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We examine the performance of the Black-Scholes (B-S) formulas around (i.e., before, during and after) two periods of market stress: the subprime crisis (October, 2008) and the onset of the Covid-19 pandemic (March, 2020). This is interesting for two reasons. First, most studies regarding the accuracy of the B-S formulas have been done under regular or normal market conditions (granted, a fuzzy concept), not under conditions of severe market stress. And second, these two crises are quite different in nature. The subprime crisis was generated within a specific segment of the capital markets and affected only some sectors of the economy;the Covid-19 crisis was due to an external shock and still continues to have a much wider impact. We find, in agreement with previous studies under different circumstances, that the accuracy of these formulas is very poor. Moreover, we find that the degree of mispricing, which is significant and in many cases exceed 100%, is manifest in both crises, and in all scenarios analyzed.

Josef Albers’ Homage to the Square series comprises a vast ensemble of compositions based on similar arrays of nested squares. The main difference among these paintings is the colors employed. Therefore, they constitute an almost natural experiment to explore color preferences. We focus on the relationship between the prices paid in public auctions for these paintings and their color attributes over a fourteen-year period. We describe the attributes of the color palette using several color-related metrics aimed at capturing dominant colors, color diversity and contrast, color harmony, and color emotions. We find that color-related metrics explain a great deal of the price variation in Albers’ Squares series. Intriguingly, dominant colors and emotions are the key variables, while color harmony, contrast and diversity play no role at all. We also find that the market favors lighter tones and bluer hues. Additionally, the analyses reveal that Albers, judged by the prices commanded by his paintings, was a quintessential experimentalist ‒ as opposed to a conceptual artist. That is, an artist who kept improving as he gained more experience playing with the same concept over and over. It is worth noting that using market prices to study color preferences or judge aesthetic merits can provide different insights regarding color preferences and color perception, given the fact that most color preference studies are carried out in experimental or artificial settings, where the subjects do not have any direct interests at stake.

Portfolio selection problems have been thoroughly studied under the risk-and-return paradigm introduced by Markowitz. However, the usefulness of this approach has been hindered by some practical considerations that have resulted in poorly diversified portfolios, or, solutions that are extremely sensitive to parameter estimation errors. In this work, we use sampling methods to cope with this issue and compare the merits of two approaches: a sample average approximation approach and a performance-based regularization (PBR) method that appeared recently in the literature. We extend PBR by incorporating three different risk metrics—integrated chance-constraints, quantile deviation, and absolute semi-deviation—and deriving the corresponding regularization formulas. Additionally, a numerical comparison using index-based portfolios is presented using historic data that includes the subprime crisis.

We present a new economic theory of value based on complexity theory. For simplicity, we call this theory ‘complexalism’ (a portmanteau of ‘complexity’ and ‘capitalism’). Complexalism is a framework that establishes valuations by quantifying the present and future complexities of objects and their surroundings. This framework reparameterises questions of economic value into more objectively addressable subcomponents. First, we motivate the importance of developing alternative frameworks for value. Next, we discuss a novel three-dimensional framework to analyse value and the use of effective complexity as a proxy metric of economic value. Finally, we propose explicit methods for quantifying complexity and simulating valuations. The resulting valuations may serve to benchmark prices and can be used in evaluating the market rules of engagement.JEL: B41, B59, C63, D63

1. Arturo Cifuentes 2. Bernardo Pagnoncelli 1. To order reprints of this article, please contact David Rowe at d.rowe{at}pageantmedia.com or 646-891-2157. Molecular biology is undergoing a major shakeup. The cell-as-machine concept, the foundation on which the discipline has built

Traditional valuation methods such as net present value (NPV) utilize increased discount rates to account for risk, in the process introducing a time bias effect that promotes short-termism. Applic...

