Rachel A.J. Pownall

VaR-x: fat tails in financial risk management

To ensure a competent regulatory framework with respect to Value-at-Risk for Establishing Banks capital adequacy requirements, as promoted by the Basle Committee, then the parametrical approach to estimate VaR needs to incorporte fat tails, apparent in the return distributions of financial assets. This paper provides a simple method to obtain accurate parametric VaR-x mesures, by including a specific measure for the tail fatness of an assets return distribution.

Capturing downside risk in financial markets: the case of the Asian Crisis

Using data on Asian equity markets, we observe that during periods of financial turmoil, deviations from the mean-variance framework become more severe, resulting in periods with additional downside risk to investors. Current risk management techniques failing to take this additional downside risk into account will underestimate the true Value-at-Risk with greater severity during periods of financial turnoil. We provide a conditional approach to the Value-at-Risk methodology, known as conditional VaR-x, which to capture the time variation of non-normalities allows for additional tail fatness in the distribution of expected returns. These conditional VaR-x estimates are then compared to those based on the RiskMetricsTM methodology from J.P. Morgan, where we find that the model provides improved forecasts of the Value-at-Risk. We are therefore able to show that our conditional VaR-x estimates are better able to capture the nature of downside risk, particularly crucial in times of financial crises.

Increased Correlation in Bear Markets: A Downside Risk Perspective

A number of studies have provided evidence of increased correlations in global financial market returns during bear markets. Other studies, however, have shown that some of this evidence may be biased. We derive an alternative to previous estimators for implied correlation that is based on measures of portfolio downside risk and that does not suffer from bias. The unbiased quantile correlation estimates are directly applicable to portfolio optimization and to risk management techniques in general. This simple and practical method captures the increasing correlation in extreme market conditions while providing a pragmatic approach to understanding correlation structure in multivariate return distributions. Based on data for international equity markets, we found evidence of significant increased correlation in international equity returns in bear markets. This finding proves the importance of providing a tail-adjusted mean–variance covariance matrix. A generally accepted concept today is that, over time, returns when the markets are experiencing large negative movements are more highly correlated than returns during more normal times. If true, this phenomenon has serious implications for portfolio and risk management because it means that the benefits of diversification are curtailed precisely when investors most need them. The correlation, however, depends on how the returns are conditioned on the size of the returns. Previous studies have provided alternative correlation structures with which to compare conditional empirical correlations, but these estimates have upward or downward biases that need to be corrected. In this article, we provide a quantile correlation approach that is not biased by the size of the return distribution. The result is a simple and pragmatic approach to estimating correlations conditional on the size of the returns. Based on empirical data, we show how the correlation estimates can be used directly in portfolio and risk management. We derive a conditional correlation structure based on the quantile of the joint return distribution; that is, correlation is conditioned on the size of the return distribution. In a bivariate framework, the correlation is estimated by using those observations that fall below the portfolio return of the two assets. The approach is thus in line with current correlation measures used in Markowitz-style portfolio analysis and in current risk management techniques. The quantile correlation structure is determined by the weights of the assets in the portfolio and the quantile estimates of the distribution of returns on the two assets and of the portfolio return. When the distribution is normal, the conditional correlation structure is constant; hence, the conditional quantile correlation will equal the unconditional correlation. Therefore, because the correlation structure is constant over the distribution for normality, one can easily compare empirical estimates of conditional correlation with their theoretical values under conditions of normal distribution. We examine a variety of daily returns from international stock market indexes for the period May 1990 through December 1999 to establish, first, their unconditional correlations. For example, this procedure produced a correlation between the U.S. market (S&P 500 Index) and the U.K. market (the FTSE 100 Index) of 0.349. Assuming bivariate normality for the whole distribution, we would expect the quantile conditional correlation also to be 0.349. For quantiles up to the 95 percent level, we found that the assumption of normality cannot be rejected at the 95 percent confidence level for all the series. For higher quantiles, however—that is, large negative returns in the bivariate return distribution—the conditional correlation structure increased the correlations; in the case of the U.S. and U.K. markets, the correlation increased to 0.457. The effect on mean–variance portfolio optimization is a reduction in the recommended weight of the risky assets held in the portfolio. These results imply that the gains from diversification are not reaped in periods when diversification benefits are most crucial from a mean–variance perspective—in bear markets. Practitioners, therefore, need to know what sort of model is generating the correlations they are relying on. If the underlying model assumed normality, then the correlation estimates used in the model need to be adjusted to incorporate the bear markets higher-than-normal correlation structure.

Going green: does it depend on education, gender or income?

Sustainable development entails meeting our present needs without compromising the ability of future generations to meet their needs. This requires us to treat economic, social and environmental aspects in an integrated way, but little is known about the nature of individual preferences towards the trade-offs involved in this effort. For the first time, we study individual preferences towards the environment, social wellbeing, and financial wellbeing using a survey of over 1400 households in the Netherlands. Using nonparametric, parametric, and matching methods, we find that gender and education are important factors for sustainability rather than income levels. Moreover results indicate that educated females put the greatest value on going green whilst being socially minded.

