Готові списки джерел за темами / Pricing Risk

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Добірка наукової літератури з теми "Pricing Risk"

Автор: Grafiati
Опубліковано: 9 травня 2025

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Статті в журналах з теми "Pricing Risk"

1

Muzychuk, Mariana I. "Risk Assessment Methods of Transfer Pricing." Business Inform 8, no. 547 (2023): 254–63. http://dx.doi.org/10.32983/2222-4459-2023-8-254-263.

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Transfer pricing is one on the greatest problem of the global system of taxation and therefore the efficient TP tax control is of special importance. As the risk-oriented approach allows to improve the TP tax control, tax administrations as well as businesses should apply and develop it for the timely risks identification. This assumption is based on the review of foreign and domestic scientific literature provided in this article. This study aims to analyze the significance of TP risk management system and its impact on the TP tax control and voluntary tax compliance as well as to develop proposals on the TP risks assessment methods, focusing on Ukrainian tax regulation as well as the OECD and the EU tax framework. The research methods include systematic and comparative analysis of scientific literature, deduction, induction, analysis, synthesis and systems approach. To fulfill the objective of this study the analyses of legislative regulation of the TP control at both the international and the country level is provided, focusing on the stage of the monitoring of the controlled transactions. For the enhancement monitoring stage of the TP control the algorithm for the risk identification and assessment for the monitoring of controlled transaction (CT) is suggested. The study also provides for the methodology on comparison of the profitability of taxpayers with the average in the industry and methodology for building the TP risks matrix. The study results revealed the significance of the TP risks management processes standardization that allows its automatization and could contribute to the TP tax control strengthening as well as an TP compliance improvement. The prospects for future research could be focused on development of an algorithm for comparing the prices of CT with the quoted prices for raw materials.
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2

Mahajan, Arvind. "Pricing Expropriation Risk." Financial Management 19, no. 4 (1990): 77. http://dx.doi.org/10.2307/3665612.

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3

Carassus, Laurence, and Miklós Rásonyi. "Risk-Neutral Pricing for Arbitrage Pricing Theory." Journal of Optimization Theory and Applications 186, no. 1 (June 23, 2020): 248–63. http://dx.doi.org/10.1007/s10957-020-01699-6.

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4

Swart, Barbara. "Fair pricing, and pricing paradoxes." South African Journal of Economic and Management Sciences 19, no. 2 (May 13, 2016): 321–29. http://dx.doi.org/10.4102/sajems.v19i2.1136.

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The St Petersburg Paradox revolves round the determination of a fair price for playing the St Petersburg Game. According to the original formulation, the price for the game is infinite, and, therefore, paradoxical. Although the St Petersburg Paradox can be seen as concerning merely a game, Paul Samuelson (1977) calls it a “fascinating chapter in the history of ideas”, a chapter that gave rise to a considerable number of papers over more than 200 years involving fields such as probability theory and economics. In a paper in this journal, Vivian (2013) undertook a numerical investigation of the St Petersburg Game. In this paper, the central issue of the paradox is identified as that of fair (risk-neutral) pricing, which is fundamental in economics and finance and involves important concepts such as no arbitrage, discounting, and risk-neutral measures. The model for the St Petersburg Game as set out in this paper is new and analytical and resolves the so-called pricing paradox by applying a discounting procedure. In this framework, it is shown that there is in fact no infinite price paradox, and simple formulas for obtaining a finite price for the game are also provided.
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5

He, Zhiguo, and Arvind Krishnamurthy. "Intermediary Asset Pricing." American Economic Review 103, no. 2 (April 1, 2013): 732–70. http://dx.doi.org/10.1257/aer.103.2.732.

