Beyond CAPM: estimating the cost of equity considering idiosyncratic risks

The present study proposes a new evaluation approach aimed at estimating the cost of equity through standardized models which consider an innovative set of firm-specific information on the main unsystematic risks which are typical of any business. Our objective is extending the Capital Asset Pricing Model (CAPM) by defining a standard formula for quantifying the premium for certain idiosyncratic risks as a function of a new set of firm-specific quantitative information. We define two econometric models, for listed and non-listed firms respectively, which consider five idiosyncratic risk factors: firm size, value factor, operating risks, financial structure and stock market price volatility. The models were tested on a sample of European non-financial companies. The empirical results show that while the CAPM systematically underestimates the cost of equity, the proposed models correctly estimate its expected value; furthermore, they show a slight improvement also in terms of estimates’ volatility. Due to their efficacy and ease of use, the proposed models represent a valid practical tool for investors, analysts and professional evaluators. This work contributes to the existing literature by proposing a typologically innovative extension of the CAPM set of explanatory variables, defining and testing new models for the estimation of the unsystematic risks’ spread of the cost of equity based on an original set of firm-specific accounting and market information.     

L‐moments skewness and kurtosis as measures of regional convergence and cohesion

The paper deals with the statistical modeling of convergence and cohesion over time with the use of kurtosis, skewness and L‐moments. Changes in the shape of the distribution related to the spatial allocation of socio‐economic phenomena are considered as an evidence of global shift, divergence or convergence. Cross‐sectional time‐series statistical modeling of variables of interest is to overpass the minors of econometric theoretical models of convergence and cohesion determinants. L‐moments perform much more stable and interpretable than classical measures. Empirical evidence of panel data proves that one pure pattern (global shift, polarization or cohesion) rarely exists and joint analysis is required.     

Determinants of idiosyncratic volatility: Evidence from the Indian stock market

This paper investigates whether firm-specific characteristics explain idiosyncratic volatility in the stocks of non-financial firms traded in the Indian stock market. It employs the linear time series five-factor model, augmented with a liquidity factor and the conditional EGARCH model, to extract yearly idiosyncratic volatility. We estimate a panel data regression to quantify the relationship between firm-specific characteristics and the volatility of individual securities. The results show that idiosyncratic volatility is significant in emerging markets such as India, and that cross-sectional return variations of firms are associated with firm-specific characteristics such as firm size, book-to-market ratio, momentum, liquidity, cash flow-to-price ratio, and returns on assets. We find that the idiosyncratic risk documented in this study is associated with smaller size of company, higher liquidity, low momentum, high book-to-market ratio, and low cash flow-to-price ratio. The findings suggest need to develop alternative tools to make investment decisions in emerging markets.     

Preferences for skewness: evidence from a binary choice experiment

In this paper, we experimentally test skewness preferences at the individual level. Several prospects that can be ordered with respect to the third-degree stochastic dominance criterion are ranked by the participants of the experiment. We find that the skewness of a distribution has a significant impact on the decisions. Yet, while skewness has an impact, its direction differs substantially across subjects: 39% of our subjects demonstrate a statistically significant preference for skewness and 10% seem to avoid skewness (at 5% level). On the level of individual decisions we find that the variances of the prospects and subjects’ experience increase the probability of choosing the lottery with greater skewness.     

House price risk and the hedging benefits of home ownership

Using a repeat-sales methodology, this paper finds that estimates of house price risk based on aggregate house price indices substantially underestimate the true size of house price risk. This is the result of the fact that aggregate house price indices average away the idiosyncratic volatility in house prices. Additional results show that the idiosyncratic risk exceeds the hedging benefits of home ownership. These results imply that for many home owners, owning a house may well add more price risk than it hedges away. These findings are based on a detailed dataset of individual housing transactions in the Netherlands.     

Effects of intervaling on high-frequency realized higher-order moments

In high-frequency finance, the statistical terms ‘realized skewness’ and ‘realized kurtosis’ refer to the realized third- and fourth-order moments of high-frequency returns data normalized (or divided) by ‘realized variance’. In particular, before any computations of these two normalized realized moments are carried out, one often predetermines the holding-interval and sampling-interval and thus implicitly influencing the actual magnitudes of the computed values of the normalized realized higher-order moments i.e. they have been found to be interval-variant. To-date, little theoretical or empirical studies have been undertaken in the high-frequency finance literature to properly investigate and understand the effects of these two types of intervalings on the behaviour of the ensuring measures of realized skewness and realized kurtosis. This paper fills this gap by theoretically and empirically analyzing as to why and how these two normalized realized higher-order moments of market returns are influenced by the selected holding-interval and sampling-interval. Using simulated and price index data from the G7 countries, we then proceed to illustrate via count-based signature plots, the theoretical and empirical relationships between the realized higher-order moments and the sampling-intervals and holding-intervals.     

Asymmetry and downside risk in foreign exchange markets

Abstract

This paper evaluates the double gamma distribution as a means of modelling asymmetry in the conditional distribution of financial data. To do this the model is applied to ten exchange rate series covering mature and emerging market countries. A second contribution of this paper is to highlight the link between the double gamma distribution and the measurement of the second lower partial moment (or semi-variance). The resulting empirical performance of the double gamma model is found to be mixed when compared to a symmetric GARCH-t model. Estimates of conditional downside risk based on the double gamma model are constructed for each series. The results for the Malaysian Riggit, Zimbabwe Dollar and the Korean Won demonstrate the extreme downside volatility experienced by these countries during the emerging markets currency crisis.     

The implied volatility smirk

This paper provides an industry standard on how to quantify the shape of the implied volatility smirk in the equity index options market. Our local expansion method uses a second-order polynomial to describe the implied volatility–moneyness function and relates the coefficients of the polynomial to the properties of the implied risk-neutral distribution of the equity index return. We present a formal, two-way representation of the link between the level, slope and curvature of the implied volatility smirk and the risk-neutral standard deviation, skewness and excess kurtosis. We then propose a new semi-analytical method to calibrate option-pricing models based on the quantified implied volatility smirk, and investigate the applicability of two option-pricing models.     

Portfolio selection with higher moments

We propose a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the traditional Markowitz approach: the ability to handle higher moments and parameter uncertainty. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portfolio selection. Further, our comparison to other methods where parameter uncertainty is either ignored or accommodated in an ad hoc way, shows that our approach leads to higher expected utility than competing methods, such as the resampling methods that are common in the practice of finance.