Financial Econometics Series
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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, 265–312, 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, 50–93, 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.
No. 2014_01   (Download full text)
Joakim Westerlund and Paresh Kumar Narayan
Testing for Predictability in Conditionally Heteroskedastic Stock Returns
The difficulty of predicting stock returns has recently motivated researchers to start looking for more powerful tests, and the current paper takes a step in this direction. Unlike existing tests, the test proposed here exploits the information contained in the heteroskedasticity of returns, which is expected to lead to higher power, a result that is confirmed by our results. In order to also maintain good size accuracy, subsample critical values are used.
JEL-Codes: C12; C22; G1.
Keywords: Predictability; FQGLS; Conditional heteroskedasticity; Subsampling; Stock returns.
No. 2013_06   (Download full text)
Paresh Kumar Narayan and Sagarika Mishra
Determinants of Stock Price Bubbles
In this paper we propose a cross-sectional model of the determinants of asset price bubbles. Using 589 firms listed on the NYSE, we find conclusive evidence that trading volume and share price volatility have statistically significant effects on asset price bubbles. However, evidence from sector-based stocks is mixed. We find that for firms belonging to electricity, energy, financial, and banking sectors, and for the smallest size firms, trading volume has a statistically significant and positive effect on bubbles. We do not discover any robust evidence of a statistically significant effect of share price volatility on bubbles at the sector-level.
Keywords: Asset Price; Bubbles; Cross-section; Trading Volume; Volatility.
No. 2013_05   (Download full text)
Sagarika Mishra and Sandeep Dhole
Least Squares Learning and the US Treasury Bill Rate
Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perrron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning.
Keywords: Survey forecasts, Least Squares Learning
No. 2013_04   (Download full text)
Kannan Thuraisamy
Intra-market Sovereign Linkages of Latin American International Bonds
This paper tests the intra-market dynamics in a regional setting using country specific international bonds differentiated only by maturity within individual markets in the Latin American region. We use 2001 Argentine default as a natural experiment in this study to examine how intra-market dynamics evolved in the presence of a credit event in the region. This paper argues that emerging market instruments have a stronger tendency to tie up with instruments within markets rather than across markets as found in the literature. The long-run equilibrium relationships tend to be stronger across instruments within each market and generate economically insignificant portfolio adjustment weights. Strong interaction across instruments within markets in terms of first order dependencies has important implications for market participants, practitioners and policy makers.
Keywords: Intra-market dynamics; Sovereign linkages; Common stochastic component
No. 2013_03   (Download full text) (Published)
Paresh Kumar Narayan, Susan S Sharma and Deepa Bannigidadmath
Does Tourism Predict Macroeconomic Performance in Pacific Island Countries?
In this paper we examine whether tourism predicts macroeconomic variables in Pacific Island countries (PICs), namely, Fiji, the Solomon Islands, PNG, Vanuatu, Samoa, and Tonga. We form seven panels of PICs—one full panel of six countries and six panels where, one-by-one, each country is excluded from the panel. We apply the Westerlund and Narayan (2012a) panel regression framework, where the null hypothesis is that visitor arrivals do not predict macroeconomic variables, which we proxy with 11 indicators, for panels of countries. We find that visitor arrivals consistently predict exports and money supply, and to a lesser extent, exchange rates and GDP.
Keywords: Tourism; Macroeconomic variables; GDP; Money Supply; Panel Data; Predictive Regression Model.
 
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