A multivariate jump-driven financial asset model

We discuss a Lévy multivariate model for financial assets which incorporates jumps, skewness, kurtosis and stochastic volatility. We use it to describe the behaviour of a series of stocks or indexes and to study a multi-firm, value-based default model. Starting from an independent Brownian world, we introduce jumps and other deviations from normality, including non-Gaussian dependence. We use a stochastic time-change technique and provide the details for a Gamma change. The main feature of the model is the fact that—opposite to other, non-jointly Gaussian settings—its risk-neutral dependence can be calibrated from univariate derivative prices, providing a surprisingly good fit.     

A multivariate analysis of REIT performance by financial and real asset portfolio characteristics

This paper examines the risk-adjusted performance of real estate investment trusts (REITs) from 1986 through 1990 in relation to financial and property characteristics of their portfolios. The Sharpe measure of risk-adjusted rate of return was regressed against financial ratios and property investment ratios for a sample of equity and mortgage REITs. The results show that, in general, financial ratios (gross cash flow, leverage, asset size), regional location of properties, and types of real estate investments determine the risk-adjusted performance. More specifically, location of properties in the western United States, ownership of health care properties, and investment in securitized mortgages positively affect the risk-adjusted return. The individual financial variables were not found to be statistically significant in influencing REIT returns.     

A multivariate nonparametric test for return and volatility timing

This paper develops a novel approach to simultaneously test for market timing in stock index returns and volatility. The tests are based on the estimation of a system of regression equations with indicator variables and provide detailed information about the statistical significance of alternative market timing components.     

对实体经济与虚拟资产回报率关系的研究综述

实体经济和虚拟资产之间回报率关系具有深刻的经济学含义和政策意义。对于实体经济和虚拟资产回报率之间的显著偏离,研究认为,虚拟资产的膨胀根植于实体经济的周期性萧条。然而,对虚拟资产价值的信心仍是建立在其随时可以转换为物质财富基础之上的。货币政策应该充分关注相关的资产价格,应努力研究金融发展和经济回报率偏离的关键因素。     

A genre-theoretic approach to financial reporting research

A genre is a category of texts marked out by the conventions employed in their production. A genre-theoretic approach draws out the complex, subtle and elusive nature of financial reporting as communication. It provides scope for examining the features of the reporting process that contribute to its complexities and subtleties in a systematic, comprehensive and integrated way, embracing both technical and social dimensions. This paper discusses aspects of genre theory, as employed in discourse analysis, and their application to financial reporting. Relevant features of the approach include financial statement composition as a challenging process; knowing users; an engaged discourse community; situated communication; intertextuality; and structural dynamism. A genre-based approach has a number of implications for financial reporting research, at both methodological and substantive levels, which are explored in the paper, and may ultimately offer the potential for integrating market-based and interdisciplinary work together with the best of the classical tradition.     

A simple nonparametric approach to low-dimension,shortfall-based portfolio selection

This paper develops a simple, low-dimension portfolio selection rule based on minimizing the probability of realizing a return below some pre-determined benchmark or target rate. Unlike most shortfall-based methods, which employ approximations to the shortfall probability, this method operates directly on the complementary Heaviside function representation of the in-sample shortfall probability. Thus, no behavioral assumptions, other than the notion of shortfall minimization, enter the portfolio selection process.     

Conditional beta pricing models: A nonparametric approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.     

Testing conditional factor models: A nonparametric approach

Some recent studies of conditional factor models do not specify conditioning information but use data from small windows to estimate the time series of conditional alphas and betas. In this paper, we propose a nonparametric method using an optimal window to estimate time-varying coefficients. In addition, we offer two empirical tests of a conditional factor model. Using our new method, we examine the performance of the conditional CAPM and the conditional Fama-French three-factor model in explaining the return variations of portfolios sorted by size, book-to-market ratios, and past returns, for which recent literature has generated controversial results. We find that, although in general the conditional FF model outperforms the conditional CAPM, both models fail to explain well-known asset-pricing anomalies. Moreover, for both models, the failure is more pronounced for the equally-weighted portfolios than for the value-weighted ones.     

A new approach to assessing model risk in high dimensions

A central problem for regulators and risk managers concerns the risk assessment of an aggregate portfolio defined as the sum of d individual dependent risks $X_i$. This problem is mainly a numerical issue once the joint distribution of $\left(X_1\text{,}{X}_2\text{,}\dots \text{,}{X}_d\right)$ is fully specified. Unfortunately, while the marginal distributions of the risks $X_i$ are often known, their interaction (dependence) is usually either unknown or only partially known, implying that any risk assessment of the portfolio is subject to model uncertainty.Previous academic research has focused on the maximum and minimum possible values of a given risk measure of the portfolio when only the marginal distributions are known. This approach leads to wide bounds, as all information on the dependence is ignored. In this paper, we integrate, in a natural way, available information on the multivariate dependence. We make use of the Rearrangement Algorithm (RA) of Embrechts et al. (2013) to provide bounds for the risk measure at hand. We observe that incorporating the information of a well-fitted multivariate model may, or may not, lead to much tighter bounds, a feature that also depends on the risk measure used. In particular, the risk of underestimating the Value-at-Risk at a very high confidence level (as used in Basel II) is typically significant, even if one knows the multivariate distribution almost completely.Our results make it possible to determine which risk measures can benefit from adding dependence information (i.e., leading to narrower bounds when used to assess portfolio risk) and, hence, to identify those situations for which it would be meaningful to develop accurate multivariate models.     

