BANKRUPTCY DISCRIMINATION WITH REAL VARIABLES

This paper reconsiders the accepted usage of nondeflated financial ratios in statistical models to differentiate between failed and nonfailed firms. Non-deflated ratios are hypothesized to inadequately reflect inter-temporal macroeconomic fluctuations that affect the ability of firm's to survive. Using a sample of 124 oil and gas companies between the period 1982–1988, the going concern assumption is evaluated with statistical logit models using either nondeflated or deflated financial ratios. Deflated company ratios are created by transforming data with price indices or by creating market value ratios. Empirical results suggest that a superior bankruptcy early warning model is developed for the oil and gas industry by creating real financial and reserve ratios and by introducing external factors, such as oil prices, interest rates and accounting method, as independent predictors. Overall classification accuracy is approximately 95 percent.     

The macroeconomic determinants of technology stock price volatility

Stock prices reflect the value of anticipated future profits of companies. Since business cycle conditions impact the future profitability of firms, expectations about the business cycle will affect the current value of firms. This paper uses daily and monthly data from July 1986 to December 2000 to investigate the macroeconomic determinants of US technology stock price conditional volatility. Technology share prices are measured using the Pacific Stock Exchange Technology 100 Index. One of the novel features of this paper is to incorporate a link between technology stock price movements and oil price movements. The empirical results indicate that the conditional volatilities of oil prices, the term premium, and the consumer price index each have a significant impact on the conditional volatility of technology stock prices. Conditional volatilities calculated using daily stock return data display more persistence than conditional volatilities calculated using monthly data. These results further our understanding of the interaction between oil prices and technology share prices and should be of use to investors, hedgers, managers, and policymakers.     

Oil Futures Prices in a Production Economy with Investment Constraints

We document a new stylized fact, that the relationship between the volatility of oil futures prices and the slope of the forward curve is nonmonotone and has a V-shape. This pattern cannot be generated by standard models that emphasize storage. We develop an equilibrium model of oil production in which investment is irreversible and capacity constrained. Investment constraints affect firms' investment decisions and imply that the supply elasticity changes over time. Since demand shocks must be absorbed by changes in prices or changes in supply, time-varying supply elasticity results in time-varying volatility of futures prices. Estimating this model, we show it is quantitatively consistent with the V-shape relationship between the volatility of futures prices and the slope of the forward curve.     

Yesterday's Bad Times Are Today's Good Old Times: Retail Price Changes Are More Frequent Today Than in the 1890s

This paper documents differences in the nominal rigidity of retail prices across two 28-month periods: 1889–91 and 1997–99. The most striking finding is that prices changed much less frequently in 1889–91. In the late-1800s when price changes did occur they were smaller on average and more narrowly distributed with fewer small or large price changes. Further, price changes were more permanent 100-plus years ago. These differences are consistent with a high occurrence of temporary price shocks and a higher cost of changing prices in 1889–91 than in 1997–99.     

What Causes the Positive Price-Turnover Correlation in European Housing Markets?

This paper examines what determines the correlation between prices and turnover in European housing markets. Using a panel vector autoregressive model, we find that there is a particularly strong feedback mechanism between prices and turnover. Momentum effects are another important reason why prices and turnover are correlated. Common underlying factors, such as GDP and interest rates, also explain part of the price-turnover correlation. The results in this paper imply that, to understand price and turnover dynamics, it is important to model prices and turnover as two interdependent processes. There is a considerable bias in the coefficient estimates of standard house price models if this dependency is not explicitly taken into account.     

China's Imported Inflation and Global Commodity Prices

In this paper, we outline China's imported inflation via global commodity prices. We show that the prices of China's imported commodities are strongly related to global commodity prices. Meanwhile, the final goods prices from upstream industries are strongly influenced by global commodity prices. However, this effect is partially offset by the production process—that is, the final goods prices in downstream industries are generally less affected by global prices. This indicates that China's commodity market has a close link with global commodity markets. Therefore, high global commodity prices generally squeeze profits in China's downstream industries; upstream industries generally benefit from high global commodity prices.     

Taxes and the Pricing of Stock Index Futures

Stock index futures prices are generally below the level predicted by simple arbitrage models. This paper suggests that the discrepancy between the actual and predicted prices is caused by taxes. Capital gains and losses are not taxed until they are realized. As Constantinides demonstrates in a recent paper, this gives stockholders a valuable timing option. If the stock price drops, the investor can pass part of the loss on to the government by selling the stock. On the other hand, if the stock price rises, the investor can postpone the tax by not realizing the gain. Since this option is not available to stock index futures traders, the futures prices will be lower than standard no-tax models predict.     