Providing decision makers and the investment community with transparent methods to value investments in resilience and adaptation measures to protect physical assets from climate change impacts is becoming increasingly critical. To address this need, this paper introduces and utilizes the decoupled net present value (DNPV) valuation methodology. DNPV is a robust method that can incorporate climate change risk into investment analyses. This paper also discusses how the widespread use of traditional valuation methods such as net present value combined with risk-adjusted discount rates introduces a pernicious time bias effect that magnifies stakeholders’ misaligned interests and investment horizons, leading investors, both public and private, to significantly underinvest in resilience and adaptation. Furthermore, because traditional valuation methods cannot correlate physical risks (e.g., loss of revenue due to physical damage or lost access to an asset) with discount rates, investments to reduce climate change risks are largely considered as expenses that make the investment less attractive. The DNPV method addresses this issue and offers a viable alternative that can consistently and transparently quantify all risks (market and non-market) in terms of cash flows. This allows investors and stakeholders to quantify in monetary terms the potential exposure of physical assets to climate-related hazards and assess the effect of decisions to invest in resilience and adaptation measures. With the aid of a simple numerical example, the DNPV method is used to illustrate how such actions can be treated as quantifiable risk reduction investment opportunities that result in better investment decisions.

In defined contribution (DC) pension schemes, the regulator usually imposes asset allocation constraints (minimum and maximum limits by asset class) in order to create funds with different risk–return profiles. In this article, we challenge this approach and show that such funds can exhibit erratic risk–return profiles that deviate significantly from the intended design. We propose to replace all minimum and maximum asset allocation constraints by a single risk metric (or measure) that controls risk directly. Thus, funds with different risk–return profiles can be immediately created by adjusting the risk tolerance parameter accordingly. Using data from the Chilean DC pension system, we show that our approach generates funds whose risk–return profiles are consistently ordered according to the intended design, and outperforms funds created by means of asset allocation limits.

Banking systems are at the center of the financial infrastructure of any country. It has become apparent after the subprime crisis that such systems cannot be studied by looking at their components individually (that is, in isolation). Thus, an integrated approach is needed.In this paper we introduce a numerically friendly, yet general, algorithm that allows us to represent a banking system in a realistic way. We start with a detailed description of the banks' balance sheets and we incorporate two different features to account for connectivity effects: interbank loans and correlated-exposures to a common universe of credit-risk loans. The driving force behind the model is the progressive deterioration of the loan portfolios to which the banks are exposed. This method is useful to identify the weaknesses of a given banking system, and it is also helpful to assess the merits of different potential responses from the regulator's viewpoint. An example based on the banking system of a Latin American country is used to demonstrate the merits of this approach.Some of our findings are in agreement with those of previous authors, namely, that the probability of developing cascades is proportional to the degree of connectivity of the banking networks, and that the maximum resilience for a given set on banks is achieved for some "intermediate" (optimal?) level of connectivity. However, we also find -- in contrast with a few earlier researchers -- that investigating the features of a banking system by collapsing banks individually (and keeping other features unaltered) can give a misleading view of the system resilience.

We propose a two-step hybrid investment strategy suitable for pension funds. Our method consists of an active component (an optimization-based approach to decide the asset allocation), followed by a passive strategy (an index-based approach). We test our strategy with data from the Chilean pension system using two different risk metrics and we show that our approach, in three out of five cases, yields results that are better than those generated by the Chilean fund administrators. In the two cases where our approach underperformed we show that it was the result of excessively tight constraints set up by the regulator.

Credit ratings play an important role in the fixed income market as the entire regulatory framework of this market segment is based on them and a significant part of what investors can and cannot do is dictated by ratings. Also, a number of ratings-based metrics are employed globally to estimate capital reserves, liquidity buffers, and solvency standards for many institutional investors such as insurance companies and pension funds. A critical assumption at the root of this regulatory architecture is that the credit-rating scales of the three leading agencies (Moody’s, Fitch, and Standard & Poor’s) are completely equivalent.In this study we focus on the Mexican fixed income market. We find that the ratings of all three rating agencies exhibit a very high degree of inter-rater reliability. This means that in terms of ranking a group of bonds based on creditworthiness the three rating agencies would produce very similar results.On the other hand, using a non-parametric statistic, the Wilcoxon matched-pairs test, we conclude that there are significant discrepancies among the ratings of the three agencies. This is consistent with a low level of inter-rater agreement detected. These findings challenge the suitability of credit ratings as a useful metric for regulatory purposes as they create the possibility of arbitrage.

