Evaluating currency crises: A Bayesian Markov switching approach

In this paper we examine the nature of a currency crisis. We do so by employing an out-of-sample forecasting exercise to analyse the Mexican crisis in 1994. Forecast evaluation was based on modern econometric techniques concerning the shape of forecaster’s loss function. We also extend the empirical framework suggested by Jeanne and Masson [Jeanne, O., Masson, P., 2000. Currency crises and Markov-switching regimes. Journal of International Economics 50, 327–350] to test for the hypothesis that the currency crisis was driven by sunspots. To this end we contribute to the existing literature by comparing Markov regime switching model with a time-varying transition probabilities with two alternative models. The first is a Markov regime switching model with constant transition probabilities. The second is a linear benchmark model. Empirical results show that the proxy for the probability of devaluation is an important factor explaining the nature of currency crisis. More concretely, when the expectation market pressure was used as a proxy of probability of devaluation, forecast evaluation supports the view that currency crisis was driven by market expectation unrelated to fundamentals. Alternatively, when interest rate differential is used as a proxy for probability of devaluation, currency crisis was due to predictable deterioration of fundamentals.     

Power issues when testing the Markov switching model with the sup likelihood ratio test using U.S. output

The likelihood ratio (LR) test statistic for the test of a linear AR(1) model against the alternative of a Markov switching model does not possess the standard χ² distribution. Garcia (1998) derives the asymptotic distribution of the Sup LR test statistic under these non-standard conditions allowing the researcher to easily compare the two models. This paper examines the power properties of this test statistic using Monte Carlo experiments calibrated to U.S. output growth data. The results suggest a test of reasonable power. When the experiments are calibrated to annual data, power is 82% at 200 observations. When the experiments are calibrated to quarterly data power is 57% for the same sample size. First Version Received: March 2000/Final Version Received: March 2001     

Monetary shocks,equity returns and volatility: a firm-level panel data analysis

This article studies the impact of monetary policy shocks on equity returns and their volatility among nine industries and their affiliated firms in the United States. We use an extension of the traditional CAPM as the analytical framework and approximate policy shocks with the unexpected component of the federal funds rate. Data on the characteristics of firms and industries are obtained from Compustat and the Center for Research in Security Prices, covering a sample period from 1987 to 2009. Our results clearly show that responses to policy shocks vary by industry and across firms. Furthermore, credit availability matters in certain industries, and small, financially constrained, and bank-dependent firms are found to be more vulnerable to unexpected federal funds rate shocks.     

Impact of macroeconomic factors and country risk ratings on GCC stock markets: evidence from a dynamic panel threshold model with regime switching

This study examines the non-linear relationship between stock markets in GCC countries and their country risk ratings as well as with major macroeconomic factors. Based on a dynamic panel threshold model with two and four regimes, the results provide evidence of short-term asymmetry between first-lagged GCC stock returns and the performance of GCC stock markets. In addition, only the financial risk (FR) rating has a significant positive effect on the performance of GCC stock markets according to the prevailing regimes for the GCC lagged returns and the Brent oil market. Among the macroeconomic factors, improvements in the global stock markets, the MSCI Global Islamic Index, and the oil price increased the performance of GCC stock markets, whereas increases in the gold price, the 3-month U.S. Treasury bill rate, and the U.S. Treasury bond rate reduced the performance of the GCC stock markets. These results have important implications for investors, policymakers, and portfolio managers.     

Plucking models of business cycle fluctuations: Evidence from the G-7 countries

Friedman's `plucking' model, in which output cannot exceed a ceiling level but is occasionally plucked downward by recessions, is tested using Kim and Nelson's formal econometric specification on output data from the G-7 countries. Considerable support for the model is obtained, leading us to conclude that during normal periods, output seems to be driven mostly by permanent shocks, but during recessions and high-growth recoveries, transitory shocks dominate. During these periods macroeconomic models that emphasise demand-oriented shocks, rather than real business cycle type models, may thus be more appropriate. First Version Received: September 2000/Final Version Received: May 2001     

Asymmetric effects of oil price shocks on stock returns: evidence from a two-stage Markov regime-switching approach

The asymmetric effects of oil price shocks on stock returns have attracted the attention of many researchers in the past several decades. Most of these researchers’ studies, however, do not separate out the sources of oil price shocks when examining the asymmetric effects. In this article, we address this limitation using a two-stage Markov regime-switching approach. Our results indicate that oil supply and demand shocks have a null or minimal impact on stock returns in a low-volatility regime and a statistically significant impact in a high-volatility regime. We observe that oil demand shocks affect stock returns significantly more than oil supply shocks. A positive aggregate demand shock significantly increases stock returns, whereas a positive oil-specific demand shock markedly decreases stock returns. These results have important implications for policymakers and investors.     

