Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis

The Asia-Pacific region remains understudied regarding return connectedness among exchange rate markets. Furthermore, previous studies mostly use average-based estimators to measure return connectedness, even though they are not necessarily suitable to quantify connectedness measures during extreme events, i.e. in the tails of the conditional distribution. The aim of this paper is to study the dynamic connectedness among 11 Asia-Pacific exchange rates from September 1994 to August 2019, not only on average but also in the left and right tails. Using mean-based measures of connectedness, the level of return connectedness is on average 32%, indicating a moderate level of connectedness across the currencies under study. However, using a novel quantile-based connectedness framework, the level of connectedness is found to be much higher, reaching around 85% at both the left and right tails of the conditional distribution. This result shows that return connectedness across the 11 exchange rates is much stronger during extreme negative and extreme positive events, suggesting that the application of mean-based models of connectedness in the Asia-Pacific exchange rate markets is too restrictive and inadequate. Furthermore, return connectedness measures are time-varying in all cases, but are less volatile in the tails. A detailed analysis involving the relative tail dependence shows that an asymmetric behaviour is present, indicating that return spillovers differ between periods of extreme market depreciations and periods of extreme market appreciations.     

Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index

We examine the quantile connectedness of returns between the recently developed S&P 500 Twitter Sentiment Index and various asset classes. Rather than a mean-based connectedness measure, we apply quantile-connectedness to explore connectedness of means and, especially, extreme left and right tails of distributions. Using mean-based connectedness measures, the level of return connectedness between the twitter sentiment index and all financial markets is a modest 46%. However, when applying a novel quantile-based connectedness approach, we find that levels of tail-connectedness are much stronger, up to 82%, at extreme upper and lower tails. This suggests that the impact of sentiment on financial markets is much stronger during extreme positive/negative sentiment shocks. Moreover, return connectedness measures are less volatile during extreme events. Net connectedness analysis shows that the Twitter sentiment index acts as a net transmitter of return spillovers, highlighting the leading role of investor sentiment on predicting other financial markets.     

Extreme quantile spillovers and drivers among clean energy,electricity and energy metals markets

This paper examines return and volatility spillover effects among the clean energy (electric vehicles, solar and wind), electricity and 8 energy metals (silver, tin, nickel, cobalt, lead, zinc, aluminum and copper) markets and their drivers under the conditions of the mean and extreme quantiles. The results show moderate spillovers among the clean energy, electricity and energy metals markets, and greater connectivity among the three markets under extreme quantile conditions. Among them, the clean energy markets always play the role of the transmitter, and the electricity market always plays the role of the receiver of spillover effects. In addition, the return and volatility spillovers among the three markets have remarkable time-varying features, and they increase dramatically when extreme events occur, especially under extreme quantile conditions. Finally, we reveal the drivers of return and volatility spillovers among these markets by the OLS and quantile regression methods. The COVID-19 and the Arca Tech 100 (PSE) index are found to be important drivers.     

Correlation and return dispersion dynamics in Chinese markets

This paper studies the relation between firm-level return dispersions and correlations among Chinese stocks during periods of unusually large upward and downward swings. We analyze individual stock returns across 18 sectors and test if return dispersions and stock correlations show asymmetric patterns for extreme up and down markets. Evidence from studies on U.S. stocks suggests that equity return correlations tend to be much greater on the downside than on the upside and that the degree of comovement gets even stronger during extreme market states. However, in the case of Chinese stock market, we find that higher downside correlations apply to only stocks within the Financial sector. With the exception of Financial stocks, we find that stock correlations are significantly higher during up markets, rather than down markets. Regarding firm-level return dispersions, our findings are consistent with rational asset pricing model predictions. We find that equity return dispersions are significantly higher during periods of large price changes.     

A New Approach to Measuring Financial Contagion

This article proposes a new approach to evaluate contagion infinancial markets. Our measure of contagion captures the coincidenceof extreme return shocks across countries within a region andacross regions. We characterize the extent of contagion, itseconomic significance, and its determinants using a multinomiallogistic regression model. Applying our approach to daily returnsof emerging markets during the 1990s, we find that contagionis predictable and depends on regional interest rates, exchangerate changes, and conditional stock return volatility. Evidencethat contagion is stronger for extreme negative returns thanfor extreme positive returns is mixed.     

