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Journal articles on the topic 'Pricing Risk'

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Published: 9 May 2025

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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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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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7

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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8

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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11

Hansen, Lars Peter, and José A. Scheinkman. "Pricing growth-rate risk." Finance and Stochastics 16, no. 1 (September 28, 2010): 1–15. http://dx.doi.org/10.1007/s00780-010-0141-9.

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12

Fiordelisi, Franco, Carlo Palego, Annalisa Richetto, and Giulia Scardozzi. "Risk-Adjusted Loan Pricing." Risk Management Magazine 17, no. 3 (November 19, 2022): 8–24. http://dx.doi.org/10.47473/2020rmm0115.

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We analyze what are the main pricing components for performing loans. By exploiting a survey conducted by the authors in AIFIRM (2021), we provide empirical evidence about whether and to what extent various pricing criteria are related to interest income within the internal model framework. Our main findings are that banks’ interest income is positively related to the adoption of advanced internal risk-based models, the calculation of the break-even rate, and the implementation of the risk-adjusted profitability measures in the pricing, while it is negatively linked to higher market competition, a decentralized pricing function (allowing more customeroriented loans prices). The results make urgent to monitor and develop improve current risk models to support both central offices and the sales network in the process of formulating loan prices and monitoring the value consequently created.

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13

Kamara, Avraham, Robert A. Korajczyk, Xiaoxia Lou, and Ronnie Sadka. "Horizon Pricing." Journal of Financial and Quantitative Analysis 51, no. 6 (December 2016): 1769–93. http://dx.doi.org/10.1017/s0022109016000685.

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The literature documents heterogeneity in the delay of stock price reaction to systematic shocks, implying that asset risk depends on investment horizon. We study the pricing of risk factors across investment horizons. Value (liquidity) risk is priced over intermediate (short) horizons. Conditioning horizon-factor exposures on firm characteristics indicates that characteristics, with the exception of momentum, are not priced beyond their contribution to systematic risk. Long-horizon institutional investors overweight assets with high intermediate-horizon exposures to value risk and high short-horizon exposures to liquidity risk. The results highlight the importance of investment horizon in determining risk premia.

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14

Borkowski, Susan C., and Mary Anne Gaffney. "Proactive Transfer Pricing Risk Management in PATA Countries." Journal of International Accounting Research 13, no. 2 (June 1, 2014): 25–55. http://dx.doi.org/10.2308/jiar-50845.

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ABSTRACT Transnational corporations (TNCs) have long considered transfer pricing as a key tax concern. If stability in transfer pricing is a necessary condition for dynamic cross-border trading, then recent financial reporting changes, updated transfer pricing guidelines, and new reporting requirements for uncertain tax positions are destabilizing influences that must be addressed by companies in order to mitigate their transfer pricing-related exposures and risk. This study reports the results of a survey of tax executives from the four countries comprising the Pacific Association of Tax Administrators (PATA), three of which have transfer pricing regulations based on Organisation for Economic Co-operation and Development (OECD) guidelines. The study seeks to explain how TNCs are managing their transfer pricing risks proactively in today's volatile environment, and if their actions are successful. Findings include contradictory evidence regarding audit risk reduction strategies recommended by tax authorities and the utility of those strategies in actually reducing corporate audit risk. These were surprising results, given that tax authorities and transfer pricing consulting firms tout certain transfer pricing agreements as the best way to mitigate transfer pricing audit risk. At best, are these agreements neutral relative to audit risk? At worst, are tax authorities using these agreements as a source of confidential data for possible future use in both transfer pricing and non-transfer pricing audits?

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15

Zhang, Yaojie, and Benshan Shi. "Systematic risk and deposit insurance pricing." China Finance Review International 7, no. 4 (November 20, 2017): 390–406. http://dx.doi.org/10.1108/cfri-12-2016-0133.

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Purpose The purpose of this paper is to alleviate the moral hazard problem created by deposit insurance and therefore develop a deposit insurance pricing model explicitly considering systematic risk. Design/methodology/approach Using the market model, the authors introduce the systematic risk component consisting of market risk and beta risk. A closed-form solution for the authors’ pricing model is derived based on the option pricing framework. Findings Compared with the authors’, the pricing model that ignores systematic risk underestimates deposit insurance premium, and cannot cover the excessive loss created by systematic risk. To examine the effect of the systematic risk component on the deposit insurance premiums estimated by the authors’ model, this paper also provides empirical evidence from China by regression analysis. The results demonstrate that, in addition to the individual failure risk, the systematic risk component is properly priced and explicitly reflected in the authors’ model. Research limitations/implications More risk factors such as liquidity risk should be introduced in the pricing of deposit insurance. Practical implications Deposit insurance premiums estimated by the authors’ model can alleviate the moral hazard problem that banks have an incentive to take on excessive systematic risk, because substantial higher insurance premiums would be charged in doing so. Originality/value Applying the option pricing theory and market model, this paper develops a deposit insurance pricing model with explicit consideration of systematic risk. The systematic risk component contains not only the market volatility but also the sensitivity of market risk.

