Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns – except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.     

On the asymmetric impact of macro–variables on volatility

We extend the GARCH–MIDAS model to take into account possible different impacts from positive and negative macroeconomic variations on financial market volatility: a Monte Carlo simulation which shows good properties of the estimator with realistic sample sizes. The empirical application is performed on the daily S&P500 volatility dynamics with the U.S. monthly industrial production and national activity index as additional (signed) determinants. We estimate the Relative Marginal Effect of macro variable movements on volatility at different lags. In the out-of-sample analysis, our proposed GARCH–MIDAS model not only statistically outperforms the competing specifications (GARCH, GJR-GARCH and GARCH–MIDAS models), but shows significant utility gains for a mean-variance investor under different risk aversion parameters. Attention to robustness is given by choosing different samples and estimating the model in an international context (six different stock markets).     

Malaysia-EU trade at the industry level: Is there an asymmetric response to exchange rate volatility?

Aftab et al. (Empirica 43:461–485, 2016) in this journal assessed the impact of exchange rate volatility on Malaysia-EU trade at commodity level using the linear ARDL approach of Pesaran et al. (J Appl Econom 16:289–326, 2001) and did not find significant effects in most of the 81 Malaysian exporting and 66 importing industries. In this paper, we argue for asymmetric effects of exchange rate volatility on the same industries’ trades which implies using Shin et al.’s (Festschrift in Honor of Peter Schmidt, Springer, New York, 2014) nonlinear ARDL approach. While we find short-run asymmetric effects of volatility in almost all industries, we find evidence of adjustment asymmetry in 17 exporting and nine importing industries. We also find significant impact or short-run cumulative asymmetry in 12 exporting and six importing industries. The most important finding is significant long-run asymmetric effects in 36 Malaysian exporting industries and 25 Malaysian importing industries. Clearly, trade flows react to an increased exchange rate volatility differently than to a decreased volatility.     

What drives idiosyncratic volatility over time?

We document the patterns of market-wide and firm-specific volatility in the Portuguese stock market over the 1991–2005 period and test several explanations for the behavior of firm-level idiosyncratic volatility. Unlike previous studies we find no evidence of a statistically significant rise in firm-specific volatility. On the contrary, the ratio of firm-specific risk to total risk slightly decreases. We show that this result stems from new listings of large privatized companies that display lower firm-specific risk. Our findings are consistent with the idea that changes in idiosyncratic volatility are related to changes in the composition of the market.

Pairs trading: does volatility timing matter?

Pairs trading is a dollar-neutral trading strategy. Using the components of two major stock indices, the S&P 500 and the Nikkei 225, this article deals with the performance of a pairs trading system based on various pairs selection methods (distance, stationarity, cointegration) over a 10-year period. On both markets, using a classical framework, cointegration appears superior and effective. On the U.S. market and also in Japan to a lower extent, pairs trading strategies exhibited an impressive performance during the 2008 financial crisis. Bearish periods are associated with a high level of the VIX index: the ‘investor fear gauge’. Using a modified trading system, this article examines the link between pairs trading performance and volatility/VIX timing. It is shown that for the best selection technique (cointegration), timing volatility has no economic value in a pairs trading context.     

What explains long memory in futures price volatility?

Long memory in futures price volatility is a well-documented stylized fact with implications for market efficiency, risk management, forecasting and option pricing bias. The implications of long-memory differ, however, based on whether it is of a ‘fractional’ or of a ‘stochastic’ type. The aims of this article are to determine, in the case of agricultural commodity futures data, which type better describes price volatility and also to evaluate several competing explanations for findings of long memory. The evidence presented here finds little support for three out of four potential explanations, namely, excessive noise in the volatility measure, bias in the long-memory estimator and understated SEs of the long-memory parameter. For the data considered, price volatility appears to be most likely generated by a nonfractional long-memory process such as a stochastic break or stochastic unit root.     

Does VIX or volume improve GARCH volatility forecasts?

This article considers whether the inclusion of two additional variables can improve volatility forecasts over a standard GARCH-based model. We consider three alternative ways of incorporating the volatility index (VIX) and trading volume as exogenous variables within a selection of GARCH models. We are particularly interested in whether these variables have additional incremental forecast power over and above the baseline GARCH specification. Our results suggest that both the VIX and volume do provide some additional forecast power, and this is generally improved when considering both of these series jointly in the model. However, while the results may be statistically significant the gain is marginal and the coefficient values small. Moreover, in a horse race exercise VIX does not outperform the GARCH approach. In answering the question of whether VIX produces better forecasts than the GARCH model, then the answer is no, but the informational content of VIX cannot be ignored and should be incorporated into forecast regressions.     

Long range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting

This study evaluates the sector risk of the Qatar Stock Exchange (QSE), a recently upgraded emerging stock market, using value-at-risk models for the 7 January 2007–18 October 2015 period. After providing evidence for true long memory in volatility using the log-likelihood profile test of Qu and splitting the sample and dth differentiation tests of Shimotsu, we compare the FIGARCH, HYGARCH and FIAPARCH models under normal, Student-t and skewed-t innovation distributions based on in and out-of-sample VaR forecasts. The empirical results show that the skewed Student-t FIGARCH model generates the most accurate prediction of one-day-VaR forecasts. The policy implications for portfolio managers are also discussed.     