We investigate the relationship between crime and property prices in the Santiago (Chile) real estate market. Our study differs from most previous studies in many respects. First, we distinguish between two property types (single-family homes and apartments); and second, we consider twelve different types of crimes (instead of one aggregate indicator). We find that in general crime has a negative effect on property prices, but the size of this effect varies considerably depending on the type of crime. We also find that apartments respond almost immediately to changes in crime rates while single-family homes take longer to respond. And we observe that ranking different crimes based on their (price/crime) elasticities yields almost identical sequences regardless of the type of property or time-lag employed. These results have important public policy implications for they might serve as useful guide to orient and design crime-mitigating measures. Additionally—and similarly to other studies in European and American cities—we find that certain crimes (violent robbery and surprise robbery, in this case) exhibit a positive correlation with price increases: a counterintuitive result that we (like previous authors) report without offering a convincing explanation. Finally, from a methodological standpoint, this study shows the importance to rely on a two-stage least square approach (2SLS) based on instrumental variables (IV) to address the endogeneity-of-crime problem. To this end we employ different instruments depending on the type of crime. And we also demonstrate the benefits of incorporating factor analysis to explore the price-crime dynamics.

We analyze the guarantees that art-auction houses offer from an options viewpoint. This approach allows us to derive analytical expressions to value the positions involved in such arrangements. We further validate these formulas with a Monte Carlo simulation applied to a realistic example. Finally, we show that the risk associated with such guarantees is lower than what is commonly believed by market practitioners, we also expose the dangers of relying on the Black-Scholes model to value such guarantees, and we introduce a simple risk-reward metric that has intuitive appeal.

Mexico and Chile have Defined Contributions (DC) pension systems. In both cases affliates are offered several investment funds with, allegedly, different risk-return profiles. Analyzing actual return data for the April 2008-March 2018 period, and using a number of risk (and return) related metrics, we reach quite different conclusions in relation to such funds. In the case of Mexico, the funds delivered returns according to their intended risk profile, and they are consistently ranked correctly in terms of absolute risk, risk-adjusted returns, and cumulative returns. Chilean funds, on the other hand, exhibited erratic risk-return patterns, with the most conservative fund outperforming the riskiest fund in terms of cumulative returns. Overall our analysis is an indictment on the idea of using asset allocation limits to control portfolio risk (Chile), and supports the view that risk is much better controlled using an overall portfolio-level risk metric (Mexico). Since most pension funds still rely heavily on asset-class limits to manage risk, our results should serve as a serious warning against the danger of relying on this practice.

We identify the three types of risks involved in an art-secured lending operation and present a framework to assess their combined effects via a Monte Carlo simulation. Also, we derive some useful closed-form expressions that are suitable when the collateral consists of only one painting. To help decision-makers and risk managers, we introduce a number of risk-related metrics that provide a detailed characterization of the lending operation risk profile. We conclude that with the customary LTV ratios currently prevalent in the art-based lending business (around 50%), the lender’s exposure is quite bounded. Moreover, the advantages of having a diversified collateral, from a risk perspective, are relevant. Finally, we find that the uncertainty related to the value of the painting is much more important than the uncertainty related to either the credit risk profile of the borrower, or, the artists’ returns during the period the loan remains outstanding.