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16

Handa, Puneet, and Scott C. Linn. "Arbitrage Pricing with Estimation Risk." Journal of Financial and Quantitative Analysis 28, no. 1 (March 1993): 81. http://dx.doi.org/10.2307/2331152.

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17

Anagnostopoulos, Yiannis, and Milad Abedi. "Risk Pricing in Emerging Economies." International Journal of Finance & Banking Studies (2147-4486) 5, no. 1 (July 21, 2016): 51–72. http://dx.doi.org/10.20525/ijfbs.v5i1.41.

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Iran’s banking industry as a developing country is comparatively very new to risk management practices. An inevitable predictive implication of this rapid growth is the growing concerns with regard to credit risk management which is the motivation of conducting this research. The paper focuses on the credit scoring aspect of credit risk management using both logit and probit regression approaches. Real data on corporate customers are available for conducting this research which is also a contribution to this area for all other developing countries. Our questions focus on how future customers can be classified in terms of credibility, which models and methods are more effective in better capturing risks. Findings suggest that probit approaches are more effective in capturing the significance of variables and goodness-of-fitness tests. Seven variables of the Ohlson O-Score model are used: CL_CA, INTWO, OENEG, TA_TL, SIZE, WCAP_TA, and ROA; two were found to be statistically significant in logit (ROA, TL_TA) and three were statistically significant in probit (ROA, TL_TA, SIZE). Also, CL_CA, ROA, and WCAP_TA were the three variables with an unexpected correlation to the probability of default. The prediction power with the cut-off point is set equal to 26% and 56.91% for defaulted customers in both logit and probit models. However, logit achieved 54.85% correct estimation of defaulted assets, 0.37% more than what probit estimated.

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18

Li, Hongtao, Robert Novy-Marx, and Mihail Velikov. "Liquidity Risk and Asset Pricing." Critical Finance Review 8, no. 1-2 (December 17, 2019): 223–55. http://dx.doi.org/10.1561/104.00000076.

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19

Diop, Allé Nar. "Agricultural Risk Pricing in Senegal." Journal of Mathematical Finance 09, no. 02 (2019): 182–201. http://dx.doi.org/10.4236/jmf.2019.92010.

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20

Tankov, Peter. "Pricing and hedging gap risk." Journal of Computational Finance 13, no. 3 (March 2010): 33–59. http://dx.doi.org/10.21314/jcf.2010.223.

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21

Kawamoto, Atsutaka. "Pricing Principles of Risk Adjustment." Hokengakuzasshi (JOURNAL of INSURANCE SCIENCE) 2016, no. 634 (2016): 634_111–634_136. http://dx.doi.org/10.5609/jsis.2016.634_111.

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22

GETTER, DARRYL E. "Consumer Credit Risk and Pricing." Journal of Consumer Affairs 40, no. 1 (February 24, 2006): 41–63. http://dx.doi.org/10.1111/j.1745-6606.2006.00045.x.

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23

Litterman, Robert. "Pricing Climate Change Risk Appropriately." Financial Analysts Journal 67, no. 5 (September 2011): 4–10. http://dx.doi.org/10.2469/faj.v67.n5.6.

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24

Frano, Andrew J. "Pricing Hazardous‐Waste Risk Revisited." Journal of Management in Engineering 7, no. 4 (October 1991): 428–40. http://dx.doi.org/10.1061/(asce)9742-597x(1991)7:4(428).

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25

Epperson, James F., James B. Kau, Donald C. Keenan, and Walter J. Muller. "Pricing Default Risk in Mortgages." Real Estate Economics 13, no. 3 (September 1985): 261–72. http://dx.doi.org/10.1111/1540-6229.00354.

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26

ALBUQUERQUE, RUI, MARTIN EICHENBAUM, VICTOR XI LUO, and SERGIO REBELO. "Valuation Risk and Asset Pricing." Journal of Finance 71, no. 6 (November 10, 2016): 2861–904. http://dx.doi.org/10.1111/jofi.12437.

