Financial Econometics Series
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No. 2015_15   (Download full text)
Kumari Ranjeeni and Susan S Sharma
The impact of the Lehman Brothers’ Bankruptcy on the Performance of Chinese Sectors
This paper investigates the impact of the news announcement of the Lehman Brothers’ (LBs’) bankruptcy on the performance of Shanghai Stock Exchange (SSE) sectors. Unlike the assumption in this literature that firms are homogenous, we address the unknown issue: does LBs’ bankruptcy have a heterogenous effect on stock returns of sectors listed on SSE? We employ an event study approach and use daily data for a total of 845 firms grouped into nine sectors, we find fresh results, previously undocumented in this literature. First, our results show that unlike the United States (see Ranjeeni 2014), Chinese Energy and Financial sectors were insignificantly affected from LBs’ bankruptcy. This implies that these sectors can provide cross-country diversification opportunities for US investors during volatile periods. Second, we find statistically insignificant effect of LBs’ bankruptcy on the performance of the financial sector while most of the other sectors suffered significantly. This implies that the Chinese market level analysis conducted by Bianconi et al. (2013) is influenced by the performance of the financial sector. Finally, our results highlight on the heterogeneous effect of LBs’ bankruptcy on different Chinese sectors and at different time intervals surrounding the event.
JEL-Codes: G01, G11, G14, G33
Keywords: Lehman Brothers’ bankruptcy, Global Financial Crisis, abnormal returns, Chinese sectors, event study.
No. 2015_14   (Download full text)
Dinh H B Phan, Susan S Sharma and Paresh K Narayan
Intraday Volatility Interaction between the Crude Oil and Equity Markets
This paper investigates the price volatility interaction between the crude oil and equity markets in the US using five-minute data over the period 2009 to 2012. Our main findings can be summarised as follows. First, we find strong evidence to demonstrate that the integration of the bid-ask spread and trading volume factors leads to a better performance in predicting price volatility. Second, trading information, such as bid-ask spread, trading volume, and the price volatility from cross-markets, improves the price volatility predictability for both in-sample and out-of-sample analyses. Third, the trading strategy based on the predictive regression model that includes trading information from both markets provides significant utility gains to mean-variance investors.
Keywords: volatility; trading volume; bid-ask spread; cross-market; predictability; forecasting.
No. 2015_13   (Download full text)
Dinh H B Phan, Susan S Sharma and Paresh K Narayan
Stock Return Forecasting: Some New Evidence
This paper makes three contributions to the literature on forecasting stock returns. First, unlike the extant literature on oil price and stock returns, we focus on out-of-sample forecasting of returns. We show that the ability of the oil price to forecast stock returns depends not only on the data frequency used but also on the estimator. Second, out-of-sample forecasting of returns is sector-dependent, suggesting that oil price is relatively more important for some sectors than others. Third, we examine the determinants of out-of-sample predictability for each sector using industry characteristics and find strong evidence that return predictability has links to certain industry characteristics, such as book-to-market ratio, dividend yield, size, price earnings ratio, and trading volume.
Keywords: Stock returns; Oil price; Predictability; Forecasting; Out-of-sample.
No. 2015_12   (Download full text)
Dinh H B Phan, Susan S Sharma and Paresh K Narayan
Oil Price and Stock Returns of Consumers and Producers of Crude Oil
In this paper we investigate how differently stock returns of oil producers and oil consumers are affected from oil price changes. We find that stock returns of oil producers are affected positively by oil price changes regardless of whether oil price is increasing or decreasing. For oil consumers, oil price changes do not affect all consumer sub-sectors and where it does, this effect is heterogeneous. We find that oil price returns have an asymmetric effect on stock returns for most sub-sectors. We devise simple trading strategies and find that while both consumers and producers of oil can make statistically significant profits, investors in oil producer sectors make relatively more profits than investors in oil consumer sectors.
Keywords: Oil Price Returns; Stock Returns; Producer; Consumer; Profits.
