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This chapter analyzes the empirical performance of alternative option pricing models using Black and Scholes (1973) as a benchmark. Specifically, we consider the Heston (1993) and Corrado and Su (1996) models and price call options on the S&P 500 index over the period from November 2010 to April 2011, evaluating each model by computing in- and out-of-sample pricing errors. We find that the two proposed models reduce both types of errors and mitigate the smile effect with respect to the benchmark. Moreover, in most of the cases, the model in Corrado and Su (1996) beats that in Heston (1993). Then, we conclude that skewness and kurtosis matter for option pricing purposes.

This chapter takes a closer look at outliers and extreme outliers identified in the data derived from a complete case treatment of missing values in the European and North American datasets and consistently observe significant negatively skewed distributions with high excess kurtosis across all industries. We then plot the density functions for return on assets (ROA) across different industries in the two datasets and find pervasive observations in the tails where negative returns and outlying observations constitute a frequent and recurring phenomenon. We analyze the persistency of outliers and find noticeable percentages of outlying over- and underperformers hovering around 3–6% dependent on industry context. We further analyze potential size effects associated with extreme negative skewness but do not find that (even sizeable) elimination of extreme values reduce the phenomenon. Finally, we analyze the percentage of firm observations that must be eliminated to reach at distributions that fulfill the characteristics of a normal distribution and reach at a substantial percentage of around 5–10% dependent on industry. To conclude, the often-assumed normally distributed performance outcomes are typically wrong and discards the substantial number of outliers in the samples.

This chapter aims to find an optimal way to hedge foreign exchange exposures on three main currency pairs being the EURUSD, EURGBP and EURJPY. Furthermore, it analyses the risk level of each portfolio together with its kurtosis level. This chapter also looks into the relationship between the EURUSD portfolios and the VIX level.

This study is based on a back-testing analysis over a period of seven years starting in January 2007 and ending in December 2014. Two main Foreign Exchange Premium-Free strategies were structured using the Bloomberg Terminal. These were the ‘At-Expiry Forward Extra’ and the ‘Window Forward Extra’. Portfolios were created using FX options strategies, FX spot and FX forwards. The EURUSD portfolios were also analysed and compared with the VIX level in order to see whether volatility has a direct effect on the outcome of the strategies. The statistical significance of the difference between returns of portfolios was analysed using a paired sample t-test. Finally, the histogram and distribution curve of each portfolio were created and plotted in order to provide a more visual analysis of returns.

It was found that the optimal strategies in all cases were the FX option strategies. The portfolios’ risk was analysed and indicated that optimal portfolios do not necessarily derive the lowest risk. It was also found that with a high VIX level, the forward contract was the most beneficial whilst the option strategy benefited from a low VIX level. When testing for statistical significance between returns of different portfolios, in most cases, the difference in returns between portfolios resulted to be statistically insignificant. Although some similarities were noticed in distribution curves, these differed from the normal distribution. When analysing the kurtosis levels, it is found that such levels differed from that of a normal distribution which has a kurtosis level of 3. Interpretation of such histograms, distribution curves and the kurtosis analysis was explained.

This chapter first analyzes how the data-cleaning process affects the share of missing values in the extracted European and North American datasets. It then moves on to examine how three different approaches to treat the issue of missing values, Complete Case, Multiple Imputation Chained Equations (MICE), and K-Nearest Neighbor (KNN) imputations affect the number of firms and their average lifespan in the datasets compared to the original sample and assessed across different SIC industry divisions. This is extended to consider implied effects on the distribution of a key performance indicator, return on assets (ROA), calculating skewness and kurtosis measures for each of the treatment methods and across industry contexts. This consistently shows highly negatively skewed distributions with high positive excess kurtosis across all the industries where the KNN imputation treatment creates results with distribution characteristics that are closest to the original untreated data. We further analyze the persistency of the (extreme) left-skewed tails measured in terms of the share of outliers and extreme outliers, which shows consistent and rather high percentages of outliers around 15% of the full sample and extreme outliers around 7.5% indicating pervasive skewness in the data. Of the three alternative approaches to deal with missing values, the KNN imputation treatment is found to be the method that generates final datasets that most closely resemble the original data even though the Complete Case approach remains the norm in mainstream studies. One consequence of this is that most empirical studies are likely to underestimate the prevalence of extreme negative performance outcomes.

