option prices. Our findings confirm that the implied risk premiums for stochastic volatility, random jump and interest rate are overall positive and varying over time. However, the estimated risk-neutral processes are not unique, suggesting a segmented option market. In particular, the deep ITM call (or deep OTM put) options are clearly priced with higher risk premiums than the deep OTM call (or deep ITM put) options. Finally, while stochastic volatility tends to better price long-term options, random jump tends to price the short-term options better, and option pricing based on multiple risk-neutral distributions significantly outperforms that based on a single risk-neutral distribution. [source] ## Does Greater Firm-Specific Return Variation Mean More or Less Informed Stock Pricing? JOURNAL OF ACCOUNTING RESEARCH, Issue 5 2003Artyom DurnevABSTRACT Roll [1988] observes low R2 statistics for common asset pricing models due to vigorous firm-specific return variation not associated with public information. He concludes that this implies "either private information or else occasional frenzy unrelated to concrete information"[p. 56]. We show that firms and industries with lower market model R2 statistics exhibit higher association between current returns and future earnings, indicating more information about future earnings in current stock returns. This supports Roll's first interpretation: higher firm-specific return variation as a fraction of total variation signals more information-laden stock prices and, therefore, more efficient stock markets. [source] ## Making Financial Goals and Reporting Policies Serve JOURNAL OF APPLIED CORPORATE FINANCE, Issue 4 2004Corporate Strategy: The Case of Progressive InsuranceThe main. nancial goal of Progressive Insurance, the third largest underwriter of auto insurance in the U.S., has remained the same since the late 1960s. Expressed in three words, "96 and grow," the goal tells the company's managers to pursue all growth opportunities while maintaining a "combined ratio" no higher than 96, or what amounts to a minimum 4% spread between revenues (premiums) and costs (including expected losses). Thanks in part to the clarity of mission provided by this goal, the company has produced an average 15% rate of growth in revenues and earnings, along with a remarkably stable 15% return for its shareholders, since going public in 1971. Progressive's simplicity and clarity of mission is also partly responsible for another of the company's distinctive policies: product pricing that, while disciplined, is aggressive and highly decentralized. Having invested some $500 million per year developing statistical models for pricing individual customer risks and acquisition costs, the company was among the. rst in its industry to underwrite "non-standard" risks. And aided by sophisticated pricing models, each of Progressive's 100 or so local product managers are charged with adapting those models to come up with premiums for their own regions. To go along with its strategic and organizational innovations, Progressive also has an innovative disclosure policy. Apart from SEC reports, the company's communications seldom mention earnings or earnings per share, and the company has never provided earnings guidance. With the passage of Reg. FD in late 2000, the company brie. y considered offering guidance. But in the spring of 2001, the board decided instead to provide monthly releases of its realized combined ratio. Since adoption of this new disclosure policy, Progressive has seen a 50% drop in the volatility of its stock price. [source] ## STRATEGY AND SHAREHOLDER VALUE CREATION: THE REAL OPTIONS FRONTIER JOURNAL OF APPLIED CORPORATE FINANCE, Issue 2 2000Martha AmramThe current interest in real options reflects the dramatic increase in the uncertainty of the business environment. Viewed narrowly, the real options approach is the extension of financial option pricing models to the valuation of options on real (that is, nonfinancial) assets. More broadly, the real options approach is a way of thinking that helps managers formulate their strategic options,the future opportunities that are created by today's investments,while considering their likely effect on shareholder value. But if the real options framework promises to link strategy more closely to shareholder value creation, there are some major challenges on the frontier of application. In the first part of this paper, the authors tackle the question, "What is really new about real options, and how does the approach differ from other wellestablished ways to make strategic decisions under uncertainty?" This article provides a specific definition of real options that relies on the ability to track marketpriced risk. Using examples from oil exploration and pharmaceutical drug development, the authors also show how specific features of the industry and the application itself determine the usefulness of the real options approach. The second part of the paper addresses the question: Given the many differences between real and financial options, how should a real options application be framed? The authors examine