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We model the dynamics of risk premia during crises in asset markets where the marginal investor is a financial intermediary. Intermediaries face an equity capital constraint. Risk premia rise when the constraint binds, reflecting the capital scarcity. The calibrated model matches the nonlinearity of risk premia during crises and the speed of reversion in risk premia from a crisis back to precrisis levels. We evaluate the effect of three government policies: reducing intermediaries borrowing costs, injecting equity capital, and purchasing distressed assets. Injecting equity capital is particularly effective because it alleviates the equity capital constraint that drives the model's crisis. (JEL E44, G12, G21, G23, G24)
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6

Lane, Morton N. "Pricing Risk Transfer Transactions." ASTIN Bulletin 30, no. 2 (November 2000): 259–93. http://dx.doi.org/10.2143/ast.30.2.504635.

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Should the pricing of reinsurance catastrophes be related to the price of the default risk embedded in corporate bonds?If not, why not?A risk is a risk is a risk, in whatever market it appears. Shouldn't the risk-prices in these different markets be comparable? More basically perhaps, how should reinsurance prices and bond prices be set? How does the market currently set them? These questions are central to the inquiry contained in this paper.Avoiding unnecessary suspense, our answers are: Yes, cat prices should be related to credit prices because both risks contain a characteristic trade-off between the frequency of and severity of adverse events. We leave the question of how prices should be set to others and focus on the empirical question of how they have been set by the markets. In the process, we develop a fairly robust pricing mechanism and explore its potential uses in many different contexts.The 1999 Insurance-Linked Securities (ILS) market (a.k.a., Cat Bond market) provides the empirical springboard to the discussion. The ILS market is only 4 years old. As such, it represents a new and unique intersection of reinsurance and financial markets. It provides a wonderful laboratory for exploring risk-pricing.The ILS market, still in its experimental phase, appears to require more generous (cheap) pricing of insurance risk than does the bond market of default risk. So much so that academics have begun to weigh in on the question of why. Previously, insurance pricing discussions had been confined to practicing insurance professionals, particularly actuaries. For finance professionals, insurance pricing, much less reinsurance pricing, seldom made the index of their financial texts – though even that is beginning to change.
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Sorensen, Eric H., and Thierry F. Bollier. "Pricing Swap Default Risk." Financial Analysts Journal 50, no. 3 (May 1994): 23–33. http://dx.doi.org/10.2469/faj.v50.n3.23.

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Cherny, A. S. "Pricing with Coherent Risk." Theory of Probability & Its Applications 52, no. 3 (January 2008): 389–415. http://dx.doi.org/10.1137/s0040585x97983158.

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9

Frano, Andrew J. "Pricing Hazardous‐Waste Risk." Journal of Management in Engineering 6, no. 1 (January 1990): 76–86. http://dx.doi.org/10.1061/(asce)9742-597x(1990)6:1(76).

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10

Aldy, Joseph E. "Pricing climate risk mitigation." Nature Climate Change 5, no. 5 (April 6, 2015): 396–98. http://dx.doi.org/10.1038/nclimate2540.

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Дисертації з теми "Pricing Risk"

1

Feeney, Paul William. "Euronotes : risk and pricing." Thesis, Bangor University, 1989. https://research.bangor.ac.uk/portal/en/theses/euronotes--risk-and-pricing(ecb4cfb8-601c-47b5-b897-cfefd66cfb37).html.

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2

Lee, Kuan-Hui. "Liquidity risk and asset pricing." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155146069.

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3

Kolman, Marek. "Pricing and modeling credit risk." Doctoral thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-264720.

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Анотація:
The thesis covers a wide range of topics from the credit risk modeling with the emphasis put on pricing of the claims subject to the default risk. Starting with a separate general contingent claim pricing framework the key topics are classified into three fundamental parts: firm-value models, reduced-form models, portfolio problems, with a possible finer sub-classification. Every part provides a theoretical discussion, proposal of self-developed methodologies and related applications that are designed so as to be close to the real-world problems. The text also reveals several new findings from various fields of credit risk modeling. In particular, it is shown (i) that the stock option market is a good source of credit information, (ii) how the reduced-form modeling framework can be extended to capture more complicated problems, (iii) that the double t copula together with a self-developed portfolio modeling framework outperforms the classical Gaussian copula approaches. Many other, partial findings are presented in the relevant chapters and some other results are also discussed in the Appendix.
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4

Ruan, Zheng. "CDS pricing with counterparty risk." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/6083.

