A model-independent measure of aggregate idiosyncratic risk

This paper introduces a model-independent measure of aggregate idiosyncratic risk, which does not require estimation of market betas or correlations and is based on the concept of gain from portfolio diversification. The statistical results and graphical analyses provide strong evidence that there are significant level and trend differences between the average idiosyncratic volatility measures of Campbell et al. [Campbell, J.Y., Lettau, M., Malkiel, B.G., and Xu, Y., 2001, Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk, Journal of Finance 56, 1–43.] and the new methodology. Although both approaches indicate a noticeable increase in the firm-level idiosyncratic risk, the volatility measure of CLMX is greater and has a stronger upward trend than the new idiosyncratic volatility measure. For both measures of idiosyncratic risk, the upward trend is found to be stronger for smaller, lower-priced, and younger firms. The analytical and empirical results show that the significant upward trend in the differences of the two idiosyncratic volatility measures is related to the increase in the cross-sectional dispersion of the volatility of individual stocks.     

Computing the market price of volatility risk in the energy commodity markets

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10 year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.     

Multivariate market association and its extremes

This paper analyzes the global market association with the cross-sectional dispersion measure proposed by [Solnik, B., Roulet, J., 2000. Dispersion as cross-sectional correlation. Financial Analysts Journal, 56 (1), 54–61]. It is demonstrated with a simple asset allocation problem that the dispersion measure increases investors’ welfare under certain conditions. From a policy makers’ perspective, the measure can be used to detect herding behavior and assess the evolution and characteristics of global market association. An empirical analysis of eleven developed stock market indices shows that the global market association has increased in recent years and that there are asymmetric effects of jointly positive and negative shocks. In addition, the market association is not always increasing in crisis periods. A comparison of the dispersion measure with estimates of a multivariate GARCH model indicates that correlation breakdowns exist but cannot be identified with the correlation coefficient. Quantile regression estimates further reveal that the volatility of market association decreased in recent years.     

International linkage of the Russian market and the Russian financial crisis: A multivariate GARCH analysis

This study considers the linkage of the Russian equity market to the world market, examining the international transmission of the Russia's 1998 financial crisis utilizing the GARCH–BEKK model proposed by Engle and Kroner [Engle, R.F., Kroner, K.F., 1995. Multivariate simultaneous generalized ARCH. Economet. Theor. 11, 122–150]. We find evidence of direct linkage between the Russian equity market with regards to returns and volatility, while the weakness of the linkage suggests that the Russian equity market was only partially integrated into the world market. At the time of the crisis, evidence of contagion is clear.     

Extreme downside risk and market turbulence

We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali et al. [Is there an intertemporal relation between downside risk and expected returns? Journal of Financial and Quantitative Analysis, 2009, 44, 883–909] with a Markov switching mechanism. We show that the risk-return relationship identified by Bali et al. (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship in all states of market volatility.     

Corporate risk management and dividend signaling theory

This article investigates the effect of corporate risk management on dividend policy. We extend the signaling framework of Bhattacharya [1979. Bell Journal of Economics 10, 259–270] by including the possibility of hedging the future cash flow. We find that the higher the hedging level, the lower the incremental dividend. This result is intuitive. It is in line with studies suggesting that cash flows’ predictability decreases the marginal gain from costly signaling through dividends and the assertion that corporate hedging decreases cash flow volatility. It is also in line with the purported positive relation between information asymmetry and dividend policy (e.g., Miller and Rock [1985. The Journal of Finance 40, 1031–1051]) and the assertion that risk management alleviates the information asymmetry problem (e.g., DaDalt et al. [2002. The Journal of Future Markets 22, 261–267]). Our theoretical model has testable implications.     

Time-varying risk aversion and asset prices

This paper uses a variant of the consumption-based representative agent model in Campbell and Cochrane [Campbell, J.Y., Cochrane, J.H., 1999. By force of habit: Consumption-based explanation of aggregate stock market behavior. Journal of Political Economy 107, 205–251] to study how investors’ time-varying risk aversion affects asset prices. First, we show that a countercyclical variation of risk aversion drives a procyclical conditional risk premium. Second, we show that with a small value for the volatility of the log surplus consumption ratio, a large value of risk aversion may not determine whether the equity premium and the risk-free rate puzzles can be resolved or not. Third, we show that countercyclical risk aversion may not help explain the predictability of long-horizon stock returns, the univariate mean-reversion of stock prices and the “leverage effect” in return volatility.     

