Stavros Stavroyiannis

Herding, anti-herding behaviour in metal commodities futures: a novel portfolio-based approach

The purpose of this article is twofold. Motivated by the heated debate on the financialization of commodities, we examine the existence of herding behaviour in metal commodities futures. In order to identify any time-dependent properties reflected in time-varying parameters, we employ the overlapping rolling window regression technique. The empirical evidence confirms a time-varying anti-herding behaviour before the global financial crisis and the absence of herding or anti-herding behaviour during the crisis. Next we attempt to formally establish the link between the documented anti-herding behaviour and portfolio management with the use of dynamic conditional correlations via the DCC-GARCH family multivariate modelling. After specifying the correlations, an in-sample recursive dynamic Markowitz portfolio is constructed and monitored. By doing so, we attribute the anti-herding behaviour to different portfolio positioning and rebalancing. On the other hand, in the absence of herding or anti-herding behaviour, we document a shift in the correlations and covariances of the commodity futures especially during the crisis, resulting in a decrease of the portfolio weights together with a substantial cash flow towards the risk-free asset.

Value-at-risk for the long and short trading position with the Pearson type-IV distribution

We examine the value-at-risk where the volatility and returns are modelled via a typical GARCH(1,1) model and the innovations process is the Pearson type-IV distribution. As case studies, we examine the NASDAQ and FTSE100 indices from 12-Dec-1984 to 21-Dec-2000. The model is fitted to the data via maximisation of the logarithm of the maximum likelihood estimator. In sample backtesting is performed by the success-failure ratio, the Kupiec p-test, the Christoffersen tests, the expected shortfall, and the DQ test of Engle and Manganelli. The results indicate that the Pearson type-IV distribution gives better results compared with the skewed student distribution.

Dynamic Properties of the Bitcoin and the US Market

This paper examines the dynamic properties of Bitcoin and the Standard and Poor’s SP500 index, using a variety of econometric approaches, including univariate and multivariate GARCH models, and vector autoregressive specifications. Moreover, we explore whether Bitcoin can be classified as a possible hedge, diversifier, or safehaven with respect to the US market, and if it possesses any of the attributes Gold has. Our results indicate that Bitcoin does not actually hold any of the hedge, diversifier, or safe-haven properties; rather, it exhibits intrinsic attributes not related to US market developments.

On the multifractal properties and the local multifractality sensitivity index of euro to Japanese yen foreign currency exchange rates

We examine the high frequency dynamics of euro to Japanese yen foreign currency exchange rates for the period of January 2001 to January 2010, comprising approximately three million data entries. The probability density function is described competently by the Tsallis q-Gaussian statistics, approaching the Gaussian distribution as the time lag increases. Multifractality has been studied using the original, shuffled and surrogate data series to investigate the origin. We then extend the local Hurst exponent idea taking in account the local multifractality, using a moving window of 10,000 data points. The square of the difference of the local multifractality ranges, defined as a sensitivity index, provides a signal before the last crash.

Herding, Faith-Based Investments and the Global Financial Crisis: Empirical Evidence From Static and Dynamic Models

ABSTRACT The purpose for this article is to explore the existence of herding behavior in the context of Shariah-based ethical investments. To this end the authors have employed the highly liquid constituent stocks of the U.S. Dow Jones Islamic Index for the period January 2007 to December 2014. The methodology encompasses both static and dynamic models that capture potential time-varying patterns or asymmetric behavior of herding. Summarizing the results, the authors document significant antiherding behavior that is robust across different formulations and testing procedures. Most interestingly, they observe an asymmetric behavior of the antiherding phenomenon. Results from the dynamic analysis reveal that antiherding tends to be more intense during turbulent periods. The findings may entail useful implications for investors who wish to diversify their portfolios using faith-based investments.

On the generalised Pearson distribution for application in financial time series modelling

We elaborate on a new distributional scheme resulting from the generalised Pearson distribution with application to financial modelling. As case studies, we consider the major historical indices daily returns, DJIA, NASDAQ composite, FTSE100, CAC40, DAX and S%P500, as well as, high-frequency returns of the Euro/Japanese Yen foreign currency exchange rates. Using non-linear optimisation techniques, we compare the results of the maximum likelihood estimator of the new distribution to the results of the Pearson type-IV distribution. The main findings indicate that the new distribution improves the value of the estimator in all cases, with significant improvement below the 60-min sampling.

Value-at-risk and related measures for the Bitcoin

Purpose The purpose of this paper is to examine the value-at-risk and related measures for the Bitcoin and to compare the findings with Standard and Poor’s SP500 Index, and the gold spot price time series. Design/methodology/approach A GJR-GARCH model has been implemented, in which the residuals follow the standardized Pearson type-IV distribution. A large variety of value-at-risk measures and backtesting criteria are implemented. Findings Bitcoin is a highly volatile currency violating the value-at-risk measures more than the other assets. With respect to the Basel Committee on Banking Supervision Accords, a Bitcoin investor is subjected to higher capital requirements and capital allocation ratio. Practical implications The risk of an investor holding Bitcoins is measured and quantified via the regulatory framework practices. Originality/value This paper is the first comprehensive approach to the risk properties of Bitcoin.

Volatility Modeling and Risk Assessment of the Major Digital Currencies

The objective of this paper is to investigate the volatility dynamics of the six major digital currencies, Bitcoin, Ethereum, Ripple, Litecoin, Dash, and NEM, using a variety of GARCH models and skewed distributions. Selecting the best GARCH model and distribution fitting better each digital currency, we perform risk assessment via Value-at-Risk and Expected Shortfall. We find that different GARCH models and distributions fit better each case, and an investor in digital currencies is exposed to a higher risk compared to regular markets.

Value-at-Risk and Expected Shortfall for the major digital currencies

Digital currencies and cryptocurrencies have hesitantly started to penetrate the investors, and the next step will be the regulatory risk management framework. We examine the Value-at-Risk and Expected Shortfall properties for the major digital currencies, Bitcoin, Ethereum, Litecoin, and Ripple. The methodology used is GARCH modelling followed by Filtered Historical Simulation. We find that digital currencies are subject to a higher risk, therefore, to higher sufficient buffer and risk capital to cover potential losses.

Is the BRICS decoupling effect reversing? Evidence from dynamic models

The recent large drop of the crude oil price since the mid-2014 has created financial turbulence in the oil-based exporting emerging markets countries. The impact of this shock is examined for the BRICS markets using two approaches: 1) we study the BRICS as a group for any recent time varying herding or anti-herding behaviour using stochastic volatility models; 2) the bivariate properties of the group are examined via implementation of the multivariate GARCH methodology. Both approaches indicate a reversal of the behaviour; the statistically significant anti-herding behaviour is diminishing, and a rise of the dynamic conditional correlations is observed.