Nonlinear predictability of stock market returns: Evidence from nonparametric and threshold models

Recent empirical evidence suggests that stock market returns are predictable from a variety of financial and macroeconomic variables. However, with two exceptions this predictability is based upon a linear functional form. This paper extends this research by considering whether a nonlinear relationship exists between stock market returns and these conditioning variables, and whether this nonlinearity can be exploited for forecast improvements. General nonlinearities are examined using a nonparametric regression technique, which suggest possible threshold behaviour. This leads to estimation of a smooth-transition threshold type model, with the results indicating an improved in-sample performance and marginally superior out-of-sample forecast results.     

Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models

The authors examine the predictive capabilities of online investor sentiment for the returns and volatility of MSCI U.S. Equity Sector Indices by including exogenous variables in the mean and volatility specifications of a Markov-switching model. As predicted by the semistrong efficient market hypothesis, they find that the Thomson Reuters Marketpsych Indices (TRMI) predict volatility to a greater extent than they do returns. The TRMI derived from equity specific digital news are better predictors than similar sentiment from social media. In the two-regime setting, there is evidence supporting the hypothesis of emotions playing a more important role during stressed markets compared to calm periods. The authors also find differences in sentiment sensitivity between different industries: it is greatest for financials, whereas the energy and information technology sectors are scarcely affected by sentiment. Results are obtained with the R programming language. Code is available from the authors upon request.     

Testing the null hypothesis of no regime switching with an application to GDP growth rates

This paper presents tests for the null hypothesis of no regime switching in Hamilton’s (Econometrica 57:357–384, 1989) regime switching model. The test procedures exploit similarities between regime switching models, autoregressions with measurement errors, and finite mixture models. The proposed tests are computationally simple and, contrary to likelihood based tests, have a standard distribution under the null. When the methodology is applied to US GDP growth rates, no strong evidence of regime switching is found. I thank Don Andrews, Peter Phillips, Yuichi Kitamura, Anat Bracha, Patrik Guggenberger, Orit Whiteman and three anonymous referees for useful comments and suggestions.     

Testing the demand regime hypothesis in the Euro Area. Evidence from a VAR approach

The article brings new evidence that intra Euro Area trade imbalances should be thought of as the outcome of the interaction of opposing growth strategies between northern/surplus countries (Austria, Belgium, Germany, Netherlands) and southern/deficit countries (France, Italy, Portugal, Spain). By using a vector autoregression model, econometric evidence clarifies that the demand regime in the southern region is wage-led, while profit-led in the northern region. Moreover, a downward-wage adjustment in the northern region (negative wage shock) contributed to increasing the intra-EA trade surplus vis-à-vis the southern region by far more than an upward-wage adjustment in the southern region (positive wage shock).     

Yield curve and the macroeconomy: Evidence from a DSGE model with housing

The slope of the yield curve has long been found to be a useful predictor of future economic activities, but the relationship is unstable. One change we have identified in this paper is that, between the early 1990s and the collapse of the housing market in 2007, movements at the long end of the yield curve have an increase in predictive power. We use a medium-scale DSGE model with a housing sector and a yield curve as a guide to find out the sources of such change. The model implies that an increase in the short-term interest rate and a decrease in the long-term interest rate have different impacts on the economy, and to use the slope as a predictor one needs to distinguish movements at the two ends of the yield curve. Based on simulated data from the model, we find that nominal wage rigidities and the capital adjustment costs are closely related to the predictive power of the yield curve. This result is further confirmed with actual data.     

Predicting long-run risk factors of stock returns: Evidence from Australia

This study utilises the stock market data provided by the Australian Equity Database to analyse the long-run relationship between Australian stock returns and key macroeconomic variables over the period 1926–2017. To measure the diverse risk factors in the stock market, we examine the possible determinants in four main categories: real, financial, domestic and international. Our results reveal that historical stock returns are strongly connected to financial and international factors as compared to real and domestic factors. Both the 1973–1974 OPEC Oil Price Crisis and 2007–2008 Global Financial Crisis had dampening effects on stock returns. There is a positive association between the US and Australian stock markets in the long-run. These findings on stock market dynamics and their linkages with domestic and international macroeconomic policy changes in the long-run have important implications for traders and practitioners.     

