Disorder in the Risk Order

Abstract

This paper studies the difference in global equity portfolios allocated by the criterion of minimizing the risk by using two different risk measures, the standard deviation and the conditional value at risk. An empirical analysis is performed on a comprehensive stock exchange database. The main hypothesis of the present work is that the choice of risk measure has crucial importance in portfolio optimisation, especially in those situations when the stock markets are extremely volatile and the return distributions are non-normal. The rationale behind establishing minimum-risk portfolios is to keep track of the highest possible risk reduction benefits from international diversification.     

Horse race of weekly idiosyncratic momentum strategies with respect to various risk metrics: Evidence from the Chinese stock market

This paper focuses on the horse race of weekly idiosyncratic momentum (IMOM) with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum based on raw returns and idiosyncratic returns, respectively. After that the univariate portfolio analysis is conducted to investigate the return predictability with respect to various idiosyncratic risk metrics. Further, we perform a comparative study on the performance of the IMOM portfolios with respect to various risk metrics. At last, we explore the possible explanations to IMOM as well as risk-based IMOM portfolios. We find that 1) there are prevailing contrarian effect and IMOM effect for the whole sample; 2) the negative relations exist between most of the idiosyncratic risk metrics and the cross-sectional stock returns, and better performance is linked to idiosyncratic volatility (IVol) and maximum drawdowns (IMDs); 3) additionally, the IVol-based and IMD-based IMOM portfolios exhibit better explanatory power to the IMOM portfolios with respect to other risk metrics; 4) finally, higher profitability of IMOM as well as IVol-based and IMD-based IMOM portfolios is found to be related to upside market states, high levels of liquidity and high levels of investor sentiment.     

Value stocks and market efficiency

We form portfolios based on firm book-to-market equity ratios and apply stochastic dominance tests. Value (high book-to-market) portfolios dominate low book-to-market portfolios. Thus, value stocks are not rationally priced by the market and the book-to-market ratio is not an efficiently priced proxy for equity risk. We also find that the superior performance of value stocks is not due to the January effect.     

Bank herding and systemic risk

Bank herding behavior is often hypothesized to increase systemic risk, but the actual effect is unclear ex-ante from the theory and unknown ex-post from the data. We expand the literature on this topic in several dimensions – posing alternative hypotheses regarding the effects of herding in asset, liability, and off-balance sheet portfolios; developing a novel set of bank-specific, time-varying measures of herding in these portfolios; and empirically testing the relations between bank herding for all three portfolios and bank systemic risk contributions. We find nuanced empirical results that differ by portfolio, bank size class, and periods before versus after TARP.     

Rational plunging and the option value of sequential investment: The case of petroleum exploration

The potential to invest sequentially in related assets creates a tradeoff between diversification and concentration. Loading a portfolio with correlated assets has the potential to inflate variance, but also creates information spillovers and real options that may augment total return and mitigate variance. We examine this tradeoff in the context of petroleum exploration. Using a simple model of geological dependence, we show that the value of learning options creates incentives for investors to plunge into dependence; i.e., to assemble portfolios of highly correlated exploration prospects. Risk-neutral and risk-averse investors are distinguished not by the plunging phenomenon, but by the threshold level of dependence that triggers such behavior. Aversion to risk does not imply aversion to dependence. Indeed the potential to plunge should be larger for risk-averse investors than for risk-neutral investors. We test the empirical validity of our theory by examining bidding activity in petroleum lease sales. We find significant plunging behavior across a broad sample of oil companies. We also find that privately-held firms pursue even more concentrated (less diversified) prospect portfolios than publicly-held firms—which we attribute to risk aversion rather than size.     

Individual risk attitudes and the composition of financial portfolios: Evidence from German household portfolios

This paper explores the relationship between the self-declared risk aversion of private investors and their propensity to hold incomplete portfolios of financial assets. The analysis is based on household survey data from the German Socioeconomic Panel (SOEP) that provides a reliable measure of individual attitudes toward financial risk. Our findings suggest that more risk averse households tend to hold incomplete portfolios consisting mainly of a few risk-free assets. We also find that the propensity to acquire additional assets is highly dependent on whether liquidity and safety needs are met.     