Repeat‐Sales Indexes: Estimation without Assuming that Errors in Asset Returns Are Independently Distributed

This paper proposes an alternative specification for the second stage of the Case-Shiller repeat sales method. This specification is based on serial correlation in the deviations from the mean one-period returns on the underlying individual assets, whereas the original Case-Shiller method assumes that the deviations from mean returns by the underlying individual assets are i.i.d. The methodology proposed in this paper is easy to implement and provides more accurate estimates of the standard errors of returns under serial correlation. The repeat sales methodology is generally used to construct an index of prices or returns for unique, infrequently traded assets such as houses, art, and musical instruments which are likely to be prone to exhibit serial correlation in returns. We demonstrate our methodology on a dataset of art prices and on a dataset of real estate prices from the city of Amsterdam.

Reference Point Heterogeneity

It is well-established that, when confronted with a decision to be taken under risk, individuals use reference payoff levels as important inputs. The purpose of this paper is to study which reference points characterize decisions in a setting in which there are several plausible reference levels of payoff. We report an experiment, in which we investigate which of four potential reference points: (1) a population average payoff level, (2) the announced expected payoff of peers in a similar decision situation, (3) a historical average level of earnings that others have received in the same task, and (4) an announced anticipated individual payoff level, best describes decisions in a decontextualized risky decision making task. We find heterogeneity among individuals in the reference points they employ. The population average payoff level is the modal reference point, followed by experimenters stated expectation of a participants individual earnings, followed in turn by the average earnings of other participants in previous sessions of the same experiment. A sizeable share of individuals show multiple reference points simultaneously. The reference point that best fits the choices of the individual is not affected by a shock to her income.

Aspirations, Well-Being, Risk-Aversion and Loss-Aversion

Financial well-being is distinct from income. Some people with high incomes suffer low financial well-being, as their incomes fall short of their aspirations. Such people feel propelled to reach their aspirations by taking risk and willing to bear losses. Conversely, some people with low incomes enjoy high financial well-being, as their incomes exceed their aspirations. We find that people whose aspirations exceed their income are less risk-averse and less loss-averse than people whose incomes exceed their aspirations. We also find that competitive and status-seeking people are less risk-averse than people who are less competitive and status-seeking, and that status-seeking people are less loss-averse than people who are not as status-seeking.

Millionaire investors: financial advisors, attribution theory and gender differences

ABSTRACT To date little attention has been paid to how social cognitive bias can influence how financial advisors interpret and respond to the needs of millionaire investors, and if this varies depending on the gender of the investor. This research investigates whether experienced professional financial advisors who work with millionaire investors make different attributions for the control and knowledge that investors have of their investments, and if they make different investment portfolio recommendations to equivalent male and female investors. Using methodology novel to finance, this vignette-based study that controls for gender finds evidence that professional financial advisors judge millionaire female investors to have less control over their investment portfolios relative to men. Empirical results also show that female advisors judge women to be less knowledgeable about investments than men. Despite such perceptual differences, advisors recommend equally risky portfolios to male and female investors. These results have implications for wealth management institutions and the monitoring of financial advisors for millionaire individuals.

Dealer Networks in the World of Art

We apply network theory to study auction outcomes in the fine art market. Using a unique historical data set, of London-based art auctions that took place between 1741 and 1913, we investigate the drivers of strategic network formation between dealers (buyers) and sellers and the effect of network structure on artwork prices and market exit. The network size and similarities in art specialization between trading partners strongly influence the decision to form links. A larger network and a higher degree of specialization exacerbate informational asymmetries across buyers leading to higher rents through lower prices and facilitate longer market presence.

Market Evolution, Bidding Strategies, and Survival of Art Dealers

We show the value of expertise during the evolution of a market characterized by asymmetric information. Using a unique historical data set, we show how market dynamics encourage entrants. Our results provide evidence that better informed dealers pay about 24% more for an artwork of the same quality than less informed dealers. Additionally, our results indicate that informed dealers are more likely to survive in the market. Our evidence supports the conjecture that, in common value auctions, when information asymmetries are present, dealers with better information benefit. These results have important implications for maintaining and sustaining competitive advantage.We show the value of expertise during the evolution of a market characterized by asymmetric information. Using a unique historical data set, we show how market dynamics encourage entrants. Our results provide evidence that better informed dealers pay about 24% more for an artwork of the same quality than less informed dealers. Additionally, our results indicate that informed dealers are more likely to survive in the market. Our evidence supports the conjecture that, in common value auctions, when information asymmetries are present, dealers with better information benefit. These results have important implications for maintaining and sustaining competitive advantage.