the use of real options in the valuation of Internet companies to demonstrate the required judgment and tradeoffs in the framing of real options applications. The case of Webvan, an online grocer, is used to illustrate the inter-action between strategy, execution, and valuation. [source] ## Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns JOURNAL OF APPLIED ECONOMETRICS, Issue 2 2010Torben G. AndersenWe provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous-time jump diffusion models traditionally used in asset pricing finance. Our approach builds directly on recently developed realized variation measures and non-parametric jump detection statistics constructed from high-frequency intra-day data. A sequence of simple-to-implement moment-based tests involving various transformations of the daily returns speak directly to the importance of different distributional features, and may serve as useful diagnostic tools in the specification of empirically more realistic continuous-time asset pricing models. On applying the tests to the 30 individual stocks in the Dow Jones Industrial Average index, we find that it is important to allow for both time-varying diffusive volatility, jumps, and leverage effects to satisfactorily describe the daily stock price dynamics. Copyright © 2009 John Wiley & Sons, Ltd. [source] ## Land of addicts? an empirical investigation of habit-based asset pricing models JOURNAL OF APPLIED ECONOMETRICS, Issue 7 2009Xiaohong ChenThis paper studies the ability of a general class of habit-based asset pricing models to match the conditional moment restrictions implied by asset pricing theory. We treat the functional form of the habit as unknown, and estimate it along with the rest of the model's finite dimensional parameters. Using quarterly data on consumption growth, assets returns and instruments, our empirical results indicate that the estimated habit function is nonlinear, that habit formation is better described as internal rather than external, and the estimated time-preference parameter and the power utility parameter are sensible. In addition, the estimated habit function generates a positive stochastic discount factor (SDF) proxy and performs well in explaining cross-sectional stock return data. We find that an internal habit SDF proxy can explain a cross-section of size and book-market sorted portfolio equity returns better than (i) the Fama and French (1993) three-factor model, (ii) the Lettau and Ludvigson (2001b) scaled consumption CAPM model, (iii) an external habit SDF proxy, (iv) the classic CAPM, and (v) the classic consumption CAPM. Copyright © 2009 John Wiley & Sons, Ltd. [source] ## On Modelling Speculative Prices: The Empirical Literature JOURNAL OF ECONOMIC SURVEYS, Issue 2 2001Elena AndreouTraditionally, financial theory and in particular asset pricing models have assumed (implicitly or explicitly) a certain probabilistic structure for speculative prices. The probabilistic structure is usually defined in terms of specific statistical models and relates to the dependence, heterogeneity and the distribution of such prices. The primary objective of this paper is to trace the development of various statistical models proposed since Bachelier (1900), in an attempt to assess how well these models capture the empirical regularities exhibited by data on speculative prices. [source] ## Daily volatility forecasts: reassessing the performance of GARCH models JOURNAL OF FORECASTING, Issue 6 2004David G. McMillanAbstract Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accurate measures and good forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility in-sample, they appear to provide relatively poor out-of-sample forecasts. Recent research has suggested that this relative failure of GARCH models arises not from a failure of the model but a failure to specify correctly the ,true volatility' measure against which forecasting performance is measured. It is argued that the standard approach of using ex post daily squared returns as the measure of ,true volatility' includes a large noisy component. An alternative measure for ,true volatility' has therefore been suggested, based upon the cumulative squared returns from intra-day data. This paper implements that technique and reports that, in a dataset of 17 daily exchange rate series, the GARCH model outperforms smoothing and moving average techniques which have been previously identified as providing superior volatility forecasts. Copyright © 2004 John Wiley & Sons, Ltd. [source] ## Pricing Double-Trigger Reinsurance Contracts: Financial Versus Actuarial Approach JOURNAL OF RISK AND INSURANCE, Issue 4 2002Helmut GründlThis article discusses various approaches to pricing double-trigger reinsurance contracts,a new type of contract that has emerged in the area of ,,alternative risk transfer.'' The potential coverage from this type of contract depends on both underwriting and financial risk. We determine the reinsurer's reservation price if it wants to retain the firm's same safety level after signing the contract, in which case the contract typically must be backed by large amounts of equity