Chih-Wei Wang

Risk Factors in the Oil Industry: An Upstream and Downstream Analysis

In this paper we examine the drivers of stock market value in the upstream (producers) and downstream segments (petroleum refiners) of the oil industry.Using a sample of U.S. firms we find that stock returns of upstream and downstream firms follow stock market and oil price returns. Moreover, the upstream firm stock returns are sensitive to changes in the Canadian dollar, an important oil trade partner of the U.S., to natural gas returns and its volatility, but not to oil return volatility.Both the upstream and downstream segments present asymmetric changes regarding oil return changes. Stock returns of the oil industry respond asymmetrically to oil returns, i.e., positive oil returns had a greater impact than oil price drops in the period 1998-2004. Before 1997 we do not find any asymmetric effects, and after 2004, they are only statistically significant in the upstream segment. Overall, the evidence for asymmetric effects is more consistent across measures and time in the upstream than in the downstream segment.

Do Structural Constraints of the Industry Matter for Corporate Failure Prediction

This study uses a hazard model with data on 3392 corporate bankruptcies by U.S. public companies during 1983–2008 to determine the effect of industry-based struc tural constraints on bankruptcy predictions. The probability of bankruptcy is significantly higher for firms in highly concentrated industries and with relatively stronger customer dependency. Most bankruptcy predictions reflect the variation of a firm’s characteristics relative to its industry, but industry-specific characteristics have negligible impacts. The investigation also includes a comparison of the relative performance of accounting and market-based variables, in terms of both in-sample fit and out-of-sample forecasting accuracy. For yearly data, the best model includes both accounting and market-based variables. However, for monthly market data and quarterly accounting reports, the best model features only market data. The usefulness of accounting measures in bankruptcy prediction models thus may be contingent on sampling frequency.

Do Investors Price Industry Risk? Evidence from the Cross-Section of the Oil Industry

Recent research identifies several industry-related patterns that standard asset pricing models cannot explain effectively. This paper investigates what explains the cross-section of returns of firms in the oil industry and, in particular, how well an oil factor performs in comparison with the common systematic factors identified in the literature. We conduct a time series analysis and demonstrate that the oil factor has substantial explanatory power over traditional factors. A cross-sectional regression shows that the size, momentum and oil factors are associated with a positive risk premium and are able to explain the cross-sectional variation in stock returns in the oil industry. Our results suggest that investors demand compensation for the exposure to oil price changes, which has implications for the computation of the cost of equity.

Impact of Rollover Risk and Corporate Policy on Extreme Risk in the Taiwanese Manufacturing Industry

We explore the crucial research question of whether the rollover risk effect amplifies or reduces a firm’s extreme risk through an empirical investigation of the Taiwanese manufacturing industry. We also investigate the relationship between corporate policy and extreme risk. On the basis of extensive empirical evidence from 2003 to 2014, we determine significant positive impacts on extreme risk for firms with difficulty in rolling over their maturing debts. Firms with higher investment levels in a financial crisis should consequentially have more tail risk spillover from the financial sector.

Financial Crises, Financing Sources, and Default Risks

We examine the mechanism through which a financial crisis affects the default risk of real economy firms. Specifically, firms with strong dependence on bank financing suffer higher increases in default risk than firms with no such dependence. Conversely, firms relying solely on financing from public-debt markets do not experience significant increases in default risk. These findings suggest that the bank supply shock theory helps to understand the transmission channel of shocks from the financial sector to the real economy. Finally, bank-dependent firms cannot completely offset adverse impacts stemming from bank-lending supply shocks by substituting bank loans with publicly traded debts.

Measuring Systemic Risk: Common Factor Exposures and Tail Dependence Effects

We model systemic risk using a common factor that accounts for market-wide shocks and a tail dependence factor that accounts for linkages among extreme stock returns. Specifically, our theoretical model allows for firm-specific impacts of infrequent and extreme events. Using data on the four sectors of the US financial industry from 1996 to 2011, we uncover two key empirical findings. First, disregarding the effect of the tail dependence factor leads to a downward bias in the measurement of systemic risk, especially during weak economic times. Second, when these measures serve as leading indicators of the St. Louis Fed Financial Stress Index, measures that include a tail dependence factor offer better forecasting ability than measures based on a common factor only.

Financial Crises, Debt Financing, and Default Risk

We examine the mechanism through which a financial crisis affects the default risk of real economy firms. We find that firms with strong dependence on bank financing suffer from higher increases in default risk than firms with no such dependence. Conversely, firms that rely solely on financing from public debt markets do not experience significant increases in default risk. We also find that the increase in default probabilities, caused by a decrease in bank lending, is only significant for firms with low credit quality. These findings suggest that the bank supply shock theory helps explain the transmission channel of shocks from the financial sector to the real economy. Finally, firms dependent on bank financing cannot completely offset adverse impacts stemming from supply shocks in bank lending by substituting bank loans with publicly traded debt.

Do Corporate Cash Holdings Decrease the Impact of Tail Risk Spillovers from the Financial Sector? Evidence from Europe

We study the extent to which the impact of tail risk spillovers, originating in the financial sector and affecting real-economy firms, depends on the level of cash holdings and the financial conditions of the firm. Empirical evidence on 4,320 firms located in 16 European countries, from 2003 to 2011, suggests strong impacts for firms located in Euro-periphery countries. Firms located in Euro-core countries are less affected and firms located in the United Kingdom are barely affected. Cash holdings act as cushion of the impact of tail risk spillovers for financially constrained firms located in France, Netherlands, and United Kingdom.

Pricing SPX and DIX by HAR models

Previous studies have documented that, with use of high-frequency data, the Heteroskedasticity AR (HAR-RV) model performs better than other models in fitting financial return volatility measurement and has a more accurate forecasting ability. However, to our knowledge, no previous studies have investigated whether HAR-RV model can improve option pricing performance in financial markets. This study compares HAR-RV model and EGARCH model in terms of option pricing performance. As expected, the results of this study demonstrate that HAR-RV model is more accurate than EGARCH model in terms of S&P500 Index options (SPX) and Dow Jones Index options (DIX).