U.S. industry-level returns and oil prices

This paper empirically investigates and provides further support for the oil price effect documented in Driesprong et al. (2008) in the U.S. industry-level returns. We find that oil price predictability is concentrated in a relatively small number of industry-level returns, the relevant measure for a study of the oil effect is percentage change in oil spot prices, and changes in oil futures prices have virtually no prediction power for industry-level returns. With percentage changes in oil spot prices as the predictor, approximately one fifth of industry returns are oil-predictable. We detect a two trading weeks delay in reaction to oil price changes which is consistent with the Hong and Stein (1996) underreaction hypothesis. These results are robust to various alternative specifications, and are shown to be unrelated to time-varying risk premia. Moreover, we demonstrate that trading strategies based on the oil effect generate superior gains in comparison with buy-and-hold strategy in the presence of reasonable trading costs.     

The impact of interest rate and exchange rate volatility on banks' stock returns and volatility: Evidence from Turkey

This paper investigates the effects of interest rate and foreign exchange rate changes on Turkish banks' stock returns using the OLS and GARCH estimation models. The results suggest that interest rate and exchange rate changes have a negative and significant impact on the conditional bank stock return. Also, bank stock return sensitivities are found to be stronger for market return than interest rates and exchange rates, implying that market return plays an important role in determining the dynamics of conditional return of bank stocks. The results further indicate that interest rate and exchange rate volatility are the major determinants of the conditional bank stock return volatility.     

Institutional development and stock price synchronicity: Evidence from China

Better developed legal and political institutions result in greater availability of reliable firm-specific information. When stock prices reflect more firm-specific information there will be less stock price synchronicity. This paper traces the experience of China, an economy undergoing dramatic institutional change in the last 20 years with rich variation in experiences across provinces. We show that stock price synchronicity is lower when there is institutional development in terms of property rights protection and rule of law. Furthermore, we investigate the influence of political pluralism on synchronicity. A more pluralistic regime reduces uncertainty and opaqueness regarding government interventions and therefore increases the value of firm-specific information that reduces synchronicity.     

The Efficiency of the Realized Range Measure of Daily Volatility: Evidence from Greece

In this study we estimate and compare the realized range volatility, a novel efficient volatility estimator computed by summing high–low ranges for intra‐day intervals, to the recently popularized realized variance estimator obtained by summing squared intra‐day returns. Our results, derived from a Greek equity high‐frequency data set, show that realized range‐based measures improve upon the corresponding realized variance‐based ones in most cases, especially for the most actively traded stocks. The usefulness of high‐frequency data in measuring and forecasting financial volatility is apparent throughout the paper.     

The heterogeneous volume-volatility relations in the exchange-traded fund market: Evidence from China

We decompose the trading volume of exchange-traded funds (ETFs) into specific components according to different triggers of trades: (i) private information, (ii) disagreement among investors due to their different opinions on public information or having different information, and (iii) investor impatience. Then we examine the particular impact of each type of ETF trade on the market volatility of the tracked index. Focusing on the three ETFs tracking the CSI 300, we show that ETF trades stemming from investor disagreement are a key determinant of CSI 300 volatility, dominating other factors considered. Liquidity ETF trades can partially explain CSI 300 volatility. However, little evidence supports a significant correlation between privately informed trades of ETFs and CSI 300 volatility.     

Predictions of growth in U.S. corporate profits: Asymmetric vs. symmetric loss

This study evaluates the Federal Reserve and private forecasts of growth in corporate profits for 1984-2004. These forecasts are both rational and directionally accurate but suggest different loss structures. The Federal Reserve forecasts tend to significantly under-predict and imply asymmetric loss. The private forecasts, however, are free of such bias, suggesting symmetric loss. Given that the Federal Reserve forecasts are made to help with policymaking, our findings point to the Fed's cautiousness not to incorrectly predict the downward moves in growth in corporate profits. The private forecasts are made by experts who (with a strong profit-motivated interest) attempt to generate financial gain and thus predict the upward moves as accurately as the downward moves.     

Volatility forecasting: combinations of realized volatility measures and forecasting models

This article examines financial time series volatility forecasting performance. Different from other studies which either focus on combining individual realized measures or combining forecasting models, we consider both. Specifically, we construct nine important individual realized measures and consider combinations including the mean, the median and the geometric means as well as an optimal combination. We also apply a simple AR(1) model, an SV model with contemporaneous dependence, an HAR model and three linear combinations of these models. Using the robust forecasting evaluation measures including RMSE and QLIKE, our empirical evidence from both equity market indices and exchange rates suggests that combinations of both volatility measures and forecasting models improve the forecast performance significantly.     