No. 2015_11   (Download full text)
Joakim Westerlund, Paresh K Narayan and Xinwei Zheng
Testing For Stock Return Predictability In A Large Chinese Panel
This paper proposes a simple panel data test for stock return predictability that is flexible enough to accommodate three key salient features of the data, namely, predictor persistency and endogeneity, and cross-sectional dependence. Using a large panel of Chinese stock market data comprising more than one million observations, we show that most financial and macroeconomic predictors are in fact able to predict returns. We also show how the extent of the predictability varies across industries and firm sizes.
JEL-Codes: C22; C23; G1; G12.
Keywords: Panel data; Bias; Cross-section dependence; Predictive regression; Stock return predictability; China.
No. 2014_16   (Download full text)
Sagarika Mishra and Sandip Dhole
Stock Price Comovement: Evidence from India
This study examines the extent to which stock prices comove in an emerging economy, India. We first document that stocks listed on the National Stock Exchange (NSE) comove. Further, we find that synchronicity is positively associated with growth and earnings volatility and negatively associated with business group affiliation and leverage.
JEL-Codes: G14, M41
Keywords: Stock Price Synchronicity, Business Groups, Firm Growth, Leverage, Earnings Volatility
No. 2014_15   (Download full text)
Joakim Westerlund and Sagarika Mishra
A Practical Note on the Determination of the Number of Factors Using Information Criteria with Data-Driven Penalty
As is well known, when using an information criterion to select the number of common factors in factor models the appropriate penalty is generally indetermine in the sense that it can be scaled by an arbitrary constant, c say, without affecting consistency. In an influential paper, Hallin and Liˇska (Determining the Number of Factors in the General Dynamic Factor Model, Journal of the American Statistical Association 102, 603�617, 2007)proposes a data-driven procedure for selecting the appropriate value of c. However, by removing one source of indeterminacy, the new procedure simultaneously creates several new ones,which make for rather complicated implementation, a problem that has been largely overlooked in the literature. By providing an extensive analysis using both simulated and real data, the current paper fills this gap.
JEL-Codes: C12; C13; C33.
Keywords: Panel data; Common factor model; Information criterion; Data-driven penalty.
No. 2014_14   (Download full text)
Paresh Kumar Narayan, Huson Ali Ahmed, Susan S Sharma and KP Prabheesh
How Profitable is the Indian Stock Market?
In this paper, using a range of technical trading and momentum trading strategies, we show that the Indian stock market is profitable. We find robust evidence that investing in some sectors is relatively more profitable than investing in others. We show that sectoral heterogeneity with respect to profitability is a result of the gradual diffusion of information from the market to the sectors. Specifically, we show that while the market predicts returns of sectors, the magnitude of predictability varies with sector. Our results are robust to a range of trading strategies.
Keywords: Momentum; Technical Trading; Profits; Sectors; Stock Market; India; Predictability.
No. 2014_13   (Download full text)
Joakim Westerlund and Paresh Kumar Narayan
Testing for Predictability in Panels of Small Time Series Dimensions with an Application to Chinese Stock returns
The few panel data tests for predictability of returns that exist are based on the prerequisite that both the number of time series observations, T, and the number of crosssection units, N, are large. As a result, these tests are impossible for stock markets where lengthy time series data are scarce. In response to this, the current paper develops a new test for predictability in panels where T ≥ 2 but N is large, which seems like a much more realistic assumption when using firm-level data. As an illustration, we consider the Chinese stock market, for which data is only available for 17 years but where the number firms is relatively large, 160.
JEL-Codes: C22; C23; G1; G12.
Keywords: Panel data; Predictive regression; Stock return predictability; China.
No. 2014_12   (Download full text)
Joakim Westerlund, Milda Norkute and Paresh K Narayan
A Factor Analytical Approach to the Efficient Futures Market Hypothesis
Most empirical evidence suggests that the efficient futures market hypothesis, henceforth referred to as EFMH, stating that spot and futures prices should cointegrate with a unit slope on futures prices, does not hold, a finding at odds with many theoretical models. This paper argues that these results can be attributed in part to the low power of univariate tests, and that the use of panel data can generate more powerful tests. The current paper can be seen as a step in this direction. In particular, a newly developed factor analytical approach is employed, which is very general and, in addition, free of the otherwise so common incidental parameters bias in the presence of fixed effects. The approach is applied to a large panel covering 17 commodities between March 1991 and August 2012. The evidence suggests that the EFMH cannot be rejected once the panel evidence has been taken into account.
