On the predictive power of ARJI volatility forecasts for Bitcoin

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.     

Pricing efficiency of Bitcoin Trusts

This article examines the pricing efficiency of Bitcoin Investment Trust. We investigate the deviation between prices and net asset values and find that there is a significant and persistent premium with an average of 44%. Such evidence points to pricing inefficiency of the currently available trust and encourages practitioners to introduce better instruments such as Exchange Traded Funds as alternatives to investors interested in having exposure to bitcoins and the digital currencies market.     

The day-of-the-week pattern of price clustering in Bitcoin

Following Urquhart (2017) who finds evidence of price clustering in Bitcoin, we answer the question of whether the documented price clustering in Bitcoin is driven by any given day-of-the-week. We find evidence that Bitcoin prices cluster around whole numbers more on Fridays and least on Mondays. We also show that Bitcoin price clustering around the top three most frequent two-digit decimals is primarily a Friday phenomenon.     

Nonparametric estimation of stochastic volatility models

This letter introduces nonparametric estimators of the drift and diffusion coefficient of stochastic volatility models which exploit techniques for estimating integrated volatility with high-frequency data. The performance of the proposed estimators is assessed on simulations of two popular stochastic volatility models.     

High-low range in GARCH models of stock return volatility

We suggest a simple and general way to improve the GARCH volatility models using the intraday range between the highest and the lowest price to proxy volatility. We illustrate the method by modifying a GARCH(1,1) model to a range-GARCH(1,1) model. Our empirical analysis conducted on stocks, stock indices and simulated data shows that the range-GARCH(1,1) model performs significantly better than the standard GARCH(1,1) model both in terms of in-sample fit and out-of-sample forecasting ability.     

Asymmetry and leverage in GARCH models: a News Impact Curve perspective

Models for conditional heteroskedasticity belonging to the GARCH class are now common tools in many economics and finance applications. Among the many possible competing univariate GARCH models, one of the most interesting groups allows for the presence of the so-called asymmetry or leverage effect. In our view, asymmetry and leverage are two distinct phenomena, both inspired by the seminal work of Black in 1976. We propose definitions of leverage and asymmetry that build on the News Impact Curve, allowing to easily and coherently verify if they are both present. We show that several GARCH models are asymmetric but none is allowing for a proper leverage effect. Finally, we extend the leverage definition to a local leverage effect and show that the AGARCH model is coherent with the presence of local leverage. An empirical analysis completes the paper.     

A new generalized volatility proxy via the stochastic volatility model

This article proposes power transformation of absolute returns as a new proxy of latent volatility in the stochastic model. We generalize absolute returns as a proxy for volatility in that we place no restriction on the power of absolute returns. An empirical investigation on the bias, mean square error and relative bias is carried out for the proposed proxy. Simulation results show that the new estimator exhibiting negligible bias appears to be more efficient than the unbiased estimator with high variance.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

The information content of implied volatility in developed versus developing FX markets

This article predicts the daily movement of monthly foreign exchange (FX) rate volatility using a linear combination of a time-series model and implied volatilities from options. The focus is on analysing the FX volatilities in three developing economies (the Brazilian real (BRL), the Indian rupee (INR) and the Russian ruble (RUB)) against the US dollar (USD). The empirical exercise utilizes two time-series models, mixed data sampling (MIDAS) and GARCH. The analysis indicates that for both developed and developing economies the predictive power of MIDAS and that of GARCH is comparable. Further on in this article, we will ascertain whether the relationship between realized and implied volatility is fundamentally different in the case of developing economies from that among developed economies. Thus, we compare the pairs USD/BRL, USD/INR and USD/RUB against EURO/USD and USD/Japanese yen to determine the information content and predictive power of implied volatilities. Plots of the MIDAS coefficients show that the volatility is more persistent in developing economies than in developed economies.     

Interest-rate volatility and volatility spillovers in emerging Europe

While many transition economies – particularly those that hope to join the Euro – have seen their economies converge to Europe’s, this process is by no means complete. Considerable macroeconomic volatility persists. This study examines the variability of the short-term nominal interest rates of ten transition economies, finding that eight of them exhibit time-varying volatility that can be modeled as a GARCH or Exponential GARCH process. Incorporating various measures of external volatility into the models, we find that those economies with fixed or managed exchange rates tend to experience more volatility spillovers, particularly from the Eurozone, regardless of the degree of transition. Only Estonia has a fixed exchange rate and remains free of international contagion.     

Measuring asymmetry and persistence in conditional volatility in real output: evidence from three East Asian tigers using a multivariate GARCH approach

We search for evidence of conditional volatility in the quarterly real Gross Domestic Product (GDP) growth rates of three East Asian tigers: Singapore, Hong Kong and Taiwan. The widely accepted Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH)-type model is used to capture the existence of asymmetric volatility and the potential structural break points in the volatility. We find evidence of asymmetry and persistence in the volatility of GDP growth rates. It is noted that the structural breakpoints of volatility correspond reasonably well to the historical economic and political events in these economies. Policy implications from our findings are discussed.     