Liquidity and asset prices: A new monetarist approach

When lenders cannot force borrowers to repay debts, assets are often pledged to secure loans. In this paper borrowers lose collateral once they renege on debts, and exclusion of defaulters occurs probabilistically, with a higher probability implying better enforcement. Increased efficiency in enforcement reduces asset prices, while raising loan-to-value ratios. If the rise in loan-to-value ratios is the dominant effect, aggregate liquidity and output increase with the advance in enforcement. Inflation raises the repayment cost by increasing the loan rate, while raising the default cost through exclusion. Consequently, inflation raises loan-to-value ratios and output only when enforcement is sufficiently efficient.     

Timing and diversification: A state-dependent asset allocation approach

Abstract

The influence of changing economic environment leads the distribution of stock market returns to be time-varying. A conditionally optimal investment hence requires a dynamic adjustment of asset allocation. In this context, this paper examines the improvement in portfolio performance by simulating portfolio strategies that are conditioned on the Markov regime switching behaviour of stock market returns. Including a memory effect eliminates the empirical shortcoming of discrete state models, namely that they produce a standard and an extreme state in stock returns. So far, this has prevented the regimes from being used as a valuable conditioning variable. Based on a discrete state indicator variable, is presented evidence of considerable performance improvement relative to the static model due to optimal shifting between aggressive and well diversified portfolio structures.     

Evaluating international consumption risk sharing gains: An asset return view

International consumption risk sharing studies often generate counterfactual implications for asset return behavior with potentially misleading results. We address this contradiction using data moments of consumption and asset returns to fit a canonical international consumption risk sharing framework. Introducing persistent consumption risk, we find that its correlation across countries is more important for risk sharing than that of transitory risk. To identify these risk components, we jointly exploit the comovement of equity returns and consumption. This identification implies high correlations in persistent consumption risk, suggesting a strong degree of existing risk sharing despite low consumption correlations in the data.     

Measuring financial contagion: A Copula approach

This paper models dependence with switching-parameter copulas to study financial contagion. Using daily returns from five East Asian stock indices during the Asian crisis, and from four Latin American stock indices during the Mexican crisis, it finds evidence of changing dependence during periods of turmoil. Increased tail dependence and asymmetry characterize the Asian countries, while symmetry and tail independence describe the Latin American case. Structural breaks in tail dependence are a dimension of the contagion phenomenon. Therefore, the rejection of the correlation breakdown hypothesis should not be considered, without further investigation, as evidence of a stable dependence structure.     

A framework for assessing the systemic risk of major financial institutions

In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks and forecasted asset return correlations. Importantly, using realized correlations estimated from high-frequency equity return data can significantly improve the accuracy of forecasted correlations. Our stress testing methodology, using an integrated micro–macro model, takes into account dynamic linkages between the health of major US banks and macro-financial conditions. Our results suggest that the theoretical insurance premium that would be charged to protect against losses that equal or exceed 15% of total liabilities of 12 major US financial firms stood at $110 billion in March 2008 and had a projected upper bound of $250 billion in July 2008.     

A fully parametric approach to return modelling and risk management of hedge funds

This paper examines the empirical properties of hedge fund returns and proposes a fully parametric model capable of adequately describing both univariate and multivariate return properties. The suggested model is based on the multivariate extension of the Normal Inverse Gaussian (NIG) distribution and will be shown to be capable of capturing the characteristic distributional features of hedge fund returns. Drawing on recent research in the area of Generalized Hyperbolic distributions and their calibration, we will elaborate on the application of the NIG-model for risk management purposes, and highlight the differences between the NIG and the Gaussian model.      

An intersectional approach to evaluating consumer financial literacy

This study applies a novel approach to explore consumer financial literacy—the necessary skills and knowledge to make personal financial decisions—across different demographic groups. Rather than exploring demographic variables independently of each other, an intersectional approach is employed to identify the most critical and vulnerable consumer groups in light of financial literacy. A survey of 1047 respondents from a panel of consumers residing in the United States demonstrates that identifying the most critical and vulnerable consumer groups is achieved using an intersectional approach. For example, although we find Generation Y exhibits lower financial literacy compared with previous generations, by examining the demographic variables simultaneously, we find that Generation Y females who are members of ethnical minorities are at the greatest risk of being financially vulnerable. Implications for research and financial literacy programs are discussed.     

The return to retail and the performance of US banks

The US banking industry is experiencing a renewed focus on retail banking, a trend often attributed to the stability and profitability of retail activities. This paper examines the impact of banks’ retail intensity on performance from 1997 to 2004 by developing three complementary definitions of retail intensity (retail loan share, retail deposit share, and branches per dollar of assets) and comparing these measures with both equity market and accounting measures of performance. We find that an increased focus on retail banking across US banks is linked with significantly lower equity market and accounting returns for all banks, but lower volatility for only the largest banking companies. We conclude that retail banking may be a relatively stable activity, but it is also a low return one.