Forecasting cryptocurrency volatility

This paper studies the behavior of cryptocurrencies’ financial time series, of which Bitcoin is the most prominent example. The dynamics of these series are quite complex, displaying extreme observations, asymmetries, and several nonlinear characteristics that are difficult to model and forecast. We develop a new dynamic model that is able to account for long memory and asymmetries in the volatility process, as well as for the presence of time-varying skewness and kurtosis. The empirical application, carried out on 606 cryptocurrencies, indicates that a robust filter for the volatility of cryptocurrencies is strongly required. Forecasting results show that the inclusion of time-varying skewness systematically improves volatility, density, and quantile predictions at different horizons.     

Stock overreaction to extreme market events

The paper investigates the behavior of individual US stocks during the 21 trading days following the event of extreme movement in the market index on a day. We find that stocks tend to overreact after both positive and negative events, but in a more pronounced way in the latter case. This behavior is more intense when the market exhibits clustered extreme swings, indicating that the overreaction and market volatility are related. We also identify that the overreaction is driven by the performance of loser stocks that revert more strongly, even as they exhibit a lower market beta than winners.     

Granger causality in risk and detection of extreme risk spillover between financial markets

Controlling and monitoring extreme downside market risk are important for financial risk management and portfolio/investment diversification. In this paper, we introduce a new concept of Granger causality in risk and propose a class of kernel-based tests to detect extreme downside risk spillover between financial markets, where risk is measured by the left tail of the distribution or equivalently by the Value at Risk (VaR). The proposed tests have a convenient asymptotic standard normal distribution under the null hypothesis of no Granger causality in risk. They check a large number of lags and thus can detect risk spillover that occurs with a time lag or that has weak spillover at each lag but carries over a very long distributional lag. Usually, tests using a large number of lags may have low power against alternatives of practical importance, due to the loss of a large number of degrees of freedom. Such power loss is fortunately alleviated for our tests because our kernel approach naturally discounts higher order lags, which is consistent with the stylized fact that today’s financial markets are often more influenced by the recent events than the remote past events. A simulation study shows that the proposed tests have reasonable size and power against a variety of empirically plausible alternatives in finite samples, including the spillover from the dynamics in mean, variance, skewness and kurtosis respectively. In particular, nonuniform weighting delivers better power than uniform weighting and a Granger-type regression procedure. The proposed tests are useful in investigating large comovements between financial markets such as financial contagions. An application to the Eurodollar and Japanese Yen highlights the merits of our approach.     

Multiscale features of extreme risk spillover networks among global stock markets

This paper studies the multiscale features of extreme risk spillover among global stock markets over various time–frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-error-variance decompositions. We further construct multiscale risk spillover networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.     

Intraday return predictability in the cryptocurrency markets: Momentum,reversal, or both

This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market. Using high-frequency price data on Bitcoin from March 3, 2013, to May 31, 2020, it shows that the patterns of intraday return predictability change in the presence of large intraday price jumps, FOMC announcement release, liquidity levels, and the outbreak of the COVID-19. Intraday return predictability is also found in other actively traded cryptocurrencies such as Ethereum, Litecoin, and Ripple. Further analysis shows that the timing strategy based on the intraday predictors produces higher economic value than the benchmark strategy such as the always-long or the buy-and-hold. Evidence of intraday momentum can be explained in light of the theory of late-informed investors, whereas evidence of intraday reversal, which is unique to the cryptocurrency market, can be related to investors’ overreaction to non-fundamental information and overconfidence bias.     

“Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet

The cryptocurrencies with small market capitalization are often overlooked despite they can potentially be the source of shocks to other cryptocurrencies in the market. To address this caveat, this paper attempts to investigate the spillover effects among 14 cryptocurrencies by employing transfer entropy. Our results suggest that among different types of cryptos, Bitcoin is still the most appropriate instrument for hedging, while Tether (USDT) which have a strong anchor with the US dollar is significantly volatile. Interestingly, we document that the small coins are more likely to be shock creators in the cryptocurrency market. Using the same approach, we further explored the link between gold prices and cryptocurrency prices. The results show that gold could be a good hedging instrument for cryptocurrencies due to its independence. In light of empirical results, it is advisable to carefully consider the coins with small capitalization. Further, investors should conduct portfolio rebalancing by including gold to hedge against the unexpected movement in the cryptocurrency market. Our paper not only contributes in terms of the application of advanced empirical methodology but also provides evidence on idiosyncratic features of the cryptocurrency market.     

Explaining the Czech interbank market risk premium

This paper focuses on the development of the interbank market risk premium in the Czech Republic during the global financial crisis. We explain the significant departure of interbank interest rates from the key monetary policy rate by a combination of different factors, including liquidity risk, counterparty risk, foreign influence, interbank relations, and strategic behavior. The results suggest a relevant role of market factors and some importance of counterparty risk.     