This article describes an analytical approach to examine the credit-risk behavior of a homogeneous portfolio. The authors demonstrate the usefulness of the approach using a synthetic index linked to high-yield corporate bonds (which resembles a synthetic CDO) and then analyze an actual synthetic CDO transaction. They show that the conventional approach to analyze these structures (Monte Carlo simulations combined with the Gaussian copula) fails to account for the tri-modal nature of the underlying portfolio default distribution, and consequently, risk assessments based on this method give a misguided view of the risk–reward profile of such portfolios. The authors further show that the benefits of portfolio diversification in the context of credit-risk portfolios are limited in high-correlation scenarios. These findings have important implications for risk managers and financial regulators.

Synthetic collateralized debt obligations (CDOs) performed very badly during the subprime crisis: they suffered massive rating downgrades (even at the most senior levels of the capital structure) and inflicted significant losses on investors. Using numerical simulations, this study shows that such structures are highly unstable; minor errors in the basic assumptions could manifest dramatically in the accuracy of CDO rating calculations. Regardless of the quality of the underlying assets, it is impossible to make reliable statements regarding the future performance of a synthetic CDO tranche. Moreover, this study demonstrates that single-point credit risk estimators (in which no attempt at specifying a confidence interval is made) could be especially misleading. Finally, the study suggests that a regulatory framework based on credit ratings as they are presently defined is unlikely to be effective.

We estimate the correlation between the returns of an S&P 500-based portfolio and Renoir paintings. Unlike previous studies that relied on single-point estimates of the correlation to explore the merits of adding art assets to a portfolio of stocks, we rely on a wild bootstrap algorithm to determine confidence intervals for the correlation estimates. We find that these confidence intervals are so wide (a situation not peculiar to our example) that it seems impossible to make absolute remarks about the merits of adding art-related assets to stocks portfolios. Moreover, our results suggest that previous conclusions regarding the correlation between art and stocks should be taken with some scepticism.

This technical note presents a numerical simulation technique to perform valuations of infrastructure projects with minimum revenue guarantees (MRG). It is assumed that the project cash flows—in the absence of the MRG—can be described in a probabilistic fashion by means of a very general multivariate distribution function. Then, the Gaussian copula (a numerical algorithm to generate vectors according to a prespecified probabilistic characterization) is used in combination with the MRG condition to generate a set of plausible cash flow vectors. These vectors form the basis of a Monte Carlo simulation that offers two important advantages: it is easy to implement and it makes no restrictive assumptions regarding the evolution of the cash flows over time. Thus, one can estimate the distribution of a broad set of metrics (net present value, internal rate of return, payback periods, etc.). Additionally, the method does not have any of the typical limitations of real options–based approaches, namely, cas...

Auction-house guarantees are becoming a common feature in the art market. We analyze these guarantees within the framework of financial options. This approach allows us to derive analytical (closed-form) expressions to value these positions, considering both, the case in which the painting is sold, and the case in which the painting goes unsold (“bought in”). In addition, we present several risk metrics that are useful to describe from an intuitive viewpoint the exposure of the auction house, and that of a third party (in case the auction house decides to layoff, fully or partially, the risk associated with offering such guarantees). We demonstrate that the expressions we derive satisfy the put-call parity relationship, and we further validate these formulas with a Monte Carlo simulation applied to a realistic example. We also show that the risk associated with such guarantees is lower than what is commonly believed by market practitioners, and we expose the dangers of relying on the Black-Scholes model to value such guarantees. Finally, having explicit expressions to assess the risk involved in these guarantees helps to bring more transparency to a notoriously opaque segment of the art market.

In defined contribution (DC) pension schemes, the regulator usually imposes asset allocation constraints (minimum and maximum limits by asset class) in order to create funds with different risk-return profiles. In this article we challenge this approach and show that such funds exhibit erratic risk-return profiles that deviate significantly from the intended design. We propose to replace all minimum and maximum asset allocation constraints by a single risk metric (or measure) that controls risk directly. Thus, funds with different risk-return profiles can be immediately created by adjusting the risk tolerance parameter accordingly. We demonstrate the effectiveness of this approach with data from the Chilean DC pension system. Specifically, we show that our approach generates funds whose risk-return profiles are consistently ordered according to the intended design and outperform funds created by means of asset allocation limits.