Determinants of land value volatility in the U.S. Corn Belt

ABSTRACT

Understanding land value volatility and its reaction to exogenous shocks helps land owners, investors, and lenders assess risk. Land value volatility, the variance of the unpredictable component of land value growth rates, is modelled for each of the Corn Belt states in the U.S. using EGARCH. A pooled VAR system is then estimated to capture the interactions between land value determinants and land value volatility. The variables of the pooled VAR are split into negative and positive vectors to allow for asymmetric impacts. Impulse response functions are mapped. All states exhibit land value volatility clustering. Inflation, cash rent and population growth rates granger cause land value volatility. Land value volatility responses to negative shocks are greater than those to positive shocks. Lenders and investors should expect greater swings in land values after negative shocks to land value growth rates, but not an overreaction of land values from shocks to cash rent growth rates. Positive shocks to changes in interest rates increases land value volatility, but unexpected shocks to population growth rates do not have statistically significant impact on land value volatility.     

Asymmetric effect of exchange rate volatility on India's cross‐border trade: Evidence from global financial crisis and multiple threshold nonlinear autoregressive distributed lag model

This paper examines the asymmetric effect of exchange rate volatility on India's cross‐border trade with its major trading partners: Japan, Germany, the United States, and China. We extend previous studies in two ways. First, we examine whether global financial crisis changes the asymmetric effect of exchange rate volatility on India's cross‐border trade. Next, we divide exchange rate volatility into quintiles and examine the effect of each quintile on cross‐border trade by using the multiple threshold nonlinear autoregressive distributed lag (MTNARDL) model. Our findings from standard nonlinear ARDL (NARDL) indicate that the asymmetric relationship between exchange rate volatility and cross‐border trade changes as a result of global financial crisis. In addition, findings from MTNARDL indicate that in short‐run, exchange rate volatility symmetrically affects India's cross‐border trade with all sample countries whereas in long‐run it asymmetrically affects cross‐border trade. Overall, these findings are very important for policy implications and open a new dimension to exchange rate volatility and trade flows.     

On the effects of income volatility on income distribution: Asymmetric evidence from state level data in the U.S.

A previous study that tried to assess the impact of income volatility on income inequality in the U.S. used state level data and a balanced panel model to conclude that increased volatility worsens income distribution in the U.S., which implies that decreased volatility should reduce inequality. We use the same data set that is extended by nine years and revisit the issue using linear and nonlinear ARDL time-series models to show that the above conclusion does not hold in every state. While we discover short-run asymmetric effects of income volatility on a measure of inequality in most states, they translate to long-run asymmetric effects only in 16 states. Both increased volatility and decreased volatility are found to have unequalizing effects on income distribution in these states.     

Dynamics of volatility transmission between the U.S. and the Chinese agricultural futures markets

The U.S. and China are two of the biggest players in the world agricultural market. The literature documents that volatility in the U.S. agricultural futures market spills over significantly to that of China. This article provides further insights into the spillovers from China to the U.S. as well as the time horizon and dynamics of the bidirectional spillovers through the application of a multivariate extension of the heterogeneous autoregressive model, in relation to four commodities – soybean, wheat, corn and sugar. The results confirm the existence of significant spillovers from the U.S. to China for four commodities, which are primarily generated by the shorter-term volatility components in the U.S., and provide evidence for the increasing pricing power of the Chinese market. The findings are robust against various specifications and have important investment and policy implications.     

Official central bank interventions and exchange rate volatility: Evidence from a regime-switching analysis

In this paper, we investigate the effect of central bank interventions on the weekly returns and volatility of the DEM/USD and YEN/USD exchange rate returns. In contrast with previous analyses, we allow for regime-dependent specifications and investigate whether official interventions can explain the observed volatility regime switches. It is found that, depending on the prevailing volatility level, coordinated central bank interventions can lead to either a stabilizing or a destabilizing effect. Our results lead us to challenge the usual view that such interventions always imply increases in volatility.     