Measuring Credit Risk of a Bank's Corporate Loan Portfolio Using Advanced Internal Ratings Base Approach

Abstract

The use of technical and advanced approaches in the measurement of credit risk of banks' portfolios has nowadays become a very hot issue. The most recent technical report issued by the Basel Committee in May 2003 has concentrated heavily on the measurement of credit risk using either foundation or advanced Internal Ratings Base (IRB) approaches. This empirical research study attempts to measure credit risk of a bank's corporate loan portfolio, including firms from 10 different Turkish sectors. The monthly observations of the total amount of corporate loans and the total amount of corporate loans at default across various sectors are downloaded from the web page of Central Bank of Turkey (CBT) in a period of 1999-2002. This period covers 47 monthly observations since CBT has captured sectoral corporate loans beginning of 1999. Therefore, the observed sectoral default rates are needed to be simulated to obtain a nicely shaped distribution. Monte Carlo simulation is applied for 1,000 times. Based on the simulated default rates, the expected sectoral default rates are computed. Next, a credit quality rating scale is fitted into sectoral default rates distributions. Finally, the sectoral weights in the whole loan portfolio are multiplied by the expected sectoral default rates matrix, considering cross-sectoral correlations to get the total amount of the bank's credit risk and capital requirement. It is assumed that sectoral monthly default rates are a good representative of the default risk of a sample bank's corporate loan portfolio since no publicly available data on any particular bank's corporate loan portfolio composition exists. Nevertheless, this research may be a good application for measuring the credit risk of banks' corporate loan portfolios using advanced IRB approach.     

Diversified behavioral portfolio as an alternative to Modern Portfolio Theory

The traditional mean–variance approach has been complemented by alternative theories that use risk measures different from standard deviation of returns or involve additional distributional features of returns like skewness and kurtosis. We propose a portfolio choice model that combines different distributional characteristics of the returns in the decision-making making process, considering preferences of investors which are modeled as non-statistical uncertainties of investors using fuzzy theory. We use 20 stocks of the S&P500 from January 2013 to December 2017. We assess the obtained portfolios’ performance, and the diversified behavioral portfolios outperform than the mean–variance portfolio. This methodological proposal can be seen as a strong managerial tool to make investment portfolio decisions.     

The Economic Determinants of the Home Country Bias in Investors' Portfolios: A Survey

In this article, we survey the international portfolio choice literature to investigate why investors choose to bias their portfolios towards domestic equity, even though there are significant gains to diversifying internationally. We focus on three potential explanations. First, we consider if the high proportion of domestic assets in investors' portfolios can be explained by their desire to hedge home inflation. While the models of Krugman (1981), Sercu (1980), Adler and Dumas (1983), and Stulz (1981a, 1983) suggest that this is the case, the model in Uppal (1993) shows that this is true only when relative risk aversion is less than one. Second, we consider the prevailing institutional barriers to foreign investment to see if they are sufficiently large to explain the bias observed in investors' portfolios. Halliday (1989) reports that there are few constraints on investing in foreign stock markets. This is especially true when investing in the markets of developed countries. Even when restrictions exist, they are usually not binding. Third, we consider the models of Black (1974) and Stulz (1981b) to see if transactions costs for investing abroad and taxes on income from foreign assets can explain the home equity bias. Cooper and Kaplanis (1986, 1991) and French and Porterba (1991) estimate that the taxes required to explain the observed bias are much larger than those investors actually face. We conclude that it is unlikely that these three factors are significant enough to explain the degree of the bias in portfolios that is observed empirically.     

Downside risk management and VaR-based optimal portfolios for precious metals,oil and stocks

Value-at-Risk (VaR) is used to analyze the market downside risk associated with investments in six key individual assets including four precious metals, oil and the S&P 500 index, and three diversified portfolios. Using combinations of these assets, three optimal portfolios and their efficient frontiers within a VaR framework are constructed and the returns and downside risks for these portfolios are also analyzed. One-day-ahead VaR forecasts are computed with nine risk models including calibrated RiskMetrics, asymmetric GARCH type models, the filtered Historical Simulation approach, methodologies from statistics of extremes and a risk management strategy involving combinations of models. These risk models are evaluated and compared based on the unconditional coverage, independence and conditional coverage criteria. The economic importance of the results is also highlighted by assessing the daily capital charges under the Basel Accord rule. The best approaches for estimating the VaR for the individual assets under study and for the three VaR-based optimal portfolios and efficient frontiers are discussed. The VaR-based performance measure ranks the most diversified optimal portfolio (Portfolio #2) as the most efficient and the pure precious metals (Portfolio #1) as the least efficient.     