Extreme observations and risk assessment in the equity markets of MENA region: Tail measures and Value-at-Risk

The standard “delta-normal” Value-at-Risk methodology requires that the underlying returns generating distribution for the security in question is normally distributed, with moments which can be estimated using historical data and are time-invariant. However, the stylized fact that returns are fat-tailed is likely to lead to under-prediction of both the size of extreme market movements and the frequency with which they occur. In this paper, we use the extreme value theory to analyze four emerging markets belonging to the MENA region (Egypt, Jordan, Morocco, and Turkey). We focus on the tails of the unconditional distribution of returns in each market and provide estimates of their tail index behavior. In the process, we find that the returns have significantly fatter tails than the normal distribution and therefore introduce the extreme value theory. We then estimate the maximum daily loss by computing the Value-at-Risk (VaR) in each market. Consistent with the results from other developing countries [see Gencay, R. and Selcuk, F., (2004). Extreme value theory and Value-at-Risk: relative performance in emerging markets. International Journal of Forecasting, 20, 287–303; Mendes, B., (2000). Computing robust risk measures in emerging equity markets using extreme value theory. Emerging Markets Quarterly, 4, 25–41; Silva, A. and Mendes, B., (2003). Value-at-Risk and extreme returns in Asian stock markets. International Journal of Business, 8, 17–40], generally, we find that the VaR estimates based on the tail index are higher than those based on a normal distribution for all markets, and therefore a proper risk assessment should not neglect the tail behavior in these markets, since that may lead to an improper evaluation of market risk. Our results should be useful to investors, bankers, and fund managers, whose success depends on the ability to forecast stock price movements in these markets and therefore build their portfolios based on these forecasts.     

Extreme dependence with asymmetric thresholds: Evidence for the European Monetary Union

Existing papers on extreme dependence use symmetrical thresholds to define simultaneous stock market booms or crashes such as the joint occurrence of the upper or lower one percent return quantile in both stock markets. We show that the probability of the joint occurrence of extreme stock returns may be higher for asymmetric thresholds than for symmetric thresholds. We propose a non-parametric measure of extreme dependence which allows capturing extreme events for different thresholds and can be used to compute different types of extreme dependence. We find that extreme dependence among the stock markets of ten initial EMU member countries, the United Kingdom, and the United States is largely asymmetrical in the pre-EMU period (1989–1998) and largely symmetrical in the EMU period (1999–2010). Our findings suggest that ignoring the possibility of asymmetric extreme dependence may lead to an underestimation of the probability of co-booms and co-crashes.     

Extreme Correlation of International Equity Markets

Testing the hypothesis that international equity market correlation increases in volatile times is a difficult exercise and misleading results have often been reported in the past because of a spurious relationship between correlation and volatility. Using "extreme value theory" to model the multivariate distribution tails, we derive the distribution of extreme correlation for a wide class of return distributions. Empirically, we reject the null hypothesis of multivariate normality for the negative tail, but not for the positive tail. We also find that correlation is not related to market volatility per se but to the market trend. Correlation increases in bear markets, but not in bull markets.     

How long memory in volatility affects true dependence structure

Long memory in volatility is a stylized fact found in most financial return series. This paper empirically investigates the extent to which interdependence in emerging markets may be driven by conditional short and long range dependence in volatility. We fit copulas to pairs of raw and filtered returns, analyse the observed changes in the dependence structure may be driven by volatility, and discuss whether or not asymmetries on propagation of crisis may be interpreted as intrinsic characteristics of the markets. We also use the findings to construct portfolios possessing desirable expected behavior such as dependence at extreme positive levels.     

Liquidity and stock returns: Evidence from a pure order-driven market using a new liquidity proxy

Liquidity has been found to be a determinant of stock returns in large hybrid quote-driven markets. Liquidity proxies have ranged from trade-based measures such as turnover rate to order-based measures such as the bid-ask spread. The relationship between return and liquidity in small pure order-driven markets is less clear, with different liquidity proxies yielding conflicting results. This study adds to the existing literature by considering the return-liquidity relationship on the Australian Stock Exchange, a small pure order-driven market, using a new liquidity measure, Weighted Order Value (WOV). Liquidity is found to be an important determinant of returns.     

