Oil prices and the macroeconomy reconsideration for Germany: Using continuous wavelet

The cross wavelet analysis is used in the study to decompose the time–frequency effects of oil price changes on the German macroeconomy. We argue that the relationship between oil prices and industrial production is ambiguous. Our results show that there are both phase and anti-phase relationships between oil price returns and inflation and in most of the cases inflation is the leading variable. Additional evidence shows that there is a huge inconsistency between the phase-difference of the return series of oil price and industrial production at the 12–16 month frequency bands but at the 16–24 month frequency bands, we find that oil price changes that have occurred during 1982–2009 were demand-driven. In a nutshell our results suggest that oil price changes that have occurred after 1994 were demand-driven and the volatility of the inflation rate started to decrease after the 1990s but the volatility of the industrial output growth rate started to decrease after the 2000s.     

Growth-returns nexus: Evidence from three Central and Eastern European countries

Using a sample of monthly data from January 1996 to December 2012, we provide new evidence on the unidirectional Granger causality from real stock market returns to real economic activity in three Central and Eastern European countries: the Czech Republic, Hungary, and Poland. By employing the Granger causality tests of Cheung and Ng (1996) and Hong (2001), we show evidence of short-term (up to 6 months), medium-term (up to 12 months) and long-term (up to 24 months) causality for the Czech Republic and Hungary. In the case of Poland, only medium-term and long-term causality is found. Using rolling-correlation analysis, we find that although the growth–returns relationship is positive during the examined period, there is an apparent variability in the strength of this relationship that is particularly visible during the period of the financial crisis in the late 2000s. Consequently, we find that the predictive power of stock markets in the CEE-3 countries increases during periods of high market volatility and decreases during periods of economic recovery.     

Time-varying relationship of news sentiment,implied volatility and stock returns

I examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.     

Volatility forecasting of exchange rate by quantile regression

Exchange rates are known to have irregular return patterns; not only their return volatilities but the distribution functions themselves vary with time. Quantile regression allows one to predict the volatility of time series without assuming an explicit form for the underlying distribution. This study presents an approach to exchange rate volatility forecasting by quantile regression utilizing a uniformly spaced series of estimated quantiles. Based on empirical evidence of nine exchange rate series, using 19 years of daily data, the adopted approach generally produces more reliable volatility forecasts than other key methods.     

Hitting SKEW for SIX

In this study, we propose “SIX” as a new forward-looking index of negative market skew derived from state-preference pricing. Specifically, SIX is a forecast of the ratio of lower to upper partial moment volatility over a 30-day horizon, for SPX market returns. Using SPX options data from 1996 to 2013, we conduct a comparison between SIX and the CBOE SKEW index. First, we document that the daily change in VIX and SIX (SKEW) are negatively (positively) related. Second, we show that the daily change of SIX (SKEW) adds (does not add) significant explanatory power for predicting the one-day ahead return. Third, though biased, SIX produces an efficient forecast of future physical skewness. In contrast, there is no statistically significant relationship between SKEW and physical skewness. Collectively, our results suggest that as an indicator of institutional anxiety, both theoretically and in practice, SIX (SKEW) is a more than useful (questionable) complement to VIX.     

Would it have paid to be in the eurozone?

Giving up an independent monetary policy and a flexible exchange rate are the key aspects of joining a monetary union. In this paper we analyse how joining the euro area would have affected the Polish business cycle during the recent financial crisis. To this end we construct a small open economy DSGE model and estimate it for Poland and the euro area. Then we run a counterfactual simulation, assuming Poland's euro area accession in 1q2007. The results are striking — volatilities of GDP and inflation increase substantially. In particular, had Poland adopted the euro, GDP growth would have oscillated between − 6% and + 9% (− 9% to + 11% under more extreme assumptions) instead of between 1% and 7%. We conclude that during the analysed period independent monetary policy and, in particular, the flexible exchange rate played an important stabilizing role for the Polish economy.     