JEL-Codes: C12; C13; C33; C36.
Keywords: Dynamic panel data models; Unit root; Factor analytical method; Efficient market hypothesis; Futures markets.
No. 2014_11   (Download full text)
Paresh Kumar Narayan and Huson Ali Ahmed
Importance of Skewness in Decision Making: Evidence from the Indian Stock Exchange
In this paper our goal is to examine the importance of skewness in decision making, in particular on investor utility. We use time-series daily data on sectoral stock returns on the Indian stock exchange. We test for sectoral stock return predictability using commonly used financial ratios, namely, the book-to-market, dividend yield and price-earnings ratio. We find strong evidence of predictability. Using this evidence of predictability, we forecast sectoral stock returns for each of the sectors in our sample, allowing us to devise trading strategies that account for skewness of returns. We discover evidence that accounting for skewness leads not only to higher utility compared to a model that ignores skewness, but utility is sector-dependent.
Keywords: Returns; Skewness; Predictability; Utility; Investor.
No. 2014_10   (Download full text)
Joakim Westerlund
A Random Coefficient Approach to the Predictability of Stock Returns in Panels
Most studies of the predictability of returns are based on time series data, and whenever panel data are used, the testing is almost always conducted in an unrestricted unit by unit fashion, which makes for a very heavy parametrization of the model. On the other hand, the few panel tests that exist are too restrictive in the sense that they are based on homogeneity assumptions that might not be true. As a response to this, the current paper proposes new predictability tests in the context of a random coefficient panel data model, in which the null of no predictability corresponds to the joint restriction that the predictive slope has zero mean and variance. The tests are applied to a large panel of stocks listed at the New York Stock Exchange. The results suggest that while the predictive slopes tend to average to zero, in case of book-to-market and cash flow-to-price the variance of the slopes is positive, which we take as evidence of predictability.
JEL-Codes: C22; C23; G1; G12
Keywords: Panel data; Predictive regression; Stock return predictability.
No. 2014_09   (Download full text)
Paresh Kumar Narayan and Susan S Sharma
Do Oil Prices Predict Economic Growth? New Global Evidence
In this paper, we test whether oil price predicts economic growth for 28 developed and 17 developing countries. We use predictability tests that account for the key features of the data, namely, persistency, endogeneity, and heteroskedasticity. Our analysis considers a large number of countries, shows evidence of more out-of-sample predictability with nominal than real oil prices, finds in-sample predictability to be independent of the use of nominal and real prices, and reveals greater evidence of predictability for developed countries.
Keywords: Economic Growth; Predictability; Oil Price.
No. 2014_08   (Download full text)
Paresh Kumar Narayan, Susan S Sharma and Kannan Thuraisamy
An Analysis of Price Discovery from Panel Data Models of CDS and Equity Returns
We propose a panel data model of price discovery. We find that the stock market contributes to price discovery in most sectors while the Credit Default Swap (CDS) market contributes to price discovery in only a few sectors. We discover that in sectors where both the stock market and the CDS market contribute to price discovery, it is the stock market that dominates the price discovery process. When we consider investment grade stocks, the importance of the CDS market in price discovery improves but the stock market still dominates the price discovery process. The results for different sizes of stocks generally suggest that both markets are important for price discovery but it is the stock market that dominates. We also find that while the price discovery process was affected by the 2007 global financial crisis, the stock market still dominated the price discovery process. Finally, in an economic significance analysis, we show that investors in the CDS market are able to make relatively more profits from a forecasting model that takes into account price discovery compared to a model that simply ignores the role of price discovery.
Keywords: Price Discovery; CDS Spread; Panel Data; Sectors; Sizes.
No. 2014_07   (Download full text)
Joakim Westerlund
On the Importance of the First Observation in GLS Detrending in Unit Root Testing
First-differencing is generally taken to imply the loss of one observation, the first, or at least that the effect of ignoring this observation is asymptotically negligible. However, this is not always true, as in the case of GLS detrending. In order to illustrate this, the current paper considers as an example the use of GLS detrended data when testing for a unit root. The results show that the treatment of the first observation is absolutely crucial for test performance, and that ignorance causes test break-down.