Extreme return–volume dependence in East-Asian stock markets: A copula approach

A copula approach is used to examine the extreme return–volume relationship in six emerging East-Asian equity markets. The empirical results indicate that there is significant and asymmetric return–volume dependence at extremes for these markets. In particular, extremely high returns (large gains) tend to be associated with extremely large trading volumes, but extremely low returns (big losses) tend not to be related to either large or small volumes.     

典型事实、极值理论与金融市场动态风险测度研究

本文在金融市场典型事实约束下,运用ARFIMA模型对金融市场条件收益率建模,运用GARCH、GJR、FIGARCH、APARCH、FIAPARCH等5种模型对金融波动率进行建模,进而运用极值理论(EVT)对标准收益的极端尾部风险建模来测度各股市的动态风险,并用返回测试(Back-testing)方法检验模型的适应性。实证结果表明,总的来说,FIAPARCH-EVT模型对各个市场具有较强的适应性,风险测度能力较为优越。进一步,本文在ARFIMA-FIAPARCH模型下,假定标准收益分别服从正态分布(N)、学生t分布(st)、有偏学生t分布(skst)、广义误差分布(GED)共4种分布,对各股市的动态风险测度的准确性进行检验,并和EVT方法的测度结果进行对比分析。结果表明,EVT方法风险测度能力优于其他方法,有偏学生t分布假设下的风险测度模型虽然略逊于EVT方法,但也不失为一种较好的方法;ARFIMA-FI-APARCH-EVT不仅在中国大陆沪深股市表现最为可靠,而且在其他市场也表现出同样的可靠性。     

Return and volatility co-movement in commodity futures markets: the effects of liquidity risk

Commodity markets are a widely researched topic in the field of finance. In this paper, we investigate the co-movement of return and volatility measures in different commodity futures markets and how these measures are affected by liquidity risk. First, we find that commodity returns display co-movement and that liquidity risk plays a key role in shaping asset return patterns. Moreover, we show that the volatilities of commodity returns co-move, and we demonstrate the role of liquidity risk in this joint pattern. We also find that the commodity markets we investigated share a common volatility factor that determines their joint volatility co-movement. Because liquidity risk affects both commodity returns and volatility shocks, it might be interpreted as the common causal factor driving both measures simultaneously. Therefore, we affirm the view that liquidity shocks are firmly related to two residual risks originating from both market return and market volatility. Finally, we also show that liquidity spillovers can significantly drive cross-sectional correlation dynamics.     

On the topology of cryptocurrency markets

Cryptocurrency markets are characterised by high volatility, high returns and comparative immaturity relative to equity and commodity markets. Topological Data Analysis (TDA) persistence norms are effective tools for the analysis of noisy dynamical systems like the cryptocurrency markets. We show how information from the shape of daily return data adds additional inference on activity within the cryptocurrency markets. TDA persistence norms embed volatility and connectedness between coins as well as incorporating information from uncertainty indexes, financial market performance and commodity returns. Our TDA measures are robust to noise and are consistent across a raft of alternative coin selections. Further, we exposit how persistence norms peak to forewarn of crashes and stay low as markets face exogenous shocks. We demonstrate the clear advantages of TDA for the study of cryptocurrency markets and develop the next steps for exploiting the potential of TDA for application to cryptocurrency markets.     

Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market

We study the economic and non-economic sources of stock return comovements of the emerging Indian equity market and the developed equity markets of the US, UK, Germany, France, Canada and Japan. Our findings show that the probability of extreme comovements in the economic contraction regime is relatively higher than in the economic expansion regime. We show that international interest rates, inflation uncertainty and dividend yields are the main drivers of the asymmetric return comovements. Findings reported in the paper imply that the impact of interest rates and inflation on return comovements could be used for anticipating financial contagion and/or spillover effects. This is particularly critical since during extreme market conditions, the tail return comovements can potentially reveal critical information for active portfolio management.     