What do ads buy? Daily coverage of listed companies on the Italian press

We match daily data on newspaper coverage of a sample of Italian listed companies with monthly data on the amount of advertising that a given company has purchased on a given newspaper. Controlling for time-invariant features of each newspaper and of each company – and for ownership links between companies and newspapers – we show that newspaper coverage of a company is positively and significantly related with advertising expenditure by that company on that newspaper. The magnitude of this correlation is quite large: when controlling for ownership links, a standard deviation increase in monthly ads expenditure (i.e. 75,000 euros) is on average associated with 8 additional articles per month mentioning that company. We also find that coverage of a company is higher the day after a press release, but especially in newspapers where more ads are purchased. This result on press releases is robust to controlling for time invariant features of each company–newspaper pair, i.e. for (company × newspaper) fixed effects.Moreover, coverage is correlated with past day absolute return and trading volume, and this relationship appears to be steeper for those newspapers where more ads are purchased, especially in the case of positive returns.     

Spillovers of volatility index: evidence from U.S., European,and Asian stock markets

We investigated the cross-market relations of volatility indexes with U.S. and non-U.S. stock market returns. We found that the pervasive VIX influence at both U.S. and non-U.S. stock markets. The VSTOXX and VKOSPI capture the major shocks to the global economy and show movements similar to the VIX. The empirical findings indicate that volatility index changes are important in explaining stock returns. We also examined spillover effects across volatility indexes. The VIX is a main transmitter, and the VKOSPI the main receiver, of these spillovers. The results point to a leading role for the VIX in the international market.     

The impact of mortgage interest-rate subsidies on household borrowing

The responsiveness of household borrowing to changes in the interest rate is a crucial parameter for assessing public policies aimed at promoting saving. We estimate the effect on household borrowing of a reform of a program that subsidized interest rates on mortgages signed by medium- and low-income households. The reform established a ceiling in the price of the house that could be financed with the program. First, we use triple difference estimators exploiting the fact that the reform should affect most the borrowing behavior of eligible individuals living in high-price areas, and estimate that the elasticity of the probability getting a loan to the interest rate lies between − 2.8 and − 1.3. Second, we document that after the reform, the distribution of loan sizes became more concentrated at the discontinuity point of the budget constraint of eligible individuals. Both findings are consistent with a negative response of household borrowing to increases in interest rates.     

Integrating bank profit and risk-avoidance decisions for selected European countries: A micro–macro analysis

A two-equation integrated model is developed to capture bank profit and risk-avoidance decisions. Output is limited to customer loans. The profit function is based on output and selected inputs. Risk-avoidance (using the capitalization ratio) depends on micro and micro 1 macro interactive variables. The SUR method is used to test the hypothesis that the two functions are interdependent. Also, a single reduced-form equation is derived from the SUR model to analyze the volatility of the capitalization ratio. Five European countries and their banks for the period 1991–2001 are used to run the regressions and to test the hypothesis. The individual statistical results were generally consistent with similar results found in the literature. The Breusch–Pagan test of independence was rejected. A key finding from the volatility analysis suggests that bank profit rates are inversely related to the volatility of the banks' capitalization ratios as measured by their variances.     

R&D, patents and stock return volatility

Recent finance literature highlights the role of technological change in increasing firm specific (idiosyncratic) and aggregate stock return volatility, yet innovation data is not used in these analyses, leaving the direct relationship between innovation and stock return volatility untested. The paper investigates the relationship between volatility and innovation using firm level patent data. The analysis builds on the empirical work by Mazzucato (Rev Econ Dyn 5:318–345, 2002; J Evol Econ 13(5):491–512, 2003) where it is found that stock return volatility is highest during periods in the industry life-cycle when innovation is the most ‘radical’. In this paper we ask whether firms which invest more in innovation (more R&D and more patents) and/or which have more important innovations (patents with more citations) experience more volatility in their returns. Given that returns should in theory be higher, on average, for higher risk stocks, we also look at the effect of innovation on the level of returns. To take into account the competition between firms within industries, firm returns and volatility are measured relative to the industry average. We focus the analysis on firms in the pharmaceutical industry between 1974 and 1999. Results suggest that there is a positive and significant relationship between volatility, R&D intensity and the various patent related measures—especially when the innovation measures are filtered to distinguish the very innovative firms from the less innovate ones.     