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27

Krasny, Yoel. "Asset Pricing with Status Risk." Quarterly Journal of Finance 01, no. 03 (September 2011): 495–549. http://dx.doi.org/10.1142/s2010139211000134.

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This paper examines the impact of status-seeking considerations on investors' portfolio choices and asset prices in a general equilibrium setting. The economy studied in this paper consists of traditional ("Markowitz") investors as well as status-seekers who are concerned about relative wealth. The model highlights the strategic and interdependent nature of portfolio selection in such a setting: Low-status investors look for portfolio choices that maximize their chances of moving up the ladder while high-status investors look to maintain the status quo and hedge against these choices of the low-status investors. In equilibrium, asset returns obey a novel two-factor model in which one factor is the traditional market factor and the other is a particular "high volatility factor" that does not appear to have been identified so far in the theoretical or empirical literature. This two-factor model found significant support when tested with stock market data. Of particular interest is that the model and the empirical results attribute the low returns on idiosyncratic volatility stocks to their covariance with the portfolio of highly volatile stocks held by investors with relatively low status.

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28

Post, Thierry, and Pim van Vliet. "Downside risk and asset pricing." Journal of Banking & Finance 30, no. 3 (March 2006): 823–49. http://dx.doi.org/10.1016/j.jbankfin.2005.06.005.

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ACHARYA, V., and L. PEDERSEN. "Asset pricing with liquidity risk." Journal of Financial Economics 77, no. 2 (August 2005): 375–410. http://dx.doi.org/10.1016/j.jfineco.2004.06.007.

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30

Ngo, M., T. Nguyen, and T. Duong. "Indifference pricing with counterparty risk." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 5 (October 1, 2017): 695–702. http://dx.doi.org/10.1515/bpasts-2017-0074.

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Abstract We present counterparty risk by a jump in the underlying price and a structural change of the price process after the default of the counterparty. The default time is modeled by a default-density approach. Then we study an exponential utility-indifference price of an European option whose underlying asset is exposed to this counterparty risk. Utility-indifference pricing method normally consists in solving two optimization problems. However, by using the minimal entropy martingale measure, we reduce to solving just one optimal control problem. In addition, to overcome the incompleteness obstacle generated by the possible jump and the change in structure of the price process, we employ the BSDE-decomposition approach in order to decompose the problem into a global-before-default optimal control problem and an after-default one. Each problem works in its own complete framework. We demonstrate the result by numerical simulation of an European option price under the impact of jump’s size, intensity of the default, absolute risk aversion and change in the underlying volatility.

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31

Zhao, Jun, Emmanuel Lépinette, and Peibiao Zhao. "Pricing under dynamic risk measures." Open Mathematics 17, no. 1 (August 8, 2019): 894–905. http://dx.doi.org/10.1515/math-2019-0070.

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Abstract In this paper, we study the discrete-time super-replication problem of contingent claims with respect to an acceptable terminal discounted cash flow. Based on the concept of Immediate Profit, i.e., a negative price which super-replicates the zero contingent claim, we establish a weak version of the fundamental theorem of asset pricing. Moreover, time consistency is discussed and we obtain a representation formula for the minimal super-hedging prices of bounded contingent claims.

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32

Sibley, David S. "Public utility pricing under risk." Economics Letters 17, no. 1-2 (January 1985): 153–56. http://dx.doi.org/10.1016/0165-1765(85)90148-x.

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33

Andersen, Per, and Martin Nielsen. "Inelastic sports pricing and risk." Economics Letters 118, no. 2 (February 2013): 262–64. http://dx.doi.org/10.1016/j.econlet.2012.10.025.

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34

Zhang, Yongmin, Shusheng Ding, and Meryem Duygun. "Derivatives pricing with liquidity risk." Journal of Futures Markets 39, no. 11 (April 2019): 1471–85. http://dx.doi.org/10.1002/fut.22008.

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35

Amihud, Yakov, and Haim Mendelson. "The Pricing of Illiquidity as a Characteristic and as Risk." Multinational Finance Journal 19, no. 3 (September 1, 2015): 149–68. http://dx.doi.org/10.17578/19-3-1.

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Koenig, Matthias, and Joern Meissner. "List pricing versus dynamic pricing: Impact on the revenue risk." European Journal of Operational Research 204, no. 3 (August 2010): 505–12. http://dx.doi.org/10.1016/j.ejor.2009.11.020.