Volatility spillovers between oil prices and the stock market under structural breaks

This paper employs univariate and bivariate GARCH models to examine the volatility of oil prices and US stock market prices incorporating structural breaks using daily data from July 1, 1996 to June 30, 2013. We endogenously detect structural breaks using an iterated algorithm and incorporate this information in GARCH models to correctly estimate the volatility dynamics. We find no volatility spillover between oil prices and US stock market when structural breaks in variance are ignored in the model. However, after accounting for structural breaks in the model, we find strong volatility spillover between the two markets. We compute optimal portfolio weights and dynamic risk minimizing hedge ratios to highlight the significance of our empirical results which underscores the serious consequences of ignoring these structural breaks. Our findings are consistent with the notion of cross-market hedging and sharing of common information by financial market participants in these markets.     

On the dynamic dependence and investment performance of crude oil and clean energy stocks

This paper examines the directional spillover between crude oil prices and stock prices of technology and clean energy companies. The study uses the daily data over the period from May 2005 to April 2015. The estimated results exhibit following empirical regularities. First, it appears that technology stocks play vital role in the return and volatility spillovers of renewable energy stocks and crude oil prices. Second, technology (PSE) and clean energy indices (ECO) are the dominant emitters of return and volatility spillovers to the crude oil (WTI) prices. Third, the time and event-dependent movements are well captured by the directional spillover approach. Fourth, the application of directional spillover method seems to be more advantageous than MGARCH models as it not only establishes the inter-variables return and volatility spillovers but also helps in identifying direction of spillover through calculation of pairwise net spillovers. Last, the dynamic hedging results suggest that clean energy index can provide a profitable hedging opportunity in combination with crude oil futures than technology index. Many new findings further discussed and analysed.     

去伪存真:油价趋势与股票市场——来自“一带一路”35国的经验证据

油价波动深刻影响全球经济,严重时会造成全球股市动荡,甚至引发系统性金融风险。然而油价中的信息噪音严重阻碍国际油价对股票市场的预测效果。本文提出的移动平均法可有效减弱信息噪音,研究表明,本文基于移动平均法构建的油价趋势因子对“一带一路”沿线国家股票市场具有良好的样本内和样本外可预测性。进一步研究发现,国际油价波动对产油国和非产油国股票市场的影响存在非对称性。本文为国际油价冲击股票市场提供了新的有力证据,同时本文研究成果提示了油价风险,对维持我国股票市场稳定,保持金融稳定具有一定意义。     

Nonlinear effects of climate policy uncertainty and financial speculation on the global prices of oil and gas

With the acceleration of global energy transition and financialization, intense climate policy uncertainty and financial speculation have significant impacts on the global energy market. This paper uses TVP-VAR-SV models to analyze the nonlinear effects of climate policy uncertainty (CPU), financial speculation, economic activity, and US dollar exchange rate on global prices of crude oil and natural gas respectively, and then compare the time-varying response of oil prices and gas prices to six representative CPU peaks. The results show that responses of energy prices to various shocks have significant nonlinear effects: the time-varying effect of CPU on energy prices from positive to negative over time is significant, and financial speculation has the opposite effects on oil and gas prices. The effect from economic activity is mainly positive, while the effects of US dollar exchange are negative and stable. These results provide important implications for policymakers and investors dealing with high levels of climate policy uncertainty, financial speculation, and global economic activity.     

Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network

This paper aims to develop an artificial neural networkbased forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007–2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural networkbased models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market.     

Do oil prices drive food prices? The tale of a structural break

It is frequently argued that biofuel (and ethanol) promotion policies in the United States have created a link between oil and corn prices that has accentuated the recent rally in the price of that crop and its substitutes (especially soybeans). Even though it is intuitively appealing, one problem with this hypothesis is that ethanol policies have been in place in the US for more than 35 years, whereas the run up in food prices dates back only to 2006. However, a significant change in US biofuel policy during that year provides an adequate framework to test for the existence of a structural break in the stochastic properties of the corn and soybean price processes. The results show that structural stability is rejected, and the transmission of oil price innovations to corn prices has become stronger after 2006 (no changes with respect to soybeans). There is also a significant transmission of corn price innovations to oil and soybean prices. Moreover, the data show evidence of a previously non-existent cointegration relationship between oil and corn prices.     