JEL-Codes: C12; C13; C33.
Keywords: Unit root test; GLS detrending; Local asymptotic power.
No. 2014_06   (Download full text)
Joakim Westerlund
Pooled Panel Unit Root Tests and the Effect of Past Initialization
This paper analyzes the role of initialization when testing for a unit root in panel data, an issue that has received surprisingly little attention in the literature. In fact, most studies assume that the initial value is either zero or bounded. As a response to this, the current paper considers a model in which the initialization is in the past, which is shown to have several distinctive features that makes it attractive, even in comparison to the common time series practice of making the initial value a draw from its unconditional distribution under the stationary alternative. The results have implications not only for theory, but also for applied work. In particular, and in contrast to the time series case, in panels the effect of the initialization need not be negative but can actually lead to improved test performance.
JEL-Codes: C22; C23.
Keywords: Panel unit root test; Initial value; Local asymptotic power.
No. 2014_05   (Download full text)
Joakim Westerlund, Mehdi Hosseinkouchack and Martin Solberger
The Local Power of the CADF and CIPS Panel Unit Root Tests
Very little is known about the local power of second generation panel unit root tests that are robust to cross-section dependence. This paper derives the local asymptotic power functions of the CADF and CIPS tests of Pesaran (A Simple Panel Unit Root Test in Presence of Cross-Section Dependence, Journal of Applied Econometrics 22, 265312, 2007), which are among the most popular tests around.
JEL-Codes: C12; C13; C33.
Keywords: Panel unit root test; common factor model; cross-sectional averages; crosssectional dependence; local asymptotic power.
No. 2014_04   (Download full text)
Johan Blomquist and JoakimWesterlund
Testing Slope Homogeneity in Large Panels with Serial Correlation
Pesaran and Yamagata (Testing slope homogeneity in large panels, Journal of Econometrics 142, 5093, 2008) propose a test for slope homogeneity in large panels, which has become very popular in the literature. However, the test cannot deal with the practically relevant case of heteroskedastic and/serially correlated errors. The present note proposes a generalized test that accommodates both features.
JEL-Codes: C32; C33.
Keywords: Homogeneity; Panel data; Serial correlation; Heteroskedasticity
No. 2014_03   (Download full text)
Joakim Westerlund
On the Asymptotic Distribution of the DF�GLS Test Statistic
In a very influential paper Elliott et al. (Efficient Tests for an Autoregressive Unit Root, Econometrica 64, 813�836, 1996) show that no uniformly most powerful test for the unit root testing problem exits, derive the relevant power envelope and characterize a family of point-optimal tests. As a by-product, they also propose a �GLS detrended� version of the conventional Dickey�Fuller test, denoted DF�GLS, that has since then become very popular among practitioners, much more so than the point-optimal tests. In view of this, it is quite strange to find that, while conjectured in Elliott et al. (1996), so far there seems to be no formal proof of the asymptotic distribution of the DF�GLS test statistic. By providing three separate proofs the current paper not only substantiates the required result, but also provides insight regarding the pros and cons of different methods of proof.
JEL-Codes: C12; C22.
Keywords: Unit root test; GLS detrending; Asymptotic distribution; Asymptotic local power; Method of proof.
No. 2014_02   (Download full text)
Joakim Westerlund
Heteroskedasticity Robust Panel Unit Root tests
This paper proposes new unit root tests for panels where the errors may be not only serial and/or cross- orrelated, but also unconditionally heteroskedastic. Despite their generality, the test statistics are shown to be very simple to implement, requiring only minimal corrections and still the limiting distributions under the null hypothesis are completely free from nuisance parameters. Monte Carlo evidence is also provided to suggest that the new tests perform well in small samples, also when compared to some of the existing tests.
JEL-Codes: C13; C33
Keywords: Unit root test; Panel data; Unconditional heteroskedasticity; GARCH; Crosssection dependence, Common factors.
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