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This thesis focuses on the impact of counterparty-risk in CDS (Credit Default Swap) pricing. The exponential growth of the Credit Derivatives Market in the last decade demands an upsurge in the fair valuation of various credit derivatives such as the Credit Default Swap (CDS), the Collateralized Debt Obligation (CDO). Financial institutions suffered great losses from Credit Derivatives in the sub-prime mortgage market during the credit crunch period. Counterparty risk in CDS contracts has been intensively studied with a focus on losses for protection buyers due to joint defaults of counterparty and reference entity. Using a contagion framework introduced by Jarrow and Yu (2001)[48], we calculate the swap premium rate based on the change of measure technique, and further extend both the two-firm and three-firm model (with defaultable protection buyer) with continuous premium payment. The results show more explanatory power than the discrete case. We improve the continuous contagion model by relaxing the constant intensity rate assumption and found close results without loss of generality. Empirically this thesis studies the behaviour of the historical credit spread of 55 sample corporates/ financial institutions, a Cox–Ingersoll–Ross model is applied to calibrate spread parameters. A proxy for counterparty spread is introduced as the difference between the spread over benchmark rate and spread over swap rate for 5 year maturity CDS. We then investigate counterparty risk during the crisis and study the shape of term structure for the counterparty spread, where Rebonato’s framework is deployed to model the dynamics of the term structure using a regime-switching framework.
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5

Dewhirst, Susan. "Pricing of risk on eurocredits /." Genève : l'auteur, 1986. http://catalogue.bnf.fr/ark:/12148/cb349457233.

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6

Lucchetta, Alberto <1995&gt. "Pricing EU Sovereign Debt Risk." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/15939.

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The aim of the analysis is to investigate if bond risk is priced. The idea to examine this topic rises by the interest to analyze in depth the movements of the bond financial market. The general framework reveals an increase of the use of fixed income securities and, consequently, an increase of the use of bonds among investors during last years. Credit institutions started to issue a large multiplicity of financial instruments; an example are perpetual bonds that were not frequently used in the past. Moreover, the increase of government bonds issued was drastic: during the period of the crisis, the European countries frequently asked money to the financial market in order to be able to guarantee their solvability. Consequently, the bond market became more interesting to investors than before and became more attractive and dynamic with respect to the past. The question that I asked to myself was if bonds returns changes affect the decisions of investors and their asset allocation. More in details, it arises the question “Is government bonds risk priced?”. In order to answer this question, the research unfolds in three main paragraphs. In the first part the characteristics of bonds are analyzed and the variables to take into account in order to understand the value of a bond are highlighted. The following features are presented: the relation between the bond and the interest rate, the difference between a long-term and a short-term bond, and some indicators, such as the duration and the Yield curve, that helps to interpret the real movements of a bond. The second part is built in order to investigate the consequences of the 2007 financial crisis and of the crisis of Sovereign Debt. In particular, the information that the Yield curve provides to investors are explained; for example, the analysis of the inversion of the yield curve is one of the main signals of an imminent crisis. Then, the causes and the consequences of the Crisis of Sovereign Debt are studied with the aim to highlight the different economic positions of different countries in the Eurozone. Finally, there is an insight about some strategies of asset allocations commonly used during crisis period that directly affected the bond market. In the Third paragraph, the empirical analysis is deployed. In order to understand if bond risk is priced, a cross sectional asset pricing test is implemented. The process to obtain the final result is composed by a two-step regression. The first regression consists of a time-series regression of the returns of some bond risk factors previously built on the return of some selected portfolios. The second regression is a cross-section regression between the ß obtained in the first regression and the mean of returns of the portfolio selected. The final results are the coefficient "gamma" that are analyzed in order to understand if bond risk is priced. The regressions are done over the period between 01/01/2002 and 01/06/2019. In addition, the same analysis is run also with a specific focus on the crisis period, from 01/01/2011 to 01/01/2016. In this part some statistical insights are presented in order to explain all the passages done to obtain the final results. To complete the analysis, the third part ends with a paragraph in which the weaknesses of the model selected and of the variables used are exposed. Finally, the analysis of results reveals that the bond risk seems to be priced in some circumstances; however, in order to better investigate the topic and to obtain more reliable results, it is probably necessary to develop a different method to run the cross section asset pricing test or, at least, to improve the quality of the method used enhancing the quality of the variables selected.
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7