美元贬值和石油价格变动相关性的实证分析

进入新世纪以来,由于各种因素导致美元不断贬值,与此形成鲜明对比的是,石油价格一路飙升。那么,美元汇率和石油价格之间是否存在着某种因果关系呢?由于期货市场具有价格发现功能。本文以最具代表性的美国纽约商品交易所的原油期货价格为研究对象,分析美元贬值和石油价格之间的关系。本文首先定性分析美元贬值导致石油价格上涨的传导机制,然后利用模型对相关数据进行实证分析。研究结果表明,石油期货价格的上涨,除了有美元指数的影响之外,更重要的原因是前期石油期货价格上涨对本期石油期货价格上涨有正向的推动作用。     

Oil convenience yields estimated under demand/supply shock

This paper extends the call option model of Milonas and Thomadakis (1997) to estimate oil convenience yields with futures prices. We define the business cycle of a seasonal commodity with demand/supply shocks and find that the convenience yield for crude oil exhibits seasonal behavior. The convenience yield for West Texas Intermediate (WTI) crude oil is the highest in the summer, while that for Brent crude oil is the highest in the winter. This implies that WTI crude oil is more sensitive to high summer demand and that Brent crude oil is more sensitive to shortages in winter supply. Convenience yields are negatively related to the inventory level of the underlying crude oil and positively related to interest rates due to the business cycle. We also show that convenience yields may explain price spread between WTI crude oil and Brent crude oil. Our computed convenience yields are consistent with Fama and French (1988) in that oil prices are more volatile than futures prices at low inventory level, verifying the Samuelson (1965) hypothesis that future prices are less variables than spot prices at lower inventory levels.

Dispersion of Beliefs, Asset Prices, and Noisy Aggregation of Information

This paper discusses the determinants of the dispersion of beliefs of informed investors and the effects of this dispersion on the equilibrium level of asset prices in a noisy rational expectations model. It is shown that lower asset prices are usually associated with an increase in the dispersion of beliefs. However, when the noise level of private signals is high, or when asset prices are abnormally high, increased dispersion of beliefs will be associated with higher asset prices. This paper shows that the dispersion of beliefs is an increasing function of the standard deviations of an asset's payoff and the noise in the asset's supply, while it may be a decreasing or an increasing function of the standard deviation of errors in private signals.     

Theory of Storage and the Pricing of Commodity Claims

We extend the literature on commodity pricing by incorporating a link between the spread of forward prices and spot price volatility suggested by the theory of storage. Our model has closed form solutions that are generalizations of the two-factor model of Gibson–Schwartz (1990). We estimate the model on daily copper spot and forward prices using the Kalman filter methodology. Our findings confirm the link between the forward spread and volatility, but also show that the Gibson–Schwartz (1990) model prices forward contracts almost as well. In the pricing of option contracts, however, there are significant differences between the models.     

Oil prices,earnings, and stock returns

Review of Accounting Studies - Research has failed to document a consistent association between oil prices and stock prices. We propose and examine whether that failure is due to the need to link...     

Distressed debt prices and recovery rate estimation

This paper has two purposes. First, it uses distressed debt prices to estimate recovery rates at default. In this regard, estimates are obtained for three recovery rate models: recovery of face value, recovery of Treasury, and recovery of market value. We show that identifying the “economic” default date, as distinct from the recorded default date, is crucial to obtaining unbiased estimates. The economic default date is defined to be the first date when the market prices the firm’s debt as if it has defaulted. An implication is that the standard industry practice of using 30-day post default prices to compute recovery rate yields biased estimates. Second, we construct and estimate a distressed debt pricing model. We use this model to implicitly estimate the parameters of the embedded recovery rate process and to price distressed debt. Our distressed debt pricing model fits market prices well, with an average pricing error of less than one basis point.      

Prices are sticky after all

Economists have interpreted the evidence that prices change every four months as implying that sticky prices cannot be important for monetary transmission. Theory implies that this interpretation is correct if most price changes are regular, but not if a large fraction are temporary, as in the data. Since regular prices are much stickier than temporary ones, our models predict that the stickiness of the aggregate price level matches that in a standard Calvo model or a standard menu cost model in which micro-level prices change about once a year. In this sense, prices are sticky after all.