The relationship between prices of paintings at public auctions and their attributes has received much attention in recent years. However, the effects of color have been mostly absent from these studies. The present study explored the relationship between price and color in Rothko’s post 1950 paintings, which were dominated by color rather than figurative elements. We characterized the color features of the paintings in terms of their dominant hues and luminosity. In addition, we developed two additional metrics to evaluate color contrast, and palette diversity. We found that in general the market favored diverse color compositions, and preferred reds over greens, blues over yellows, and lighter-colored paintings. We also identified two distinct price regimes in the period studied: a first period, dominated by enthusiasm for the artist, regardless of the painting’s characteristics; and a second period, driven by color-related attributes.

In 1980 Chile switched from a state-managed defined-benefit pension system to a defined-contribution scheme based on individual capital accounts. The new system was further refined in 2002 with the introduction of five investment funds, with, allegedly, different risk-return profiles. The funds differ in their portfolio composition which is driven by strict minimum and maximum limits (mostly related to stocks and bonds), dictated by the regulator. We have examined the performance of these funds over a fifteen-year period looking at their returns and actual risk profiles, aided by three rank-order metrics. Unfortunately, our results are unambiguously distressing: while the regulator succeeded in creating five funds with clearly different risk profiles, their risk-adjusted returns as well as their cumulative (absolute) returns are completely at odds with the desired goal. In fact, during long stretches of time the funds exhibited a performance that was exactly the opposite of what it was intended: an indictment on the idea of controlling portfolio risk via asset allocation limits.

Securitization has been a significant breakthrough in our ability to manage financial risk. In the same way that a futures contract permits exposure to price risk to be separated from ownership of a risky asset, securitization allows the separation of many types of risk exposure and other important characteristics from ownership of the securities in which they originate. Credit derivatives, in particular, can eliminate nearly all exposure to default risk for most investors in a pool of credit-risky bonds, even if the credit quality of the underlying securities is not strong. But the financial crisis of 2008 exposed several important facts: These instruments are complicated, their true risk characteristics had not been fully appreciated, and few people really understood them—apparently including some of those who were in the business of buying, selling, and creating them. This article is a basic primer on securitization of credit risk and the derivative securities that are created in the process. It provides an accessible overview that will be useful for teaching undergraduates about securitization of credit risk and as a general introduction to the subject for the non-technically-oriented reader.

We propose a model to assess the credit risk features of fixed income portfolios assuming they can be characterized by two parameters: their default probability and their default correlation. We rely on explicit expressions to assess their credit risk and demonstrate the benefits of our approach in a complex leveraged structure example. We show that using expected loss as a proxy for credit risk is misleading as it does not capture the dispersion effects introduced by correlation. The implications of these findings are relevant for improving current risk management practices and for regulation purposes.

This article introduces a new financial metric for the art market. The metric, which the authors call artistic power value (APV), is based on the price per unit of area (dollars per square centimeter) and is applicable to two-dimensional art objects such as paintings. In addition to its intuitive appeal and ease of computation, this metric has several advantages from the investor’s viewpoint. It makes it easy to 1) estimate price ranges for different artists; 2) perform comparisons among them; 3) follow the evolution of the artists’ creativity cycle over time; and 4) compare, for a single artist, paintings with different subjects or different geometric properties. Additionally, the APV metric facilitates the process of estimating total returns. Finally, due to its transparency, the APV can be used to design derivatives-like instruments that can appeal to both investors and speculators. Several examples validate this metric and demonstrate its usefulness.