capital (if equity capital is the risk management measure to be taken). We contrast the financial insurance pricing models with an actuarial pricing model that has as its objective no lessening of the reinsurance company's expected profits and no worsening of its safety level. We show that actuarial pricing can lead the reinsurer into a trap that results in the failure to close reinsurance contracts that would have a positive net present value because typical actuarial pricing dictates the type of risk management measure that must be taken, namely, the insertion of additional capital. Additionally, this type of pricing structure forces the reinsurance buyer to provide this safety capital as a debtholder. Finally, we discuss conditions leading to a market for double-trigger reinsurance contracts. [source] ## SELF-DECOMPOSABILITY AND OPTION PRICING MATHEMATICAL FINANCE, Issue 1 2007Peter CarrThe risk-neutral process is modeled by a four parameter self-similar process of independent increments with a self-decomposable law for its unit time distribution. Six different processes in this general class are theoretically formulated and empirically investigated. We show that all six models are capable of adequately synthesizing European option prices across the spectrum of strikes and maturities at a point of time. Considerations of parameter stability over time suggest a preference for two of these models. Currently, there are several option pricing models with 6,10 free parameters that deliver a comparable level of performance in synthesizing option prices. The dimension reduction attained here should prove useful in studying the variation over time of option prices. [source] ## MSM Estimators of European Options on Assets with Jumps MATHEMATICAL FINANCE, Issue 2 2001João Amaro de MatosThis paper shows that, under some regularity conditions, the method of simulated moments estimator of European option pricing models developed by Bossaerts and Hillion (1993) can be extended to the case where the prices of the underlying asset follow Lévy processes, which allow for jumps, with no losses on their asymptotic properties, still allowing for the joint test of the model. [source] ## On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing THE JOURNAL OF FINANCE, Issue 2 2007JACOB BOUDOUKHABSTRACT We investigate the empirical implications of using various measures of payout yield rather than dividend yield for asset pricing models. We find statistically and economically significant predictability in the time series when payout (dividends plus repurchases) and net payout (dividends plus repurchases minus issuances) yields are used instead of the dividend yield. Similarly, we find that payout (net payout) yields contains information about the cross section of expected stock returns exceeding that of dividend yields, and that the high minus low payout yield portfolio is a priced factor. [source] ## Specification Analysis of Option Pricing Models Based on Time-Changed Lévy Processes THE JOURNAL OF FINANCE, Issue 3 2004Jing-zhi HuangWe analyze the specifications of option pricing models based on time-changed Lévy processes. We classify option pricing models based on the structure of the jump component in the underlying return process, the source of stochastic volatility, and the specification of the volatility process itself. Our estimation of a variety of model specifications indicates that to better capture the behavior of the S&P 500 index options, we need to incorporate a high frequency jump component in the return process and generate stochastic volatilities from two different sources, the jump component and the diffusion component. [source] ## Asset Trading Volume with Dynamically Complete Markets and Heterogeneous Agents THE JOURNAL OF FINANCE, Issue 5 2003Kenneth L. JuddTrading volume of infinitely lived securities, such as equity, is generically zero in Lucas asset pricing models with heterogeneous agents. More generally, the end-of-period portfolio of all securities is constant over time and states in the generic economy. General equilibrium restrictions rule out trading of equity after an initial period. This result contrasts the prediction of portfolio allocation analyses that portfolio rebalancing motives produce nontrivial trade volume. Therefore, other causes of trade must be present in asset markets with large trading volume. [source] ## Presidential Address: Liquidity and Price Discovery THE JOURNAL OF FINANCE, Issue 4 2003Maureen O'HaraThis paper examines the implications of market microstructure for asset pricing. I argue that asset pricing ignores the central fact that asset prices evolve in markets. Markets provide liquidity and price discovery, and I argue that asset pricing models need to be recast in broader terms to incorporate the transactions costs of liquidity and the risks of price discovery. I argue that symmetric information-based asset pricing models do not work because they assume that the underlying problems of liquidity and price discovery have been solved. I develop an asymmetric information asset pricing model that incorporates these effects. [source] |