Measuring the impact of option market activity on the stock market: Bivariate point process models of stock and option transactions

I apply the bivariate Autoregressive Conditional Duration model of Engle and Lunde [2003. Trade and quotes: a bivariate point process. Journal of Financial Econometrics 1, 159–188] to stock and option market transactions. The first model uses option trades and stock trades. Shocks to option trade/option trade durations have a significant impact on option trade/stock trade durations. Higher implied volatility, larger stock and option market order imbalances, larger stock trades, larger spreads, smaller depths in the stock market and faster trading in the stock and option markets are all associated with faster trading in both markets. In the second model, option trade/option trade timing leads option trade/stock quote timing and several information-related stock and option market covariates impact the expected inter-market event durations.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.     

Beta,size, book-to-market equity and returns: A study based on UK data

This paper examines the role of beta, size and book-to-market equity as competing risk measurements in explaining the cross-sectional returns of UK securities for the period July 1980 through June 2000. The methodology of [Fama, E., French, K., 1992. The cross-section of expected stock returns. Journal of Finance 47, 427–467] and [Pettengill, G., Sundaram, S., Mathur, I., 1995. The conditional relation between beta and returns. Journal of Financial and Quantitative Analysis 30, 101–116] is adopted. Results show that, when adopting the methodology of [Pettengill, G., Sundaram, S., Mathur, I., 1995. The conditional relation between beta and returns. Journal of Financial and Quantitative Analysis 30, 101–116], where data is segmented between up and down markets, a significant relationship is found between beta and returns even in the presence of size and book-to-market equity. Size is not found to be a significant risk variable, whereas book-to-market equity is found to be priced by the market and is thus a significant determinant of security returns. This is the case irrespective of the methodology adopted.     

On the importance of systematic risk factors in explaining the cross-section of corporate bond yield spreads

In this paper we examine the importance of systematic equity market factors in explaining the cross-sectional variation in yield spreads on corporate debt. Based on a sample of 1771 corporate bonds over the period from January 1985 to March 1998, we find that once the default-related variables are controlled for, bond betas or sensitivities to aggregate equity market risks have very limited explanatory power. This is in contrast to [Elton, E.J., Gruber, M.J., 2001. Explaining the rate spread on corporate bonds. Journal of Finance 56, 247–277] who find that market factors tied to expected returns are predominantly important, but who do not control for these variables (i.e. the relevant variables from structural models), possibly biasing their estimates. On the other hand, our finding that the systematic factors exhibit some limited explanatory power suggests that the standard contingent claims approach may not fully apply. This finding is consistent with previous research that bond betas are not completely irrelevant once market frictions are introduced. Overall, the evidence provides empirical support for the proposition that structural models capture important elements of corporate bond yield spread determination and equity market systematic factors are by no means predominant.     

Evaluating the impact of market reforms on Value-at-Risk forecasts of Chinese A and B shares

This paper analyses the time-varying conditional correlations between Chinese A and B share returns using the Dynamic Conditional Correlation (DCC) model of Engle [Engle, R.F. (2002), “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models”, Journal of Business and Economic Statistics, 20, 339–350.]. The results show that the conditional correlations increased substantially following the B share market reform, whereby Chinese investors were permitted to purchase B shares. However, this increase in correlations was found to have begun well before the B share market reform. This result has significant implication relating to the structure of the information flow between the markets for the two classes of shares. Value-at-Risk (VaR) threshold forecasts are used to analyse the importance of accommodating dynamic conditional correlations between Chinese A and B shares, and thus reflects the impact of the changes in information flow on the risk evaluation of a diversified portfolio. The competing VaR forecasts are analysed using the Unconditional Coverage, Serial Independence and Conditional Coverage tests of Christoffersen [Christoffersen (1998), “Evaluating Interval Forecasts”, International Economic Review, 39, 841–862], and the Time Until First Failure Test of Kupiec [Kupiec, P.H., (1995), “Techniques for Verifying the Accuracy of Risk Measurements Models”, Journal of Derivatives, 73–84]. The results offer mild support for the DCC model over its constant conditional correlation counterpart.     

