Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index

In this paper, we aim to improve the predictability of aggregate stock market volatility with industry volatilities. The empirical results show that individual industry volatilities can provide useful predictive information, while the predictive contribution is limited. We further consider the spillover index between industry volatilities and find it displays strong predictive power for stock market volatility. Based on the portfolio exercise, we find that a mean-variance investor can achieve sizeable economic gains by using volatility forecasts of the spillover index. In addition, we conduct three extended analyses and further demonstrate the superior performance of the spillover index. Also, our results show robustness to a series of alternative settings. Finally, we investigate why the spillover index performs better and answer what information it contains. The results show that the spillover index can reflect and explain investor sentiments that are related to stock market volatility.     

Derivative activities and the risk of international banks: A market index and VaR approach

The results of a comparison of international banks using a three-factor multi-index model and a modified value-at-risk (VaR) analysis indicate that the use of options increases the interest rate beta for all banks, while both interest rate and currency swaps generally reduce risk. The results are the strongest and the most consistent for U.S. dealer banks, followed by European banks, and then Japanese banks. Furthermore, the evidence suggests that the VaR approach to risk management can effectively be used by both domestic as well as international banks, although the results appear to be somewhat sensitive to the regulatory environment in which the bank operates.     

Information content of sustainability index recomposition: A synthetic portfolio approach

A firm’s inclusion on the Dow Jones Sustainability Index (DJSI) signals its leadership in Environmental, Social, and Governance (ESG). We studied the effects of DJSI inclusion using data through 2021. For additions, we found significant positive pre-announcement effects, as well as persistence of positive abnormal returns. On deletions, we also saw positive abnormal returns, although significance reduced in later years. Our analysis demonstrated a stronger information role of DJSI inclusion for non-S&P 500 firms, which suggests that the DJSI is relatively more important for firms with lower investor recognition. We used a generalised synthetic control approach to estimate our results, which responds to the parallel trends critique of stock return event studies and provides robustness to industry shocks that are so important to ESG applications. Thus, this paper represents an important extension to our understanding of the value relevance of DJSI membership.     

On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach

The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.     

A new approach to measuring riskiness in the equity market: Implications for the risk premium

We introduce a new approach to measuring riskiness in the equity market. We propose option implied and physical measures of riskiness and investigate their performance in predicting future market returns. The predictive regressions indicate a positive and significant relation between time-varying riskiness and expected market returns. The significantly positive link between aggregate riskiness and market risk premium remains intact after controlling for the S&P 500 index option implied volatility (VIX), aggregate idiosyncratic volatility, and a large set of macroeconomic variables. We also provide alternative explanations for the positive relation by showing that aggregate riskiness is higher during economic downturns characterized by high aggregate risk aversion and high expected returns.     

Volatility spillovers and hedging effectiveness between the oil market and Eurozone sectors: A tale of two crises

This paper aims to analyze the mean and volatility spillovers between oil prices and the Eurozone supersector returns. It uses daily data of the Brent prices and 19 Eurozone supersector indices for the period from August 2004 to August 2015. This area experienced two important instabilities in that period, the global financial crisis (GFC) and the Euro debt crisis (EDC). Because financial turbulences are suspected to induce changes in the volatility dynamics, the full sample is divided into three sub-samples. Empirically, this study employs a bivariate VAR-BEKK-GARCH model that allows for transmission in volatility. The obtained volatilities and covariances are used to compute the optimal weights and hedge ratios for oil–stock portfolio holdings. The findings show that both mean and volatility spillovers between the oil market and the different Eurozone sectors are time-varying and heterogeneous. In the GFC sub-period, there is evidence of contagion effects because there is an intensification of volatility spillovers. The EDC does not seem to have induced any particular change in the spillover effects. The optimal weights, hedge ratios, and correlation analysis results allow an accurate understanding of the time series relationship between the two markets and are useful for financial market participants and policymakers.     