Se trata de mostrar en el artículo la importancia que representa para la cultura monolítica la imagen de saurios o lagartos. Si bien es cierto que otras imágenes animales llamaron la atención de arqueólogos e historiadores centrando el foco de atención en el jaguar, la serpiente, el águila, entre otros. La presencia del caimán en las esculturas agustinianas indica un culto al animal que abundaba en el Magdalena. Observando los dibujos desdoblados de las esculturas de San Agustín, elaborados por Velandia (1994), vemos los afilados colmillos del animal, similares a los puntiagudos dientes del jaguar. Y nos sorprende además uno de los resultados obtenidos por Velandia en la parte posterior de la escultura «después de reconstruir lo desarmado fue un taimado caimán que nos mira de frente».

The subprime crisis was an important wake up call for the financial discipline and the academic community. Several fundamental tenets of the field were called into question as the empirical evidence showed that they were less solid than previously believed. Currently, the list of issues under examination is long and challenging. It goes from basic ideas (are markets stable? are investors rational and homogeneous?) to more specialized topics (does the Value-at-Risk metric work? are credit ratings reliable?) The conventional Discounted Cash Flow (DCF) method -- since it did not play any role in the crisis -- has gone so far unexamined. Yet, with or without subprime crisis, it remains one of the weakest and most difficult to defend elements of the financial canon.

We analyze the Solvency II standard formula (SF) for capital risk aggregation in relation to the treatment of operational risk (OR) capital. We show that the SF implicitly assumes that the correlation between OR and the other risks is very high, a situation which seems to be at odds with both, the empirical evidence, and the view of most industry participants. We also show that this formula, which somehow obscures the correlation assumptions, gives different insurance companies, different benefits for diversification effects in relation to OR. Unfortunately, these benefits are based on the relative weights of the six basic capital components and not on any risk-related metric. Hence, contrary to what has been claimed, the SF does give diversification benefits (although minor ones) in relation to OR. Finally, since the SF does not treat the correlation between OR and the other risks explicitly, it provides no incentive to gather data regarding this effect.

The one-factor Gaussian copula method has become the de facto standard to analyze most synthetic collateralized debt obligation structures. Unfortunately, this method produces a peculiar phenomenon known as a correlation smile (the implied correlation determined by the model depends on the CDO tranche one is considering instead of being tranche-independent). Market participants are divided regarding this issue. Many suspect that the correlation smile is caused by a flaw in the above-mentioned modeling strategy although they have been unable to articulate why. Others insist that the smile is actually correct and reveals important and relevant tranche-dependent characteristics, but have failed to produce convincing evidence to support this view. In this article the authors present evidence that the correlation smile is really a by-product (artifact) of an unfortunate modeling strategy and has no financial or market-driven interpretation whatsoever. Moreover, the authors argue that this modeling approach should be abandoned at once.

Credit ratings play an important role in the fixed income market as the entire regulatory framework of this market segment is based on them and a significant part of what investors can and cannot do is dictated by ratings. Also, a number of ratings-based metrics are employed globally to estimate capital reserves, liquidity buffers, and solvency standards for many institutional investors such as insurance companies and pension funds. A critical assumption at the root of this regulatory architecture is that the credit-rating scales of the three leading agencies (Moody’s, Fitch, and Standard & Poor’s) are completely equivalent.In this study we focus on the Mexican fixed income market. We find that the ratings of all three rating agencies exhibit a very high degree of inter-rater reliability. This means that in terms of ranking a group of bonds based on creditworthiness the three rating agencies would produce very similar results.On the other hand, using a non-parametric statistic, the Wilcoxon matched-pairs test, we conclude that there are significant discrepancies among the ratings of the three agencies. This is consistent with a low level of inter-rater agreement detected. These findings challenge the suitability of credit ratings as a useful metric for regulatory purposes as they create the possibility of arbitrage.