Idiosyncratic risk and the cross-section of stock returns: Merton (1987) meets Miller (1977)

Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] predicts that idiosyncratic risk should be priced when investors hold sub-optimally diversified portfolios, and cross-sectional stock returns should be positively related to their idiosyncratic risk. However, the literature generally finds a negative relationship between returns and idiosyncratic risk, which is more consistent with Miller's [1977. Risk, uncertainty, and divergence of opinion. Journal of Finance 32, 1151–1168] analysis of asset pricing under short-sale constraints. We examine the cross-sectional effects of idiosyncratic risk while explicitly recognizing the confounding effects that dispersion of beliefs and short-sale constraints produce in the Merton framework. We find strong support for Merton's [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] model among stocks that have low levels of investor recognition and for which short selling is limited. For these stocks, the relation between idiosyncratic risk and expected returns is positive, as predicted by Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510].     

Comparing different explanations of the volatility trend

We analyze the puzzling behavior of the volatility of individual stock returns over the past few decades. The literature has provided many different explanations to the trend in volatility and this paper tests the viability of the different explanations. Virtually all current theoretical arguments that are provided for the trend in the average level of volatility over time lend themselves to explanations about the difference in volatility levels between firms in the cross-section. We therefore focus separately on the cross-sectional and time-series explanatory power of the different proxies. We fail to find a proxy that is able to explain both dimensions well. In particular, we find that Cao et al. [Cao, C., Simin, T.T., Zhao, J., 2008. Can growth options explain the trend in idiosyncratic risk? Review of Financial Studies 21, 2599-2633] market-to-book ratio tracks average volatility levels well, but has no cross-sectional explanatory power. On the other hand, the low-price proxy suggested by Brandt et al. [Brandt, M.W., Brav, A., Graham, J.R., Kumar, A., 2010. The idiosyncratic volatility puzzle: time trend or speculative episodes. Review of Financial Studies 23, 863-899] has much cross-sectional explanatory power, but has virtually no time-series explanatory power. We also find that the different proxies do not explain the trend in volatility in the period prior to 1995 (R-squared of virtually zero), but explain rather well the trend in volatility at the turn of the Millennium (1995-2005).     

Banking industry volatility and banking crises

While studies using balance sheet information of banks and macroeconomic indicators to forecast banking crises are prolific, empirical research using market information of banks is relatively sparse. We investigate whether banking industry volatility, constructed with the disaggregated approach from Campbell et al. [Campbell, J.Y., Lettau, M., Malkiel, B.G., Xu, Y., 2001. Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk? The Journal of Finance 56, 1–43] using exclusively publicly available market information of banks, is a good predictor of systemic banking crises in the analyses including data from 18 developed and 18 emerging markets. We find that banking industry volatility performs well in predicting systemic banking crises for developed markets but very poor for emerging markets, which suggest that the impact of market forces on the soundness of the banking system might be different for developed and emerging markets. We also find that those macroeconomic and banking risk management indicators have different impact on the probability of banking crises. Therefore, the traditional cross-country results of the studies on banking crises need to be interpreted cautiously.     

Influence of structural changes in transmission of information between stock markets: A European empirical study

The aim of this study is to analyze the influence of structural changes in volatility on the transmission of information. We present empirical evidence on European stock exchange markets based on information from the principal European stock indexes. In order to include structural changes in variance, we followed the [Sansó, A., Aragó, V., Carrión, J.Ll., 2004. Testing for changes in the unconditional variance of financial time series. Revista de Economía Financiera 4, 32–53] modification of the methodology proposed by [Inclan, C., Tiao, G.C., 1994. Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association 89, 913–923.], to take into account the problems of kurtosis and heteroskedasticity in the analyzed series. To study the existence of transmission of volatility we used an asymmetric bivariate GARCH model, specifically, the time-varying covariance asymmetric BEKK model [Engle, R.F., Kroner, K.F., 1995. Multivariate simultaneous generalized ARCH. Econometric Theory 11, 122–150]. The most outstanding result is the significance of the variables that represent these changes. When taken into consideration, they influence the scheme of transmission. Structural changes should therefore be incorporated into this type of study.     