Why cryptocurrency markets are inefficient: The impact of liquidity and volatility

In this research, we study the multifractality, long-memory process, and efficiency hypothesis of six major cryptocurrencies (Bitcoin, Ethereum, Monero, Dash, Litecoin, and Ripple) using the time-rolling MF-DFA approach. For an in-depth analysis, this study uses the quantile regression approach to examine the determinants of efficient markets. The results show that all markets present evidence of long-memory property and multifractality. Furthermore, the inefficiency of cryptocurrency markets is time-varying, and Dash is the least inefficient market while Litecoin is the most inefficient. Finally, we find that higher liquidity improves but higher volatility weakens the efficiency of cryptocurrencies, depending on the quantiles. Therefore, we conclude that high liquidity with low volatility helps active traders to arbitrage away opportunities, resulting in market efficiency.     

Liquidity skewness premium

Risk-averse investors may dislike decrease of liquidity rather than increase of liquidity, and thus there can be asymmetric preference in variation of liquidity. In addition, investors are likely to avoid extreme illiquidity. This paper examines whether the skewness of an individual firm’s liquidity capturing asymmetric distribution of liquidity and extreme illiquidity is priced in the US stock market. Using the skewness of the daily price impact, we find that it is positively priced, and this positive relation is significant up to eight months after controlling for other effects. Moreover, we find our results remain significant with the skewness of alternative liquidity measures, i.e., dollar-volume, and turnover.     

Exploring the dynamic spillover of cryptocurrency environmental attention across the commodities,green bonds,and environment-related stocks

We analyze the dynamic spillover impact of cryptocurrency environmental attention (ICEA) on three asset classes: commodities, green bonds (GBs), and environment-related stocks. Our wavelet-based analysis suggests that ICEA is sharply escalated after the first quarter of 2021. During this period of intense attention, only the soybean commodity and Solactive GB tend to move positively and negatively with ICEA, respectively. Accordingly, the clean energy, sustainability, and Environmental, Social, and Governance (ESG) stock indices are positively associated with ICEA during 2018–2019 at the medium frequency bands. In most periods and frequency domains, most commodities, GBs, and environment-related stocks are not strongly linked to ICEA. Moreover, Diebold and Yilmaz’s (2014) spillover estimations signify no strong spillover effect of ICEA on the asset classes considered in this study. These findings are further corroborated by the wavelet-based Granger causality analysis. Moreover, our quantile regression (QR) estimations suggest that most assets are adversely influenced by ICEA, depending on the market conditions. Our research conveys some novel and vital policy ramifications to both investors and policymakers.     

基于CAPM的黄金风险溢价研究

运用CAPM理论中的边际风险价格的概念,通过分析一个包含了黄金市场和股票市场在内的市场资产组合,定量给出了黄金的风险溢价。同时检验了黄金收益是否在CAPM框架内有效。在与我国股市进行比较之后,得出投资者可将黄金包括到投资组合中去,以取得更好的风险收益比。     

Ross risk vulnerability for introductions and changes in background risk

We present two theorems that provide necessary and sufficient conditions for an expected utility maximizer to become more risk averse in the sense of Ross with respect to bearing a foreground risk after the introduction of any independent fair or unfair additive background risk. We call these decision makers Ross risk vulnerable, and show that Ross decreasing absolute risk aversion and Ross decreasing absolute prudence are jointly sufficient for Ross risk vulnerability. Restrictions on utility necessary and sufficient for Ross risk vulnerability with respect to stochastic dominance deteriorations of an existing background risk are also presented. Our analysis concludes with applications of Ross risk vulnerability.     

Comparative Ross risk aversion in the presence of mean dependent risks

This paper studies comparative risk aversion between risk averse agents in the presence of a background risk. Our contribution differs from most of the literature in two respects. First, background risk does not need to be additive or multiplicative. Second, the two risks are not necessarily mean independent, and may be conditional expectation increasing or decreasing. We show that our order of cross Ross risk aversion is equivalent to the order of partial risk premium, while our index of decreasing cross Ross risk aversion is equivalent to decreasing partial risk premium. These results generalize the comparative risk aversion model developed by Ross for mean independent risks. Our theoretical results are related to utility functions having the n-switch independence property.     