An application of Lotka–Volterra model to Taiwan's transition from 200 mm to 300 mm silicon wafers

In this paper, we explore the innovation growth of 200 mm and 300 mm silicon wafers from Taiwan. Using the historic data, we simulate the growth of the area of 200 mm and 300 mm silicon wafers manufactured in Taiwan by the competitive Lotka–Volterra model. The parameters in the Lotka–Volterra model estimated with the realistic data are obtained numerically. The dynamic growth of competitive relationship between 200 mm silicon wafers and 300 mm silicon wafers is then analyzed. To prove the performance of the model, we further compare the famous Bass model and the Lotka–Volterra model. We also perform the equilibrium analysis to determine the long-term stability state in the simulation trajectory. Our research exhibits that 200 mm silicon wafers and 300 mm silicon wafers show a prey–predator relationship under the assumption of natural competition in the global semiconductor market. From a managerial perspective, the coefficients in the Lotka–Volterra model of exponential growth, self-interaction and cross-interaction represent the strength of product attractiveness, niche capacity and interaction for two competition products. We also find that there exists a stable equilibrium state for 200 mm silicon wafers and 300 mm silicon wafers. The prey 200 mm generation does not disappear completely; it finally settles to a constant market alongside the predator 300 mm generation.     

Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram

This paper presents a new method to estimate the fractional differencing parameters in the SARFIMA model. A technique of split cosine bell tapering is suggested to improve the EGPH method. The simulation study shows that the optimal split proportion and bandwidth for the EGPH with split cosine bell tapering method respectively are p = 0.1 and b = 0.9. The new method with the optimal parameters outperforms the EGPH and EGPH with cosine bell tapering. We further applied the EGPH method to estimate intraday volume series and high-frequency absolute return data. The results show that the seasonal fractionally differencing parameters are all estimated to be large, while the nonseasonal fractionally differencing parameters are all very small. This indicates that their long memory property may be mainly caused by the structure of long-range dependence at the seasonal lags instead of dependence at the nonseasonal lags.     

Hedging and diversification across commodity assets

ABSTRACT

We investigate the conditional cross effects and volatility spillover between equity markets and commodity markets (oil and gold), Fama and French HML and SMB factors, volatility index (VIX) and bonds using different multivariate GARCH specifications considering the potential asymmetry and persistence behaviours. We analyse the dynamic conditional correlation between the US equity market and a set of commodity prices and risk factors to forecast the transmission of shock to the equity market firstly, and to determine and compare the optimal hedge ratios from the different models based on the hedging effectiveness of each model. Our findings suggest that all models confirm the significant returns and volatility spillovers. More importantly, we find that GO-GARCH is the best-fit model for modelling the joint dynamics of different financial variables. The results of the current study have implications for investors: (i) the equity market displays inverted dynamics with the volatility index suggesting strong evidence of diversification benefit; (ii) of the hedging assets gold appears the best hedge for the US equity market as it has a higher hedge effectiveness than oil and bonds over time; and (iii) despite these important results, a better hedge may be obtained by using well-selected firm sized and profitability-based portfolios.     

Fitting survey expectations and uncertainty about trend inflation

Many studies document that the inflation rate is governed by persistent trend shifts and time-varying uncertainty about trend inflation. As both these quantities are unobserved, a forecaster has to learn about changes in trend inflation by a signal extraction procedure. I suggest that the forecaster uses a simple IMA(1, 1) model because it is well suited to forecast inflation and it provides an efficient way to solve the signal extraction problem. I test whether this model provides a good fit for expectations from the Survey of Professional Forecasters. The model appears to be well suited to model observed inflation expectations if we allow for stochastic volatility. When I estimate the implied learning rule, results are supportive for the trend learning hypothesis. Moreover, stochastic volatility seems to influence the way agents learn over time. It appears that survey participants systematically adapt their learning behavior when inflation uncertainty changes.     

Application of artificial neural networks in tendency forecasting of economic growth

Economic growth results from the synthesis influence of various known or unknown and certain or uncertain factors. Mapping of the stimuli effects and the input and output estimates of artificial neural networks (ANN) are obtained via combinations of nonlinear functions. This approach offers the advantages of self-learning, self-organization, self-adaptation, and fault tolerance, as well as the potential for use in forecasting applications of economic growth. Furthermore, the ANN technology allows the use of multiple variables in both the input and output layers. This capability is very important for economic growth calculations because economic development is often a function of many influential variables. Herein, a forecasting system of economic growth with related application has been proposed, based on ANN. The results show that the Zhejiang proportions of tertiary industry in China for 1995, 1996, and 1997 were 32.305%, 32.174%, and 32.114%, respectively, and the comparative errors e_ann were 0.64%, − 0.08%, and − 0.27%, respectively, indicating that the forecast result of ANN was better than that of the GM(1.1) model. This method offered better performance and efficiency.     

