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on Financial Markets |
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Issue of 2012‒01‒10
four papers chosen by |
| By: | Jaime Casassus; Freddy Higuera |
| Abstract: | This paper shows that oil price changes, measured as short-term futures returns, are a strong predictor of excess stock returns at short horizons. Ours is a leading variable for the business cycle and exhibits low persistence which avoids the ctitious long-horizon predictability associated to other predictors used in the literature. We compare our variable with the most popular predictors in a sample period that includes the recent nancial crisis. Our results suggest that oil price changes are the only variable with forecasting power for stock returns. This signi cant predictive ability is robust against the inclusion of other variables and out-of-sample tests. We also study the cross-section of expected stock returns in a conditional CAPM framework based on oil price shocks. Our model displays high statistical signi cance and a better t than all the conditional and unconditional models considered including the Fama French three-factor model. From a practical perspective, ours is a high-frequency, observable variable that has the advantage of being readily available to market-timing investors. |
| Keywords: | Return predictability, business cycle, crude oil, futures prices, asset pricing, conditional CAPM |
| JEL: | G17 E44 Q43 E32 G12 G14 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:ioe:doctra:406&r=fmk |
| By: | Gao-Feng Gu; Fei Ren; Wei-Xing Zhou |
| Abstract: | We have studied the empirical distribution of cancellation positions through rebuilding the limit-order book using the order flow data of 23 liquid stocks traded on the Shenzhen Stock Exchange in the year 2003. We find that the probability density function (PDF) of relative price levels where cancellations allocate obeys the log-normal distribution. We then analyze the PDF of normalized relative price levels by removing the factor of order numbers stored at the price level, and find that the PDF has a power-law behavior in the tails for both buy and sell orders. When we focus on the probability distribution of cancellation positions at a certain price level, we find that the PDF increases rapidly in the front of the queue, and then fluctuates around a constat value until the end of the queue. In addtion, the PDF of cancellation positions can be fitted by the exponent function for both buy and sell orders. |
| Date: | 2011–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1112.6085&r=fmk |
| By: | Lars Stentoft (HEC Montréal, CIRANO, CIRPEÉ, and CREATES) |
| Abstract: | The GARCH framework has been used for option pricing with quite some success. While the initial work assumed conditional Gaussian innovations, recent contributions relax this assumption and allow for more flexible parametric specifications of the underlying distribution. However, until now the empirical applications have been limited to index options or options on only a few stocks and this using only few potential distributions and variance specififications. In this paper we test the GARCH framework on 30 stocks in the Dow Jones Industrial Average using two classical volatility specififications and 7 different underlying distributions. Our results provide clear support for using an asymmetric volatility specifification together with non-Gaussian distribution, particularly of the Normal Inverse Gaussian type, and statistical tests show that this model is most frequently among the set of best performing models. |
| Keywords: | American options, GARCH models, Model Confidence Set, Simulation. |
| JEL: | C22 C53 G13 |
| Date: | 2011–12–21 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2011-52&r=fmk |
| By: | Leonidas Sandoval Junior; Adriana Bruscato; Maria Kelly Venezuela |
| Abstract: | Using Random Matrix Theory, we build a covariance matrix between stocks of the BM&F-Bovespa (Bolsa de Valores, Mercadorias e Futuros de S\~ao Paulo) which is cleaned of some of the noise due to the complex interactions between the many stocks and the finiteness of available data, and use a regression model in order to remove the market effect due to the common movement of all stocks. These two procedures are then used in order to build portfolios of stocks based on Markovitz's theory, trying to build better predictions of future risk based on past data. This is done for years of both low and high volatility of the Brazilian market, from 2004 to 2010. |
| Date: | 2012–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1201.0625&r=fmk |