Impacts of derivative markets on spot market volatility and their persistence

In this article, we investigate the impacts of futures and options markets on the volatility of the underlying market. Unlike earlier studies, the focus is on their persistence over time. Tests on the Hang Seng index yield several interesting results that often contrast with previous findings. Empirical results suggest that the quality of new information generated by derivative trading determines the impacts on the spot market volatility. The futures market provides new, material information reducing spot market volatility. The Options market, on the other hand, generates noisy information and distorts price, which is followed by an increase in volatility and a decrease in its sensitivity to price change. While the impact of futures persists, that of options mostly disappears as the market matures. Our conjecture is that the futures market is mainly driven by informed, experienced participants, while the options market attracts new, inexperienced investors.     

Inflation and inflation volatility in Thailand

Quarterly data for Thailand are used in this article for the period 1965q3–2013q4 to investigate both the relationship between inflation and inflation volatility, and the impact of inflation volatility on economic growth. Inflation volatility is estimated by deploying the generalized autoregressive conditional heteroscedastic (GARCH) technique. A Granger causality test is then conducted to examine the causality between inflation and inflation volatility. The empirical results obtained are consistent with a number of theoretical propositions. First, the results are consistent with the Friedman–Ball proposition, which states that a rise in inflation raises inflation volatility. Second, there is evidence supporting the Holland proposition that inflation volatility lowers the rate of inflation. This is consistent with the view that central banks attempt to stabilize inflation with the rise in inflation volatility. Third, empirical results obtained by asymmetric GARCH models suggest that inflation shocks have an asymmetric impact on inflation volatility (i.e. a positive inflation shock has a larger impact on inflation volatility – as measured by the logarithm of the conditional variance of inflation – than a negative inflation shock). Fourth, inflation volatility has an adverse impact on economic growth. Finally, given the fixed/pegged or managed float exchange rate system, US inflation has been found to have a positive impact on inflation and its volatility in Thailand. This article discusses the implications of empirical findings on the design and enactment of monetary policy for price stability in Thailand.     

Revisiting the risk-return relation in the Chinese stock market: Decomposition of risk premium and volatility feedback effect

The empirical results of the risk-return relationship are mixed for both mature and merging markets. In this paper, we develop a new volatility model to revisit the risk-return relation of the aggregate stock market index by extending the Realized GARCH model of Hansen et al. (2012) with the Wang and Yang (2013) framework, in which the overall risk-return relation is decomposed into a risk premium and a volatility feedback effect. An empirical analysis of three major Chinese stock indices reveals positive risk premium and negative volatility feedback effect, and those findings are stable across different markets and sub-samples. However, their relative magnitudes differ between markets and varies through time.     

Long memory in the volatility of China stock returns

In this paper, we empirically examine the volatility process of China's stock market returns using daily and weekly Shanghai and Shenzhen stock indices during January 1990 to August 2008. To investigate the property of the process, we used the FIGARCH (fractionally integrated GARCH) model including GARCH and IGARCH processes as special cases. Since the FIGARCH model allows fractional integration order, it can detect hyperbolically decaying volatility processes which cannot be explained by previous models with integer integration order. Our results show that the Shanghai and Shenzhen stock indices exhibit long-term dependencies. The long memory properties of the Shanghai and Shenzhen stock markets do not seem to be spuriously induced without exception.     

Exchange rate volatility and US commodity trade with the rest of the world

Exchange rate volatility is argued to affect the trade flows negatively and positively. Indeed, empirical studies that have addressed the issue have supported both effects. These studies have used aggregate trade flows data either between one country and the rest of the world or between two countries at the bilateral level. Studies that have disaggregated trade data by industry are rare. Thus, we extend the literature by looking at the experiences of 66 American industries that trade with the rest of the world using monthly data. In most cases, trade flows are not affected by GARCH‐based volatility of the real effective exchange rate of the dollar.     

基于GARCH族模型对中国股市波动的分析与预测

运用GARCH类模型对沪深300指数序列的波动性、收益率进行了实证研究,并且对序列做了拟合与预测,获得了不错的效果。除此,还证实了中国股市存在着显著的非对称效应。     

Altered bank and exchange volatility

This paper examines the behaviour of the Czech crown's exchange rate when pegged to a currency basket. The peg is supposed to limit the overall instability of the currency. The GARCH(1,1) model with a dummy variable for the volatility response is used to account for a change in the width of the fluctuation band. The results of this paper show that volatility of the exchange rate decreased after a much wider fluctuation band was introduced to limit movements of the currency basket index.     

Linear and non-linear transmission of equity return volatility: evidence from the US, Japan and Australia

This paper models the transmission of shocks between the US, Japanese and Australian equity markets. Tests for the existence of linear and non-linear transmission of volatility across the markets are performed using parametric and non-parametric techniques. In particular the size and sign of return innovations are important factors in determining the degree of spillovers in volatility. It is found that a multivariate asymmetric GARCH formulation can explain almost all of the non-linear causality between markets. These results have important implications for the construction of models and forecasts of international equity returns.     

A note on the estimated GARCH coefficients from the S&P1500 universe

ABSTRACT

We employ 1440 stocks listed in the S&P Composite 1500 Index of the NYSE. Three benchmark GARCH models are estimated for the returns of each individual stock under three alternative distributions (Normal, t and GED). We provide summary statistics for all the GARCH coefficients derived from 11,520 regressions. The EGARCH model with GED errors emerges as the preferred choice for the individual stocks in the S&P 1500 universe when non-negativity and stationarity constraints in the conditional variance are imposed. 57% of the constraint’s violations are taking place in the S&P small cap stocks.