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37

Löschenbrand, Stefan, Martin Maier, Laurent Millischer, and Florian Resch. "Credit Risk Where It’s Due." IMF Working Papers 2025, no. 062 (March 2025): 1. https://doi.org/10.5089/9798229005777.001.

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This study investigates carbon pricing-induced credit risk, the potential negative impact of carbon pricing on firms’ ability to meet their financial obligations. Applying a well-established credit assessment model to a novel data set combining financial statements and emissions data, we subject the over 2.5 million borrowers of the euro area’s largest banking groups to two carbon pricing stress scenarios. Our findings reveal a notable variation in impacts between and within sectors. However, even under the conservative scenario, many firms experience only a minimal increase in their probabilities of default. In the more realistic scenario, the aggregate impact on firms’ creditworthiness is not material. The analysis further suggests that the capitalization of euro area banks would not be significantly affected by the carbon pricing-induced increase in corporate credit risk. While this study does not consider the macroeconomic transmission channels, it indicates that higher carbon prices are not likely to trigger widespread firm defaults and jeopardize financial stability.

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38

Liu, Yu, Conglin Hu, Lei Wang, and Kun Yang. "Multilayer Network Risk Factor Pricing Model." Complexity 2020 (November 4, 2020): 1–6. http://dx.doi.org/10.1155/2020/6618853.

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This paper proposes a multilayer network risk factor pricing model to depict the impact of interactions between stocks on excess stock returns by constructing the network risk factor based on the stock multilayer network and introducing it to the traditional three-factor pricing model. According to China’s stock market data, we find that compared with the traditional three-factor model, the multilayer network risk factor pricing model can achieve higher fitting degree. Meanwhile, the multilayer network risk factor has a significant positive impact on the excess stock returns in most cases.

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39

Kleimeier, Stefanie, and Michael Viehs. "Pricing carbon risk: Investor preferences or risk mitigation?" Economics Letters 205 (August 2021): 109936. http://dx.doi.org/10.1016/j.econlet.2021.109936.

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40

Bellenbaum, Reiner. "Reinsurance of Environmental Risk Pricing and Risk Assessment." Geneva Papers on Risk and Insurance - Issues and Practice 20, no. 3 (July 1995): 393–401. http://dx.doi.org/10.1057/gpp.1995.32.

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41

Niederau, Harry, and Peter Zweifel. "Quasi Risk-Neutral Pricing in Insurance." ASTIN Bulletin 39, no. 1 (May 2009): 317–37. http://dx.doi.org/10.2143/ast.39.1.2038067.

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AbstractThis contribution shows that for certain classes of insurance risks, pricing can be based on expected values under a probability measure ℙ* amounting to quasi risk-neutral pricing. This probability measure is unique and optimal in the sense of minimizing the relative entropy with respect to the actuarial probability measure ℙ, which is a common approach in the case of incomplete markets. After expounding the key elements of this theory, an application to a set of industrial property risks is developed, assuming that the severity of losses can be modeled by “Swiss Re Exposure Curves”, as discussed by Bernegger (1997). These curves belong to a parametric family of distribution functions commonly used by pricing actuaries. The quasi risk-neutral pricing approach not only yields risk exposure specific premiums but also Risk Adjusted Capital (RAC) values on the very same level of granularity. By way of contrast, the conventional determination of RAC is typically considered on a portfolio level only.

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42

Li, Xinting, Baochen Yang, Yunpeng Su, and Yunbi An. "Pricing Corporate Bonds with Credit Risk, Liquidity Risk, and Their Correlation." Discrete Dynamics in Nature and Society 2021 (March 2, 2021): 1–14. http://dx.doi.org/10.1155/2021/6681035.

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This paper proposes a generalized bond pricing model, accounting for all the effects of credit risk, liquidity risk, and their correlation. We use an informed trading model to specify the bond liquidity payoff and analyze the sources of liquidity risk. We show that liquidity risk arises from reduced information accuracy and market risk tolerance, and it is market risk tolerance that links credit and liquidity. Then, we extend the traditional bond pricing model with only credit risk by incorporating liquidity risk into the framework in which the probabilities of the two risk events are estimated by a joint distribution. Using numerical examples, we analyze the role of the correlation between credit and liquidity in bond pricing, especially during a financial crisis. We document that the varying correlation between default and illiquidity explains the phenomenon of bond death spiral observed in a financial crisis. Finally, we take the US corporate bond market as an example to demonstrate our conclusions.