Forecasting extreme financial risk: A score-driven approach

This paper develops a new class of dynamic models for forecasting extreme financial risk. This class of models is driven by the score of the conditional distribution with respect to both the duration between extreme events and the magnitude of these events. It is shown that the models are a feasible method for modeling the time-varying arrival intensity and magnitude of extreme events. It is also demonstrated how exogenous variables such as realized measures of volatility can easily be incorporated. An empirical analysis based on a set of major equity indices shows that both the arrival intensity and the size of extreme events vary greatly during times of market turmoil. The proposed framework performs well relative to competing approaches in forecasting extreme tail risk measures.     

The uncertainty in extreme risk forecasts from covariate-augmented volatility models

For a GARCH-type volatility model with covariates, we derive asymptotically valid forecast intervals for risk measures, such as the Value-at-Risk or Expected Shortfall. To forecast these, we use estimators from extreme value theory. In the volatility model, we allow for leverage effects and the inclusion of exogenous variables, e.g., volatility indices or high-frequency volatility measures. In simulations, we find coverage of the forecast intervals to be adequate for sufficiently extreme risk levels and sufficiently large samples, which is consistent with theory. Finally, we investigate if covariate information from volatility indices or high-frequency data improves risk forecasts for major US stock indices. While—in our framework—volatility indices appear to be helpful in this regard, intra-day data are not.     

House price risk and the hedging benefits of home ownership

Using a repeat-sales methodology, this paper finds that estimates of house price risk based on aggregate house price indices substantially underestimate the true size of house price risk. This is the result of the fact that aggregate house price indices average away the idiosyncratic volatility in house prices. Additional results show that the idiosyncratic risk exceeds the hedging benefits of home ownership. These results imply that for many home owners, owning a house may well add more price risk than it hedges away. These findings are based on a detailed dataset of individual housing transactions in the Netherlands.     

指数风险偏好下的最优保险策略

本文讨论了当投保个体和保险公司为指数风险偏好时，在保费约束下投保个体的最优保险策略问题。本文采用求解对偶优化问题的方法求解这个问题，并给出当损失服从指数分布时最优保险策略解的解析式。本文最后讨论了投保个体和保险公司风险厌恶程度以及保费预算变化对个体最优保险策略的影响。     

Statistics of extreme events in risk management: The impact of the subprime and global financial crisis on the German stock market

Given the growing need for managing financial risk and the recent global crisis, risk prediction is a crucial issue in banking and finance. In this paper, we show how recent advances in the statistical analysis of extreme events can provide solid methodological fundamentals for modeling extreme events. Our approach uses self-exciting marked point processes for estimating the tail of loss distributions. The main result is that the time between extreme events plays an important role in the statistical analysis of these events and could therefore be useful to forecast the size and intensity of future extreme events in financial markets. We illustrate this point by measuring the impact of the subprime and global financial crisis on the German stock market in extenso, and briefly as a benchmark in the US stock market. With the help of our fitted models, we backtest the Value at Risk at various quantiles to assess the likeliness of different extreme movements on the DAX, S&P 500 and Nasdaq stock market indices during the crisis. The results show that the proposed models provide accurate risk measures according to the Basel Committee and make better use of the available information.     

Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach

We analyze the degree of mutual excitation that exists between extreme events across the stock markets of OECD member nations and the Brent and WTI crude oil markets. For this analysis, marked point process models are proposed which are able to capture the dynamics of the intensity of occurrence and comovement during periods of crisis. The results show a significant, negative interdependence between most OECD markets, especially those of the USA, Japan and France. These major oil importing countries display links between equity market losses and positive returns in both oil markets. However, positive interdependence is not observed between any of the OECD countries except for South Korea. The great advantage of this methodology is that, apart from using the size distribution of extreme events, it also uses the occurrence times of extreme events as a source of information. With this information, these models are better able to capture the stylized facts of extreme events in financial markets such as clustering behavior and cross-excitation.     

Insurance and climate-driven extreme events

We investigate how insurance affects agents’ decisions when being faced by endogenous, climate-driven extreme events. This is not only important in order to understand how the possibility of insurance augments mitigation and saving decisions, but it also improves our understanding of how insurance should be provided. Since there are no studies as of now that rely on such an integrated approach, we extend the literature along two lines. Firstly, we develop a neoclassical growth framework with endogenous extreme events and an insurance sector. Secondly, we introduce a simulation method that allows us to explicitly take these extreme events into account and which yields additional numerical insights. In doing so we can fully characterize and quantify the impact of different insurance policies for mitigation and economic growth decisions.Our analytical results and computational experiments show that (i) transparency of the insurance sector is the decisive requisite for abatement activities, implying substantial policy opportunities; (ii) a decentralized economy will under-invest in abatement without adequate policy interventions; (iii) precautionary beliefs on the frequency of extreme events lead to more sustainability; (iv) a social security system which prices insurance fairly is preferable to an insurance industry which provides insurance with an overhead.