Modeling common volatility characteristics and dynamic risk premia in European equity markets

The paper applies a Factor-GARCH model to evaluate the impact of the market portfolio, as a single common dynamic risk factor, on conditional volatility and risk premia for the returns on size-based equity portfolios of three major European markets; France, Germany and the United Kingdom. The results show that for the size-based portfolios the factor loading for the dynamic market factor is significant and positive but the association between the risk premia and the conditional market volatility is weak. However, the dynamic market factor is shown to explain common characteristics in the conditional variance such as asymmetry and persistence. This finding is consistent across markets and portfolio sizes.     

Transition matrix models of consumer credit ratings

Although the corporate credit risk literature includes many studies modelling the change in the credit risk of corporate bonds over time, there has been far less analysis of the credit risk for portfolios of consumer loans. However, behavioural scores, which are calculated on a monthly basis by most consumer lenders, are the analogues of ratings in corporate credit risk. Motivated by studies of corporate credit risk, we develop a Markov chain model based on behavioural scores for establishing the credit risk of portfolios of consumer loans. Although such models have been used by lenders to develop models for the Basel Accord, nothing has been published in the literature on them. The model which we suggest differs in many respects from the corporate credit ones based on Markov chains — such as the need for a second order Markov chain, the inclusion of economic variables and the age of the loan. The model is applied using data on a credit card portfolio from a major UK bank.     

Portfolio optimization based on GARCH-EVT-Copula forecasting models

This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-$t$ and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-$t$ copulas, are best at reducing the portfolio risk.     

Portfolio selection with a systematic skewness constraint

This paper investigates portfolio selection within a mean-variance-systematic skewness framework. We derive the composition of efficient portfolios in our model, and analyze the properties of these efficient portfolios. We show that the required systematic skewness is achieved at the expense of traditional mean-variance efficiency, and that a more stringent systematic skewness constraint induces a greater loss in mean-variance efficiency. Our numerical analysis demonstrates that the presence of the systematic skewness constraint helps improve the skewness of efficient portfolios in our model over the skewness of traditional efficient portfolios.     

Model risk and option hedging

This paper presents a theoretical approach to option hedging and valuation when traders are facing model risk. Model risk is restrictively defined as the financial risk resulting from the choice of an approximating model to proxy for the true but ex-ante unknown state space of the underlying security process. A generalized model is defined for estimating the appropriate volatility markup, which is dependent on the noisiness of the volatility estimate over time. Delta neutral hedge portfolios are created using simulated S&P 500 option prices to demonstrate that using a volatility markup in the traditional binomial model reduces model risk.     

A coupled Markov chain approach to credit risk modeling

We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heuristic global optimization techniques.We benchmark the model against a GLMM model in the context of bond portfolio risk management. The proposed model yields stronger dependencies and higher risks than the GLMM model. As a result, the risk optimal portfolios are more conservative than the decisions resulting from the benchmark model.     

Measuring Spot Variance Spillovers when (Co)variances are Time‐varying – The Case of Multivariate GARCH Models

We propose global and disaggregated spillover indices that allow us to assess variance and covariance spillovers, locally in time and conditionally on time‐t information. Key to our approach is the vector moving average representation of the half‐vectorized ‘squared’ multivariate GARCH process of the popular BEKK model. In an empirical application to a four‐dimensional system of broad asset classes (equity, fixed income, foreign exchange and commodities), we illustrate the new spillover indices at various levels of (dis)aggregation. Moreover, we demonstrate that they are informative of the value‐at‐risk violations of portfolios composed of the considered asset classes.     

Does the bank risk concentration freeze the interbank system?

Probably, one test of the stability of the banking system is to evaluate how risky assets are distributed across banks’ portfolios and the implications for the contagion via interbank relations. This paper explores theoretically a bank sector with risks concentration and the functioning of interbank markets. It employs a simple model where banks are exposed to both credit and liquidity risk that suddenly correlate over the business cycle. We show that risk concentration makes interbank market breakdowns more likely and welfare monotonically decreases in risk concentration.