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43

Brody, Dorje C., and Lane P. Hughston. "Lévy information and the aggregation of risk aversion." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 469, no. 2154 (June 8, 2013): 20130024. http://dx.doi.org/10.1098/rspa.2013.0024.

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When investors have heterogeneous attitudes towards risk, it is reasonable to assume that each investor has a pricing kernel, and that these individual pricing kernels are aggregated to form a market pricing kernel. The various investors are then buyers or sellers depending on how their individual pricing kernels compare with that of the market. In Brownian-based models, we can represent such heterogeneous attitudes by letting the market price of risk be a random variable, the distribution of which corresponds to the variability of attitude across the market. If the flow of market information is determined by the movements of prices, then neither the Brownian driver nor the market price of risk are directly visible: the filtration is generated by an ‘information process’ given by a combination of the two. We show that the market pricing kernel is then given by the harmonic mean of the individual pricing kernels associated with the various market participants. Remarkably, with an appropriate definition of Lévy information one draws the same conclusion in the case when asset prices can jump. As a consequence, we are led to a rather general scheme for the management of investments in heterogeneous markets subject to jump risk.

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44

Hunt, James M., and Howard Forman. "The role of perceived risk in pricing strategy for industrial products: a point‐of‐view perspective." Journal of Product & Brand Management 15, no. 6 (October 1, 2006): 386–93. http://dx.doi.org/10.1108/10610420610703711.

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PurposeThe purpose of this research paper is to examine the role corporate and individual risk (from the point of view of the pricing manager) plays in developing pricing strategies.Design/methodology/approachManagerial professionals in two graduate business programs were used to assess riskiness associated with pricing strategies. Grounded in attribution theory, t‐tests were used to compare the different types of risk associated with the various pricing strategies.FindingsThe findings suggest that pricing managers will view risk from different perspectives (i.e. corporate and individual) and that this “point of view” should have an impact on the pricing strategies selected.Research limitations/implicationsResearch limitations include the use of graduate students in lieu of actual pricing managers. However, this research is a first step in examining the different perspectives of risk that may be taken by managers.Practical implicationsPricing managers and organizations alike should be made aware of how a point‐of‐view perspective regarding risk can have a significant impact on selecting pricing strategies. The results of the study could provide guidance for corporations so that they can make sure pricing managers have the “correct” point of view regarding the riskiness of pricing strategies.Originality/valueThe research is the first to identify and examine the different risk perspectives. This provides value for academic research because it is the first in the area of risk regarding the different perspectives.

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45

Trinh, Yen Thuan, and Bernard Hanzon. "An introduction to Monte Carlo-Tree (MC-Tree) method." Boolean 2022 VI, no. 1 (December 6, 2022): 94–96. http://dx.doi.org/10.33178/boolean.2022.1.16.

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The article aims to introduce concepts in option pricing and risk management. Pricing and risk management is one of the fundamental problems in financial mathematics. Then readers may explore further to understand how to use mathematical models in pricing and risk management. More specifically, our research introduces a new method called Monte Carlo-Tree (MC-Tree), for option pricing and risk management with high accuracy.

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46

Groot, Oliver, Alexander W. Richter, and Nathaniel A. Throckmorton. "Valuation risk revalued." Quantitative Economics 13, no. 2 (2022): 723–59. http://dx.doi.org/10.3982/qe1779.

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This paper shows the success of valuation risk—time‐preference shocks in Epstein–Zin utility—in resolving asset pricing puzzles rests sensitively on the way it is introduced. The specification used in the literature is at odds with several desirable properties of recursive preferences because the weights in the time‐aggregator do not sum to one. When we revise the specification in a simple asset pricing model the puzzles resurface. However, when estimating a sequence of increasingly rich models, we find valuation risk under the revised specification consistently improves the ability of the models to match asset price and cash‐flow dynamics.

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Ho, Kim Hin David, and Shea Jean Tay. "REIT market efficiency through a binomial option pricing tree approach." Journal of Property Investment & Finance 34, no. 5 (August 1, 2016): 496–520. http://dx.doi.org/10.1108/jpif-01-2016-0004.