Asymmetric volatility of stock returns during the Asian crisis: Evidence from Indonesia

This paper investigates the conditional volatility in stock returns in Indonesia over the period covered by the Asian crisis. Rolling regression parameter estimates from three asymmetric volatility models suggested that all parameters, including those capturing asymmetric response, were time-varying. The precise pattern of adjustment was sensitive to model selection. Nevertheless, increases in asymmetric response patterns appear to coincide with the very large exchange rate devaluations in the rupiah over this period and were followed by more general symmetric short-term volatility in the post crisis period. Estimates from a smooth transition volatility model indicated both sign and size asymmetries during the crisis period.     

Asymmetric effect of advertising on the Chinese stock market

In this paper, we investigate whether investor attention to advertising has an asymmetric effect on Chinese stock returns by using a multivariate Markov switching model with time-varying regime transition probabilities. Using the Chinese stock market as a setting, we obtain lagged conditional volatility from generalized autoregressive conditional heteroskedasticity (GARCH) for modelling the time-varying transition probabilities of the regime-switching process to capture changes in the market regime. Our evidence documents that the high advertising portfolio does earn higher abnormal return than the low advertising portfolio in low-volatility periods. In high-volatility periods, however, the abnormal return is insignificant when the firm increases advertising spending. Our results support the behavioural model argument that in high-volatility period, advertising information diffuses slowly due to cognitive dissonance. Thus, the effect of advertising on stock returns is asymmetric, and it shows statistical significance in low-volatility periods.     

Analysis of contagion from the dynamic conditional correlation model with Markov Regime switching

Over the last decades, the transmissions of international financial events have been the subject of many academic studies focused on multivariate volatility models. This study evaluates the financial contagion between stock market returns. The econometric model employed, regime switching dynamic correlation (RSDC). A modification was made in the original RSDC model, the introduction of the GJR-GARCH-N and also GJR-GARCH-t models, on the equation of conditional univariate variances, thus allowing us to capture the asymmetric effects in volatility and also heavy tails. A database was built using series of indices in the United States (S&P500), the United Kingdom (FTSE100), Brazil (IBOVESPA) and South Korea (KOSPI) from 1 February 2003 to 20 September 2012. Throughout this study the methodology is compared with those frequently found in literature, and the model RSDC with two regimes was defined as the most appropriate for the selected sample with t-Student distribution in the disturbances. The adapted RSDC model used in this article can be used to detect contagion – considering the definition of financial contagion from the World Bank called very restrictive – with the help of the empirical exercise.     

Patterns of technology life cycles: stochastic analysis based on patent citations

Patent analysis has been considered as an effective means of estimating phases of a technology life cycle. However, previous studies have not considered the dynamic and idiosyncratic aspects of a technology’s progression since they were based on deterministic methods, mainly fitting s- or double s-shaped curves to patent application counts. Moreover, previous methods cannot be executed at the individual patent level. We propose a stochastic technology life cycle analysis to trace the phases of a technology’s progression based on patent citations and identify the patterns of technology life cycles at the individual patent level. At the heart of the proposed approach are a hidden Markov model to estimate the probability of a system being at a certain hidden state from observation and cluster analysis to group a set of objects according to their similarities. A case study of patents about laser technology in lithography is presented.     

Gender,productive ability and the perceived returns to education: Evidence from rural India

Recent research has established the importance of perceived (as opposed to actual) returns as a determinant of educational investments. We analyze data from India to highlight gender disparities in the perceived returns to education and the salience of productive characteristics.     

A switching ARCH (SWARCH) model of stock market volatility: some evidence from Latin America

We use the Markov regime-switching ARCH (SWARCH) model of Hamilton and Susmel (J Econometrics 64:307–333, 1994) to document the presence of high volatility regimes in six Latin American countries (Argentina, Brazil, Chile, Mexico, Peru, and Venezuela). We found four high volatility episodes, each associated to either a local (the Mexican crisis of 1994, the Brazilian crisis of 1998–1999, the Argentinean crisis of 2001–2002) or a worldwide financial crisis (the Asian financial crisis of 1997). However, we found that the effects of each financial crisis are short-lived and that between 2 and 4 months after each crisis, all markets return to low volatility regimes.

What determines the productivity growth? Evidence from Chinese renewable energy firms by using Bayesian stochastic frontier approach

This paper examines the determinants of the total factor productivity (TFP) growth of Chinese renewable energy firms from 2011 to 2016, using a Bayesian stochastic frontier approach. Employing the “Bauer–Kumbhakar” decomposition method, the TFP growth is decomposed into the technology part and the market part. The empirical results reveal that the TFP improvement of Chinese renewable energy firms is mainly due to technical progress, followed by technical efficiency change. With regard to the market part, the misallocation of production factors has hindered the TFP growth. Our findings also indicate that only for large firms, the TFP growth can benefit from the scale economy effect. Compared with non‐state‐owned firms, state‐owned firms suffer much lower allocative efficiency.