Ahmed, Hasib. "Pricing of Idiosyncratic Risk in an Intermediary Asset Pricing Model." ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2659.

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Standard asset pricing theories suggest that only systematic risk is priced. Empirical studies report a relationship between idiosyncratic volatility or risk (IVOL) and asset price. The most common explanation for this anomaly is that households under-diversify creating a Bad Model problem. This paper uses an Intermediary Asset Pricing Model (IAPM) as a way to control for under-diversification in evaluating the relationship between IVOL and asset price. We find that IVOL premia is lower in an IAPM. Our findings indicate that under-diversification can explain the anomaly partially.
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8

Watson, Ed. "Pricing credit derivatives and credit risk." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ54085.pdf.

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9

Vliet, Willem Nicolaas van. "Downside Risk And Empirical Asset Pricing." [Rotterdam]: Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam ; Rotterdam : Erasmus University Rotterdam [Host], 2004. http://hdl.handle.net/1765/1819.

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10

Ghunmi, Diana Nawwash Abed El-Hafeth Abu. "Stock return, risk and asset pricing." Thesis, Durham University, 2008. http://etheses.dur.ac.uk/2908/.

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This thesis attempts to address a number of issues that have been identified in the asset pricing literature as essential for shaping stock returns. These issues include the need to uncover the link between the macroeconomic variables and stock returns. In addition to this, is the need to decide, in light of the findings of the literature, whether to advise investors to include idiosyncratic risk and downside risk as risk factors in their asset pricing models. The results presented here suggest, consistent with other previous studies, that stock returns are a function of a number of previously identified risk factors along with the wider set of macroeconomic variables. These macroeconomic variables could be represented by a number of estimated macro factors. However, only one of these estimated factors emerged as significant in explaining the cross-section of stock returns. Nevertheless, it is important to note that the size (SMB) and value (HML) factors remain important factors in explaining the cross sectional returns on UK stocks, even with the existence of the other risk factors. This finding of inability of the examined macroeconomic variables to capture the pricing power of the SMB and the HML may cast doubt on the possibility of finding more macroeconomic factors that are able to account for these two factors in the cross section of returns in the UK. Interestingly, this conclusion seems to contradict previous authors' findings of potential links in the UK market. The results also support past studies that find that downside risk is an important risk factor and by allowing the downside risk premium to vary with business cycle conditions, downside risk might be a better measure of risk than market risk. Nevertheless, this thesis shows that although this finding is applicable in times of economic expansion, during recession, there is no conclusive relationship between . downside risk and stock returns. Furthermore, this thesis supports the studies which find that idiosyncratic risk is not significant in pricing stocks. However in contrast to other studies, it reveals this by showing that time-varying risk could be the reason behind the potentially illusive findings of idiosyncratic risk effect. This thesis confirms that, for London Stock Exchange investors, macroeconomic variables should never be overlooked when estimating stock returns and downside risk could be an influential risk factor.
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Книги з теми "Pricing Risk"

1

Ammann, Manuel. Pricing Derivative Credit Risk. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-22330-7.