Tiebout equilibria in local public good economies with spillovers

This paper analyzes the effects of spillovers on the equilibrium population distribution across jurisdictions in a local public good economy with free mobility. Spillovers are parametrized by a matrix [α_ij] where α_ij ∈ [0, 1]. When spillovers are symmetric and close to 0 or 1 (pure local public goods and pure public goods), all equilibrium jurisdiction structures are symmetric. However, any population distribution can be sustained in equilibrium for some value of the spillover parameter α. In the class of utility functions with additive externalities, we identify the unique family of utility functions for which equilibria are symmetric except for an isolated value of α. This is a class of utility functions which are linear in the public good and a power function of the private good, u(c, γ) = − A(1 − c)^β + γ. With this specification of utility, we show that an increase in α results in a more fragmented equilibrium population distribution, and that when spillovers are asymmetric and large, a jurisdiction which is more centrally located (i.e. benefits more from the public goods provided in other jurisdictions) has a larger population in equilibrium.     

Functional sensitivity of testing the environmental Kuznets curve hypothesis

Conventional tests for the environmental Kuznets curve (EKC) hypothesis mostly apply a quadratic equation in modeling the non-linear relationship between environmental indices (such as air pollutants) and welfare measures (such as income per capita). If their inverted-U shaped pattern is empirically accepted with two significant regressors, the income per capita and its square transformation, the EKC hypothesis is supported. Using an OECD sample, this paper shows that the validity of testing the EKC hypothesis is sensitive to how we transform income non-linearly in sulfur and carbon EKC regressions. This paper carries out experiments on different powers of γ for transforming income non-linearly and concludes that only when 0 < γ < 1 and 1 < γ < 2 will the EKC regression demonstrate a testable non-linear cointegration relationship between the two air pollutants and income per capita. In the generalized EKC regressions estimated in this paper, although we find sulfur and carbon EKC patterns in the OECD sample, none of the EKC regressions using different γ is a cointegrating equation. This finding implies an inside critique to the EKC literature that failure of cointegration of the conventional EKC regression is not because of using the quadratic functional form, but because of the fundamentally spurious relationship between the trends of pollutants and income levels.     

Functional sensitivity of testing the environmental Kuznets curve hypothesis

Conventional tests for the environmental Kuznets curve (EKC) hypothesis mostly apply a quadratic equation in modeling the non-linear relationship between environmental indices (such as air pollutants) and welfare measures (such as income per capita). If their inverted-U shaped pattern is empirically accepted with two significant regressors, the income per capita and its square transformation, the EKC hypothesis is supported. Using an OECD sample, this paper shows that the validity of testing the EKC hypothesis is sensitive to how we transform income non-linearly in sulfur and carbon EKC regressions. This paper carries out experiments on different powers of γ for transforming income non-linearly and concludes that only when 0 < γ < 1 and 1 < γ < 2 will the EKC regression demonstrate a testable non-linear cointegration relationship between the two air pollutants and income per capita. In the generalized EKC regressions estimated in this paper, although we find sulfur and carbon EKC patterns in the OECD sample, none of the EKC regressions using different γ is a cointegrating equation. This finding implies an inside critique to the EKC literature that failure of cointegration of the conventional EKC regression is not because of using the quadratic functional form, but because of the fundamentally spurious relationship between the trends of pollutants and income levels.     

RISK PERCEPTION AND EQUITY RETURNS: EVIDENCE FROM THE SPX AND VIX

We use the semi‐nonparametric (SNP) model to study the relationship between the innovation of the Volatility Index (VIX) and the expected S&P 500 Index (SPX) returns. We estimate the one‐step‐ahead contemporaneous relation subject to leverage GARCH effect. Results agree with a body of newly established literature arguing non‐linearity, and asymmetries. In addition, the risk‐return behaviour depends on the signs as well as magnitudes of the perceived risk. We conclude that influence of fear or exuberance on the conditional market return is non‐monotonic and hump‐shaped. Very deep fear does not necessarily mean huge losses, instead, the loss may not be as bad as fears of normal levels. Results pass the robustness tests.