We examine the annual returns based on auction data for two groups of artists (Surrealists and Impressionists) and two individual artists (Picasso and Renoir) using hedonic pricing models in combination with a wild bootstrap statistical technique. With this approach we estimate confidence intervals for such returns; we also estimate confidence intervals for the correlations with returns of other type of assets, and risk-return metrics. We find that the confidence intervals associated with these figures of merit are so wide that it is difficult, if not impossible, to derive absolute conclusions or to make meaningful comparisons, with the behavior of other assets. We also observe that relying on single-point estimates of the above-mentioned metrics – without accounting for the corresponding confidence intervals – can lead to erroneous interpretations regarding art-market returns. Moreover, our results suggest that previous studies regarding art market returns, their correlation with broader market indices, and their risk-return profiles, should be re-examined as they were based on single-point estimates of the relevant metrics. These findings might be of interest to researchers who use hedonic pricing models in the analysis of other infrequently traded assets as the number of sales/observations is likely to be rather low.

This paper explores the influence of cash-flow correlations on the behavior of the net present value (NPV) and internal rate of return (IRR) when performing valuations. In general, correlation has a negligible effect on the expected value of both the NPV and IRR. Even in cases of high correlation the IRR distribution departs very little from normality. In cases of moderate to low correlation, very good approximations of the SD of the NPV and IRR can be obtained, assuming that the cash flows are independent. These results, coupled with the simple two-parameter correlation structure investigated, provide a useful framework to perform valuation analysis of large-scale civil engineering projects.

Paintings are − among other things − financial assets. The most basic piece of information regarding a financial asset is probably its return, or, more appropriately, the potential return that it can offer. Not surprisingly, many scholars have devoted a fair amount of effort to explore how to compute returns for such assets. Unfortunately, after more than forty years of efforts, many fundamental issues regarding returns remain unsolved. In this paper we address two issues that somehow have been overlooked by previous research: (i) how the choice of metric influences the computation of estimated returns; and (ii) the importance of taking into consideration the error propagation in the computation of these estimates. We conclude that estimating returns for paintings is far more difficult than previously believed; there is no unique or best solution; and perhaps more relevant, the errors associated with these estimates are quite substantial.

The Capital Asset Pricing Model (CAPM) has been used in a number of studies to explore the features of the art market (or individual artists). We claim that such studies have been based on unsuitable estimates of art market returns in the context of the CAPM methodology. The CAPM calls for employing total returns whereas most art-related studies rely on return estimates based on the time-dummies of hedonic pricing models. This choice is conceptually flawed since it is based on an ideal or average painting whose characteristics do not change overtime, and therefore, it does not capture total returns. We also claim that most indexes used in previous studies as proxy for the market are not able to capture the dynamics of the art market. These two observations call into question at least some of the findings of earlier researchers. Finally, we illustrate these points with several examples using actual auction prices for a group of surrealist artists. We also advance some proposals to overcome the above-mentioned shortcomings.

Notwithstanding almost forty years of efforts, the market for paintings still lacks a widely accepted price index. In this paper, we introduce a simple and intuitive metric to construct such index. Our metric is based on the price of a painting divided by its area. This formulation rests on a solid mathematical foundation as it corresponds to a particular type of hedonic model. However, unlike indexes based on the time-dummy coefficients of conventional hedonic models, this index satisfies the monotonicity condition. We demonstrate with a simple example the advantages of our metric. We also show the dangers of relying on the time-dummy coefficients of conventional hedonic models to estimate returns and generate price indexes., Comment: 15 pages 3 figures

Structured products performed very badly during the subprime crisis. Not only they suffered massive ratings downgrades (even at the most senior levels of the capital structure) but also inflicted significant losses on investors. This study shows, using numerical simulations based on historical data, that such structures were highly unstable. In other words, minor errors in the basic assumptions could manifest themselves dramatically in the accuracy of their ratings. In fact, for all practical purposes, in many cases it was impossible to make a reliable statement regarding the future performance of a synthetic CDO tranche, regardless of the quality of the underlying assets. Moreover, the study demonstrates that single-point credit risk estimators (in which no attempt at specifying a confidence interval is made) could be specially misleading. Finally, the study suggests that a regulatory framework based on ratings “as we know them” is unlikely to be effective and probably much fresh thinking is needed in this area.