Term premium dynamics in an emerging market: Risk,liquidity, and behavioral factors

This paper contributes to the fixed income research by identifying determinants of term premium in an emerging market’s treasury bond yields with particular attention on ambiguity. We use Nelson–Siegel yield curves generated from daily bond price quotes as input to construct a three-factor affine term structure model which decomposes observed yields into risk-neutral and term premium components. We also construct an ambiguity index using intraday FX return data following Brenner and Izhakian (2018). Our analyses suggest that a combination of factors representing market risk, credit risk, liquidity, ambiguity, and investor sentiments can explain majority of the variation in term premia. Explanatory power of credit risk measures are found to increase while those of volatility, ambiguity, and sentiment measures diminish with the maturity horizon. The results imply that ambiguity aversion of bond investors is a major determinant of the shape of the yield curve as it drives the premia for short end of the yield curve lower in line with the expectation of flight-to-safety behavior.     

The impact of macroeconomic and regulatory factors on bank efficiency: A non-parametric analysis of Hong Kong’s banking system

This paper assesses the relative technical efficiency of institutions operating in a market that has been significantly affected by environmental and market factors in recent years, the Hong Kong banking system. These environmental factors are specifically incorporated into the efficiency analysis using the innovative slacks-based, second stage Tobit regression approach advocated by Fried et al. [Fried, H.O., Schmidt, S.S., Yaisawarng, S., 1999. Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis 12, 249–267]. A further innovation is that we also employ Tone’s [Tone, K., 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research 130, 498–509] slacks-based model (SBM) to conduct the data envelopment analysis (DEA), in addition to the more traditional approach attributable to Banker, Charnes and Cooper (BCC) [Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science 30, 1078–1092].The results indicate: high levels of technical inefficiency for many institutions; considerable variations in efficiency levels and trends across size groups and banking sectors; and also differential impacts of environmental factors on different size groups and financial sectors. Surprisingly, the accession of Hong Kong to the People’s Republic of China, episodes of financial deregulation, and the 1997/1998 South East Asian crisis do not seem to have had a significant independent impact on relative efficiency. However, the results suggest that the impact of the last-mentioned may have come via the adverse developments in the macroeconomy and in the housing market.     

Default risk and equity returns: Australian evidence

We test whether default risk is related to equity returns using the Fama and MacBeth [Fama, E.F., MacBeth, J., 1973. Risk, return, and equilibrium: empirical tests. Journal of Political Economy 81, 607–636.] regression framework. The proxy we use for default risk is the default probability obtained from option-based models. Our findings show that default probability is negatively related to returns. While we find that size and book-to-market are related to default risk, the ability of these variables to explain cross-sectional variation in returns is not because they are proxying default risk. Further, our evidence suggests that the negative relationship between default probability and returns is not due to a leverage, volatility or momentum effect.     

Coherent risk measures under filtered historical simulation

Recent studies have strongly criticised conventional VaR models for not providing a coherent risk measure. Acerbi provides the intuition for an entire family of coherent measures of risk known as “spectral risk measures” [Spectral measures of risk: A coherent representation of subjective risk aversion. Journal of Banking and Finance 26 (7) (2002) 1505–1518]. In this study we illustrate how the Filtered Historical Simulation [Barone-Adesi, G., Bourgoin, F., Giannopoulos, K., 1998. Don’t look back. Risk 11, 100–104; Barone-Adesi, Giannopoulos, K., Vosper, L., 1999. VaR without correlations for non-linear portfolios. Journal of Futures Markets 19, 583–602], can provide an improved methodology for calculating the Expected Shortfall. Thereafter, we prove that these new risk measures are spectral and are coherent as well, following Acerbi. Furthermore, we provide the statistical error formula that allows to calculate the error for our model.