On the asymmetric effects of exchange-rate volatility on trade flows: Evidence from US–UK Commodity Trade

We consider the response of each of the 67 industries that trade between the United States and United Kingdom to the volatility of the real dollar–pound exchange rate. When we follow previous research and estimate a linear ARDL model for each industry, we find short-run effects of volatility in 22 US exporting industries to the United Kingdom that last into the long run only in nine industries. As for the UK exports to the United States, we find short-run effects in 18 industries that last into the long run in 15 industries. However, when we estimate a nonlinear model for each industry, we find short-run effects of volatility on 41 US exporting industries and on 43 UK exporting industries, all in an asymmetric manner. Short-run asymmetric effects lasted into long-run asymmetric effects in 24 US exporting industries to the United Kingdom and in 33 UK exporting industries to the United States. While total trade shares of industries from the linear models were negligible, those of the industries from the nonlinear models were significant in size, in the tune of one-third of the trade.     

Are business cycles asymmetric? Some European evidence

Economic thought has often regarded business cycles as asymmetric. This study examines the existence of asymmetries over the business cycle in seven European countries: France, Germany, Ireland, Italy, Luxembourg, The Netherlands and the United Kingdom. To analyse this issue, industrial production in these countries from 1957 to 1998 is examined, and quarterly contractions and expansions in this variable are compared. The results obtained with both parametric and non-parametric methods allow the existence of asymmetries in these countries to be questioned.     

When does more aid imply less democracy? An empirical examination

Foreign aid flows have increased considerably during the last decades, targeting, apart from development objectives, goals related to democracy. In this paper we investigate whether aid has affected the political regime of recipient countries. To this end, we use annual data on Net Official Development Assistance covering 64 aid-recipients. Because of data limitations, we cover the 1967-2002 period. We find that aid flows decreased the likelihood of observing a democratic regime in a recipient country. This effect is sensitive to economic and social conditions. The negative relation between aid and democracy is moderated when aid flows are preceded by economic liberalization. Aid from the U.S. has a non-significant effect on the political regime of recipients.     

Do Aussie markets smile? Implied volatility functions and determinants

If options are correctly priced, the interpretation of volatility in the Black–Scholes model (as identifying the volatility of the underlying asset) is violated. The empirical relation between the model ‘implied volatility’ and the degree to which the option is in-the-money (moneyness) has been reported as resembling a U-shape (or ‘smile’) for options on currencies (and more of a ‘smirk’ for options on equities). In this article, using multivariate time-series analysis and employing an impulse response function, we investigate the structural relationships and dynamics of the volatility smile in relation to the option liquidity, key features of the underlying asset and market momentum. Our findings confirm evidence of a number of biases in the Black–Scholes model consistent with Chou et al. (2011) in regard to liquidity in both the underlying and the option itself, and with Peña et al. (1999) as to the importance of the option time to maturity. As well as delineating such biases as they co-relate both with each other and with the underlying asset volatility and momentum, we find that the pronounced smile is related to the differential sensitivities of in-the-money and out-of-the-money options, which itself suggests an explanation for the characteristic smile shape.     

Do dark pools amplify volatility in times of stress?

In this article, we explore the relationship between trading on dark pools and equity volatility in the context of the recent concerns about increase in frequency of market shocks combined with changes in market microstructure. In order to understand the potential role of dark pools in times of stress and implications for financial stability, it is essential to investigate the relationship between investor trading preferences and market volatility. For our analysis, we use data on daily trading volumes of FTSE100 stocks on dark and lit order books. We find evidence that dark pool trading has explanatory power in predicting volatility, implying that dark pools may affect the dynamics of price formation through liquidity. Our findings suggest that increased use of dark pools does not increase volatility, but may in fact lower it. Thus, dark pools may not be significantly detrimental to market stability in times of stress. This highlights the need for further analysis of the effects that shifting financial market structure might have on financial stability.     

Do the central bank actions reduce interest rate volatility?

This paper investigates how Central Bank of Brazil (CBB) actions influence market uncertainty. We consider two kinds of actions: the monetary policy decision about the interest rate target and the pure communication event of this decision published one week later. Unlike related papers, we measure the market uncertainty by the implied volatility extracted from interest rate options. Implied volatility is more suitable than physical volatility to assess economic effects since it encompass market beliefs adjusted by risk. We use an event study approach to evaluate the impact of CBB actions. The results show that both the decisions about the target rate and the communication event reduce the interest rate volatility.     

Why does bad news increase volatility and decrease leverage?

A recent literature shows how an increase in volatility reduces leverage. However, in order to explain pro-cyclical leverage it assumes that bad news increases volatility, that is, it assumes an inverse relationship between first and second moments of asset returns. This paper suggests a reason why bad news is more often than not associated with higher future volatility. We show that, in a model with endogenous leverage and heterogeneous beliefs, agents have the incentive to invest mostly in technologies that become more volatile in bad times. Agents choose these technologies because they can be leveraged more during normal times. Together with the existing literature this explains pro-cyclical leverage. The result also gives a rationale to the pattern of volatility smiles observed in stock options since 1987. Finally, the paper presents for the first time a dynamic model in which an asset is endogenously traded simultaneously at different margin requirements in equilibrium.     

Long-run growth and volatility: which source really matters?

The aim of the article is to analyse the relationship between long-run growth and business cycle volatility. In particular, the main purpose of this article is to identify which source of volatility is most detrimental to growth. Using cross-country data from 1970 to 2000, and several indicators of volatility (such as inflation, exchange rate, government expenditure, output and investment volatility) this article shows that although, all these measures of volatility are remarkably harmful for growth, business cycle investment volatility is the main source that hampers long-run growth. This relation is robust to different measures of business cycle, and to different sub-samples of countries.     

Are adjustments in the default risk premium asymmetric?

This article employs threshold cointegration and error-correction models to the default risk premium. The approach allows asymmetry in the dynamic process that has not been captured in previous studies of corporate credit spreads. The results indicate that the adjustment process is asymmetric and would be beneficial to investors and macroeconomic forecasters as the default risk premium may signal future business cycles.