Global predictive power of the upside and downside variances of the U.S. equity market

Given the pace of increasing globalization and the pioneering role of the U.S. economy, we anlayze the global impact of the U.S. equity market’s uncertainty. The asymmetric impact of upside (downside) uncertainty, related with the upward (downward) movements of the underlying assets, has raised substantial concerns recently. We comprehensively analyze the global predictability of the upside and downside variances of the U.S. equity market, implied by S&P-500 calls and puts, respectively. We contribute to the literature on the asymmetric impacts of the upside and downside variances of the U.S. equity market in an international setting. Our study also complements the study on predicting international stock returns. Moreover, substantial economic value can be generated from the perspective of asset allocation. The main channel for the positive (negative) predictability of upside (downside) variance stems from its positive (negative) impacts on international investment, highlighting the leading role of the U.S. economy.     

Overnight exchange rate risk based on multi-quantile and joint-shock CAViaR models

Overnight risk of exchange rate is more and more important because the exchange rate trading time of various countries is inconsistent. Drawing on the multi-quantile CAViaR model for two markets, this study proposes a multi-quantile CAViaR model for three markets and a multi-quantile CAViaR model for joint shock. The two new models are used to measure the impact of the U.S. Dollar index and the Euro on the overnight risk for the exchange rate of the Japanese Yen, Hong Kong Dollar, and Chinese Renminbi. The results show that, first, a lag risk affects the overnight risk of the three exchange rates, of which the Renminbi exchange rate is subject to the largest risk. Second, the U.S. Dollar index and Euro exchange rate risks impact the overnight risk of the three exchange rates and this effect is highest for the overnight risk of the Yen's exchange rate. In addition, the impact of the U.S.Dollar index risk is greater than that of the Euro. Third, the Euro and U.S.Dollar index produce a joint shock on the overnight risk of the three exchange rates, and here, the Yen's exchange rate suffers the biggest shock. Finally, the multi-quantile CAViaR model for joint shock is more accurate than that for three markets, particularly when the Hong Kong Dollar exchange rate has a 5% VaR. These empirical results have meaningful implications for regulatory authorities.     

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.     

Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.     

The relationship between trading activity and stock market volatility: Does the volume threshold matter?

This paper examines whether trading activity conveys valuable information about changes in market volatility dynamics. We use a modelling framework, in which the market smoothly switches from one state to another, according to the volume level. Results show that large volume drives the high volatility regime for most of the markets, quite consistently with the disagreement-in-beliefs hypothesis. The volume decomposition into normal trading activity and surprising information arrival reveals a reverse threshold linkage for emerging markets. Results support the sequential information arrival hypothesis and highlight the key role of asymmetric information and thin trading in modelling the volume-volatility relationship. The proposed volume-based models provide significant forecast improvements over competing models and offer scope for investors to earn substantial profits.     

Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models

This paper studies the nonlinear adjustment between industrial production and carbon prices – coined as ‘the carbon-macroeconomy relationship’ – in the EU 27. We model carbon price returns and industrial production as nonlinear and state-dependent, with dynamics depending on the sign and magnitude of past realization of returns and the growth of industrial production. Our findings show that (i) macroeconomic activity is likely to affect carbon prices with a lag, due to the specific institutional constraints of this environmental market; (ii) the joint dynamics of industrial production and carbon prices seem adequately captured by two-regime threshold vector error-correction and two-regime Markov-switching VAR models compared to linear models as main competitors. The regime-switching models proposed are profoundly checked for their economic content and statistical congruency, and are found to provide a sound statistical framework for a comprehensive analysis of the carbon-macroeconomy relationship.     

The influence of cultural distance on the volatility of the international stock market

Along with the development of cultural dimensions and cultural distance, the influence of cultural variables on the stock market is attracting more and more attention. In this study, we propose an improved gravity model to examine the relationship between culture and the volatility of the international stock market. Firstly, based on Hofstede's cultural dimensions theory, a model of the impact of cultural dimensions on the volatility of the national stock market is presented. Secondly, cultural distance is incorporated into the extended gravity model. Then, models of the impact of cultural distance on fluctuations in the international stock market and on foreign securities investment are proposed. Finally, the results of case studies using samples of national stock market indices indicate that different cultural dimensions have different influences on the volatility of national stock markets. The smaller the cultural distance between countries, the more similar the level of volatility in those countries' stock markets. Greater cultural similarity promotes increased securities investment between countries.     

Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models

We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.