Point and density prediction of intra-day volume using Bayesian linear ACV models: evidence from the Polish stock market

Trading volume is one of the key measures of trading activity intensity and plays a crucial role in the financial market microstructure literature. In this paper, we examine the out-of-sample point and density forecasting performance of Bayesian Autoregressive Conditional Volume (ACV) models for intra-day volume data. Based on 5-min traded volume data for stocks quoted on the Warsaw Stock Exchange, a leading stock market in Central and Eastern Europe, we find that, in terms of point forecasts, the considered linear ACV models significantly outperform benchmarks such as the naïve and Rolling Means methods but not necessarily Autoregressive Moving Average (ARMA) models. Moreover, the point forecasts obtained within the exponential error ACV model are significantly superior to those calculated in other competing structures for which Burr or generalized gamma distributions are specified. The main finding from the analysis of density forecasts is that, in many cases, the linear ACV models with the Burr and generalized gamma distributions provide significantly better density forecasts than the linear ACV model with exponential innovations and the ARMA models in terms of the log-predictive score, calibration and sharpness.     

Bond yields and debt supply: new evidence through the lens of a preferred-habitat model

This paper examines the responsiveness of bond yields to changes in debt supply. The preferred-habitat theory predicts a positive relation between the term spread and relative supply of longer term debt, and that this relation is stronger when risk aversion is high. To capture this effect, a time-varying coefficient model is introduced and applied to German bond data. The results support the theoretical predictions and indicate substantial time variation: under high risk aversion, yield spreads react about three times more strongly than when risk aversion is low. The accumulated response of term spreads to a one standard deviation change in debt supply ranges between 4 and 46 basis points.     

The term structure of banking crisis risk in the United States: A market data based compound option approach

We use a compound option-based structural credit risk model to estimate banking crisis risk for the United States based on market data on bank stocks on a daily frequency. We contribute to the literature by providing separate information on short-term, long-term and total crisis risk instead of a single-maturity risk measure usually inferred by Merton-type models or barrier models. We estimate the model by applying the Duan (1994) maximum-likelihood approach. A strongly increasing total crisis risk estimated from early July 2007 onwards is driven mainly by short-term crisis risk. Banks that defaulted or were overtaken during the crisis have a considerably higher crisis risk (especially higher long-term risk) than banks that survived the crisis.     

A closer look at return predictability of the US stock market: evidence from new panel variance ratio tests

This paper examines the return predictability of the US stock market using portfolios sorted by size, book-to-market ratio and industry. We use novel panel variance ratio tests, based on the wild bootstrap proposed in this paper, which exhibit desirable size and power properties in small samples. We have found evidence that stock returns have been highly predictable from 1964 to 1996, except for a period leading to the 1987 crash and its aftermath. After 1997, stock returns have been unpredictable overall. At a disaggregated level, we find evidence that large-cap portfolios have been priced more efficiently than small- or medium-cap portfolios; and that the stock returns from high-tech industries are far less predictable than those from non-high-tech industries.     

Modeling the business risk of financially weakened firms: A new approach for corporate bond pricing

Most structural models of default risk assume that the firm's asset return is normally distributed, with a constant volatility. By contrast, this article details the properties that the process of assets should have in the case of financially weakened firms. It points out that jump-diffusion processes with time-varying volatility provide a refined and accurate perspective on the business risk dimension of default risk. Representative Arrow-Debreu state price densities (SPD) and term structures of credit spreads are then explored. The credit curves show that the business uncertainties play a major in the pricing of corporate liabilities.     

Assessing the impact of COVID-19 on major industries in Japan: A dynamic conditional correlation approach

This study assesses the impact of the novel coronavirus disease (COVID-19) cases on the Japanese stock market. As of October 30, 2020, the cumulative number of cases in Japan has reached over one hundred thousand. COVID-19 has significantly affected both the lifestyle and the economy in Japan. First, this study develops composite stock indices by industry sector and prefecture, taking into consideration the effects of the increase in infections on industries and firms in the core prefectures. Second, this study investigates the dynamic conditional correlations between the composite stock index returns and the increment in COVID-19 cases using dynamic conditional correlation multivariate GARCH models. Finally, it can contribute to financial research in terms of coexistence of regional business economies with COVID-19.