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Purpose – The purpose of this paper is to examine the risk neutral and non-risk neutral pricing of Singapore Real Estate Investment Trusts (S-REITs) via comparing the average of the individual ratios (of deviation between expected and observed closing price/observed closing price) with the ratio (of standard deviation/mean) for closing prices via the binomial options pricing tree model. Design/methodology/approach – If the ratio (of standard deviation/mean) ratio > the ratio (of deviation between expected and observed closing price/observed closing price), then the deviation of closing prices from the expected risk neutral prices is not significant and that the S-REIT is consistent with risk neutral pricing. If the ratio (of deviation between expected and observed closing price/observed closing price) is greater, then the S-REIT is not consistent with risk neutral pricing. Findings – Capitacommercial Trust (CCT), Capitamall Trust (CMT) and Keppel Real Estate Investment Trust (REIT) have large positive differences between the two ratios (39.86, 30.79 and 18.96 percent, respectively), implying that these S-REITs are not trading at risk neutral pricing. Suntec REIT has a small positive difference of 2.35 percent between both ratios, implying that it is trading at risk neutral pricing. Ascendas REIT has the largest negative difference between the two ratios at −4.24 percent, to be followed by Mapletree Logistics Trust at −0.44 percent. Both S-REITs are trading at risk neutral pricing. The analysis shows that CCT, CMT and Keppel REIT exhibit risk averse pricing. Research limitations/implications – Results are consistent with prudential asset allocation for viable S-REIT portfolio investing but that not all these S-REITs exhibit strong market efficiency in their pricing. Practical implications – Pricing may be risk neutral over a certain period but investor sentiments, fear of risks and speculative activities could affect an S-REIT’s risk neutrality. Social implications – With enhanced risk diversification activities, the S-REITs should attain risk neutral pricing. Originality/value – Virtually no research of this nature has been undertaken for S-REITS.

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Su, Xiaonan, Wei Wang, and Wensheng Wang. "Pricing Warrant Bonds with Credit Risk under a Jump Diffusion Process." Discrete Dynamics in Nature and Society 2018 (July 8, 2018): 1–10. http://dx.doi.org/10.1155/2018/4601395.

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This article investigates the pricing of the warrant bonds with default risk under a jump diffusion process. We assume that the stock price follows a jump diffusion model while the interest rate and the default intensity have the feature of mean reversion. By the risk neutral pricing theorem, we obtain an explicit pricing formula of the warrant bond. Furthermore, numerical analysis is provided to illustrate the sensitivities of the proposed pricing model.

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MADAN, DILIP B., and WIM SCHOUTENS. "TENOR SPECIFIC PRICING." International Journal of Theoretical and Applied Finance 15, no. 06 (September 2012): 1250043. http://dx.doi.org/10.1142/s0219024912500434.

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Observing that pure discount projection curves are now based on a variety of tenors leads us to enquire into the possibility of theoretically deriving tenor specific zero coupon bond prices. The question then also arises on how to construct tenor specific prices for all financial contracts. Noting that in conic finance one has the law of two prices, bid and ask, that are nonlinear functions of the random variables being priced, we model dynamically consistent sequences of such prices using the theory of nonlinear expectations. The latter theory is closely connected to solutions of backward stochastic difference equations. The drivers for these stochastic difference equations are here constructed using concave distortions that implement risk charges for local tenor specific risks. It is then observed that tenor specific prices given by the mid quotes of bid and ask converge to the risk neutral price as the tenor is decreased and liquidity increased when risk charges are scaled by the tenor. Square root tenor scaling can halt the convergence to risk neutral pricing, preserving bid ask spreads in the limit. The greater liquidity of lower tenors may lead to an increase or decrease in prices depending on whether the lower liquidity of a higher tenor has a mid quote above or below the risk neutral value. Generally for contracts with a large upside and a bounded downside the prices fall with liquidity while the opposite is the case for contracts subject to a large downside and a bounded upside.

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Zou, Leyu. "Option pricing and risk hedging for Apple." BCP Business & Management 32 (November 22, 2022): 189–95. http://dx.doi.org/10.54691/bcpbm.v32i.2887.

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The Black Sholes Merton (BSM) model is one of the fundamental stochastics models in quantitative finance and the Merton Jump diffusion (MJ) model. This paper examines how BSM, and MJ behave on the European pricing based on 10 options chosen for Apple Inc, with BSM using RRS, SSE, and Historical Volatility, and MJ using SSE as calibration methods. Then delta-neutral hedging strategy is performed using the BSM on the historical data collected from the concessive 10 days. The BSM with RRS and SSE when pricing should be preferred, and the results are similar. The MJ and the BSM using Historical Volatility, however, do not work well when pricing. The delta-neutral hedging strategy is not ideal in this case, since it results in lower profits. The result possesses valuable insights for quantitative finance that calibration methods can significantly influence the accuracy of pricing, and the hedging method can limit the maximum profit.

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