In this chapter, we perform more detailed analyses and present the distribution characteristics and risk-return relationships of accounting-based financial returns (ROA) across different industry contexts and between periods with different economic conditions. We first display the frequency diagrams of the return measure (ROA) and its two components, net income and total assets, that show entirely different contours in the density graphs that must be reconciled. This is partially accomplished by analyzing the skewness, kurtosis, cross-sectional, and longitudinal risk-return characteristics of each of the three variables. The analyses further considers potential effects of accounting manipulation, and different organizational and executive traits, that identifies significant effects on the accounting-based return measures. We find extremely left-skewed return distributions with high negative correlations between the average return and risk measures, which reproduces the “Bowman paradox” as originally conceived. The same analysis is performed on net income and operating cash flows, the latter being less susceptible to accounting manipulation, which should display similar effects even though these performance distributions show positive skewness. We find negative but insignificant cross-sectional risk-return relations that nevertheless reappear in analyses performed within the specific industry contexts. The study further uncovers effects from prevailing economic conditions where left-skewness and kurtosis as well as negative risk-return correlations are much more significant during periods of high economic growth and business expansion where competition is more pronounced.

A common feature of certain kinds of data is a high level of statistical dependence across space and time. This spatial and temporal dependence contains useful information that can be exploited to significantly reduce the uncertainty surrounding local distributions. This chapter develops a methodology for inferring local distributions that incorporates these dependencies. The approach accommodates active learning over space and time, and from aggregate data and distributions to disaggregate individual data and distributions. We combine data sets on Kansas winter wheat yields – annual county-level yields over the period from 1947 through 2000 for all 105 counties in the state of Kansas, and 20,720 individual farm-level sample moments, based on ten years of the reported actual production histories for the winter wheat yields of farmers participating in the United States Department of Agriculture Federal Crop Insurance Corporation Multiple Peril Crop Insurance Program in each of the years 1991–2000. We derive a learning rule that combines statewide, county, and local farm-level data using Bayes’ rule to estimate the moments of individual farm-level crop yield distributions. Information theory and the maximum entropy criterion are used to estimate farm-level crop yield densities from these moments. These posterior densities are found to substantially reduce the bias and volatility of crop insurance premium rates.

We extend earlier work on the NoVaS transformation approach introduced by Politis (2003a, 2003b). The proposed approach is model-free and especially relevant when making forecasts in the context of model uncertainty and structural breaks. We introduce a new implied distribution in the context of NoVaS, a number of additional methods for implementing NoVaS, and we examine the relative forecasting performance of NoVaS for making volatility predictions using real and simulated time series. We pay particular attention to data-generating processes with varying coefficients and structural breaks. Our results clearly indicate that the NoVaS approach outperforms GARCH model forecasts in all cases we examined, except (as expected) when the data-generating process is itself a GARCH model.

The expanded sovereign bond portfolios from the sizeable public interventions in the financial sector during the current crisis need close monitoring and analysis of emerging vulnerabilities. This chapter presents some conventional and new measures of market, credit, and liquidity risks for government bond portfolios, considered from the perspective of a sovereign debt manager. In particular, it examines duration, convexity, and VaR statistics as measures of market exposure; the contingent-claims approach as the most promising measure of credit risk exposure; and a VaR statistic as a measure of liquidity risk.

This study takes a novel approach to testing the efficacy of technical analysis. Rather than testing specific trading rules as is typically done in the literature, we rely on institutional portfolio managers’ statements about whether and how intensely they use technical analysis, irrespective of the form in which they implement it. In our sample of more than 10,000 portfolios, about one-third of actively managed equity and balanced funds use technical analysis. We compare the investment performance of funds that use technical analysis versus those that do not, using five metrics. Mean and median (3 and 4-factor) alpha values are generally slightly higher for a cross section of funds using technical analysis, but performance volatility is also higher. Benchmark-adjusted returns are also higher, particularly when market prices are declining. The most remarkable finding is that portfolios with greater reliance on technical analysis have elevated skewness and kurtosis levels relative to portfolios that do not use technical analysis. Funds using technical analysis appear to have provided a meaningful advantage to their investors, albeit in an unexpected way.

In view of a disruptive environment, the authors consider theories that explain left-skewed performance outcomes and inverse risk–return relationships where some rationales imply causal dependencies with slightly differing outcomes while others refer to spurious artifacts. These literatures are briefly outlined and dynamic response capabilities introduced as an alternative perspective expressed as strategic responsiveness where commonly observed performance outcomes derive from heterogeneous response capabilities among firms that compete in dynamic environments. Financial performance outcomes are analyzed empirically based on a comprehensive corporate dataset where computational simulations of adaptive strategy making among firms generate comparable outcomes from a simple strategic responsiveness model. The findings demonstrate how diverse adaptive strategy-making processes can generate a substantial part, if not all, of the commonly observed artifacts of firm financial performance. The implications of these results are discussed pointing to propitious approaches of analyzing the impact of dynamic adaptive strategies.