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2

Schmid, Bernd. Credit Risk Pricing Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24716-6.

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3

Ammann, Manuel. Pricing derivative credit risk. Berlin: Springer, 1999.

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4

Tapiero, Charles S. Risk Finance and Asset Pricing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9781118268155.

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5

Acharya, Viral V. Asset pricing with liquidity risk. Cambridge, MA: National Bureau of Economic Research, 2004.

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6

Acharya, Viral V. Asset pricing with liquidity risk. Cambridge, Mass: National Bureau of Economic Research, 2004.

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7

Dewhirst, Susan. Pricing of risk on Eurocredits. Genève: Institut universitaire de hautes études internationales, 1986.

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8

Mella-Barral, Pierre. Default risk in asset pricing. London: London School of Economics, Financial Markets Group, 1996.

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9

Lane, Morton. Catastrophe risk pricing: An empirical analysis. [Washington, D.C: World Bank, 2008.

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10

Ammann, Manuel. Pricing derivative credit risk: Manuel Ammann. New York: Springer, 1999.

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Частини книг з теми "Pricing Risk"

1

Willsher, Richard. "Pricing Risk." In Export Finance, 151–53. London: Palgrave Macmillan UK, 1995. http://dx.doi.org/10.1007/978-1-349-13980-4_20.

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2

Evstigneev, Igor V., Thorsten Hens, and Klaus Reiner Schenk-Hoppé. "Risk-Neutral Pricing." In Springer Texts in Business and Economics, 115–23. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16571-4_12.

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Shreve, Steven E. "Risk-Neutral Pricing." In Springer Finance, 209–61. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4296-1_5.

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4

Das, Satyajit. "Pricing Options." In Risk Management and Financial Derivatives, 221–74. London: Palgrave Macmillan UK, 1997. http://dx.doi.org/10.1007/978-1-349-14605-5_5.

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Rogers, Jamie. "Option Pricing Methods." In Strategy, Value and Risk, 90–100. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230353930_12.

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Rogers, Jamie. "Option Pricing Methods." In Strategy, Value and Risk, 181–92. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9780230392687_9.

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Dempsey, Michael. "Option pricing." In Financial Risk Management and Derivative Instruments, 200–212. Milton Park, Abingdon, Oxon ; New York, NY : Routledge, 2021. | Series: Routledge advanced text in economics and finance: Routledge, 2021. http://dx.doi.org/10.4324/9781003132240-15.

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Devonshire-Ellis, Chris, Andy Scott, and Sam Woollard. "Transfer Pricing Risk Management." In Transfer Pricing in China, 35–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16080-6_4.

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Cesari, Giovanni, John Aquilina, Niels Charpillon, Zlatko Filipović, Gordon Lee, and Ion Manda. "Pricing Counterparty Credit Risk." In Modelling, Pricing, and Hedging Counterparty Credit Exposure, 215–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04454-0_14.

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Chan, Raymond H., Yves ZY Guo, Spike T. Lee, and Xun Li. "Risk-Neutral Pricing Framework." In Financial Mathematics, Derivatives and Structured Products, 145–60. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3696-6_13.

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Тези доповідей конференцій з теми "Pricing Risk"

1

Chen, Dangxing, and Yuan Gao. "Attribution Methods in Asset Pricing: Do They Account for Risk?" In 2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr), 1–8. IEEE, 2024. https://doi.org/10.1109/cifer62890.2024.10772752.

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Sun, Shulei, and Weijun Peng. "Pricing Optimizations of Insurance Products Based on Risk Model Under Surrender." In 2024 8th International Symposium on Computer Science and Intelligent Control (ISCSIC), 467–70. IEEE, 2024. https://doi.org/10.1109/iscsic64297.2024.00100.

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Lan, Chunsu, and Zehao Wang. "Risk Assessment and Pricing Model of Natural Disaster Insurance Based on EVM-Topsis." In 2024 International Conference on Data Science and Network Security (ICDSNS), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10690990.