ANALYSIS OF AN OPTION MARKET DYNAMICS BASED ON A HETEROGENEOUS AGENT MODEL

We propose a two‐market model in which an option market and its underlying market interact. Many artificial markets representing stock markets have been developed, and these models have been actively used to investigate the effects of market rules. However, no artificial market model for derivatives has been intensively studied, even though derivative markets are increasingly important. We tested stylized facts that can be observed in an option market and our model can replicate fat‐tailed distributions, positive skew of the return and positive autocorrelation of the square of return of implied volatility. We found that the speed of volatility mean reversion for fundamentalists and the existence of chartists are important factors for replicating the positive skew of an option market. The value of fat‐tailed distributions and positive skewness of the return get closer to the real value by coupling an option market and an underlying market. Copyright © 2014 John Wiley & Sons, Ltd.     

Contrarian Investing in a Small Capitalization Market: Evidence from New Zealand

This paper investigates the performance of accounting–based contrarian investment strategies in the New Zealand market. The return patterns of these strategies are then related to risk–based and behavioral–based explanations of the contrarian anomaly. Based on our analysis of the risk–return characteristics of the various strategies, we attribute the first year underperformance and second year outperformance of the value portfolios to expectational errors caused by noise trading in the relatively illiquid New Zealand market. The longer two–year correction process is in contrast to the much larger and more developed U.S. and Japanese markets, where value stock price corrections have been found to occur more rapidly. This provides support for the conjecture that longer horizons are required for value strategies to pay off in imperfectly competitive markets than in competitive markets.     

Conditional VaR using EVT - Towards a planned margin scheme

This paper constructs a robust Value-at-Risk (VaR) measure for the Indian stock markets by combining two well-known facts about equity return time series — dynamic volatility resulting in the well-recognized phenomenon of volatility clustering, and non-normality giving rise to fat tails of the return distribution. While the phenomenon of volatility dynamics has been extensively studied using GARCH model and its many relatives, the application of Extreme Value Theory (EVT) is relatively recent in tracking extreme losses in the study of risk measurement. There are recent applications of Extreme Value Theory to estimate the unexpected losses due to extreme events and hence modify the current methodology of VaR. Extreme value theory (EVT) has been used to analyze financial data showing clear non-normal behavior. We combine the two methodologies to come up with a robust model with much enhanced predictive abilities. A robust model would obviate the need for imposing special ad hoc margins by the regulator in times of extreme volatility. A rule based margin system would increase efficiency of the price discovery process and also the market integrity with the regulator no longer seen as managing volatility.     

Fair value accounting and the present value fallacy: The need for an alternative conceptual framework

The major functions of company accounting identified by the IASB and the FASB are (1) reporting on ‘the custody and safekeeping’ of the company's resources and (2) reporting on ‘their efficient and profitable use’. The joint IASB/FASB project for improving the conceptual framework for financial reporting is directed towards better performance of both functions within the conventional ‘accrual’ system of accounting through the use of ‘fair value’. Although the disclosure of fair values is a development to be welcomed, the requirement that changes in fair value should be reported as ‘gains’ or ‘losses’ appears to rely on the ‘Hicksian’ concept of income as a theoretical ideal.The object of the present paper is to establish that this concept is fundamentally flawed by what may be called the ‘present value fallacy’. Even in an economic utopia of perfectly competitive markets (with no discrepancies between objective market values and subjective present values), the concept of income or profit as value growth can be seriously misleading.If the prevailing Hicksian conceptual framework is discarded in favour of an alternative based on Fisher's theory of income, the two major, but incompatible, functions of financial reporting can be carried out independently and without compromise. The conventional ‘hybrid’ system of accrual accounting, in which backward-looking measures of volume and forward-looking measures of value are mixed together, would be replaced by a ‘segregated’ system in which they are kept strictly apart. A logical extension of Fisher's theory suggests the disclosure by agent/managers of the return on investment that they are planning to deliver to their principal/owners. This type of ‘decision-useful information’ is vital for the efficient operation of capital markets and for removing the accounting incentive to short-termism.     

Adverse Selection and the Required Return

An important feature of financial markets is that securitiesare traded repeatedly by asymmetrically informed investors.We study how current and future adverse selection affect therequired return. We find that the bid-ask spread generated byadverse selection is not a cost, on average, for agents whotrade, and hence the bid-ask spread does not directly influencethe required return. Adverse selection contributes to trading-decisiondistortions, however, implying allocation costs, which affectthe required return. We explicitly derive the effect of adverseselection on required returns, and show how our result differsfrom models that consider the bid-ask spread to be an exogenouscost.