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Asri, Marselinus. "Idiosyncratic Risk And Asset Pricing." In 2nd International Conference on Accounting, Management, and Economics 2017 (ICAME 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/icame-17.2017.12.

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Du, Jun, and Yang Liu. "Credit Risk Pricing with Multivariate Stochastic Volatility." In 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.50.

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Halil Paino, PhD, and Wan Mardyatul Miza Wan Tahir. "Financial reporting risk assessment and audit pricing." In 2012 IEEE Symposium on Business, Engineering and Industrial Applications (ISBEIA). IEEE, 2012. http://dx.doi.org/10.1109/isbeia.2012.6423014.

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Chen Yang, Qunfang Bao, Shenghong Li, and Guimei Liu. "Pricing credit spread option with counterparty risk." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5622881.

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8

Sanjana, N. B., M. Ishwarya, N. Balaji, and E. P. Siva. "Risk based pricing using k-means clustering." In 2ND INTERNATIONAL CONFERENCE ON MATHEMATICAL TECHNIQUES AND APPLICATIONS: ICMTA2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0108665.

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9

Zhang, Chi, Chetan Gupta, Seiji Joichi, Ahmed Farahat, and Huijuan Shao. "Risk-Based Dynamic Pricing via Failure Prediction." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00030.

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10

Martin, Todd, and Kuo-Chu Chang. "Risk-based pricing for secondary spectrum access." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009842.

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Звіти організацій з теми "Pricing Risk"

1

Albuquerque, Rui, Martin Eichenbaum, and Sergio Rebelo. Valuation Risk and Asset Pricing. Cambridge, MA: National Bureau of Economic Research, December 2012. http://dx.doi.org/10.3386/w18617.

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2

Acharya, Viral, and Lasse Heje Pedersen. Asset Pricing with Liquidity Risk. Cambridge, MA: National Bureau of Economic Research, October 2004. http://dx.doi.org/10.3386/w10814.

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3

Barro, Robert, and Gordon Liao. Options-Pricing Formula with Disaster Risk. Cambridge, MA: National Bureau of Economic Research, January 2016. http://dx.doi.org/10.3386/w21888.

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4

Bolton, Patrick, and Marcin Kacperczyk. Global Pricing of Carbon-Transition Risk. Cambridge, MA: National Bureau of Economic Research, February 2021. http://dx.doi.org/10.3386/w28510.

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5

Friedman, Benjamin, and Kenneth Kuttner. Time-Varying Risk Perceptions and the Pricing of Risky Assets. Cambridge, MA: National Bureau of Economic Research, August 1988. http://dx.doi.org/10.3386/w2694.

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6

Ai, Hengjie, and Anmol Bhandari. Asset Pricing with Endogenously Uninsurable Tail Risk. Cambridge, MA: National Bureau of Economic Research, August 2018. http://dx.doi.org/10.3386/w24972.

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7

Constantinides, George, and Anisha Ghosh. Asset Pricing with Countercyclical Household Consumption Risk. Cambridge, MA: National Bureau of Economic Research, May 2014. http://dx.doi.org/10.3386/w20110.

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8

Lettau, Martin, Sydney Ludvigson, and Sai Ma. Capital Share Risk in U.S. Asset Pricing. Cambridge, MA: National Bureau of Economic Research, December 2014. http://dx.doi.org/10.3386/w20744.

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9

Biais, Bruno, Johan Hombert, and Pierre-Olivier Weill. Incentive Constrained Risk Sharing, Segmentation, and Asset Pricing. Cambridge, MA: National Bureau of Economic Research, November 2017. http://dx.doi.org/10.3386/w23986.

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10

Tsai, Jerry, and Jessica Wachter. Disaster Risk and its Implications for Asset Pricing. Cambridge, MA: National Bureau of Economic Research, February 2015. http://dx.doi.org/10.3386/w20926.

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