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Buy-now-pay-later (BNPL) arrangements have rapidly emerged as a short-term debt option, and like other innovative and disruptive Fintech, challenge existing regulation. BNPL arrangements avoided prescribed ‘responsible lending’ legislative obligations, which applied to similar short-term credit products. Instead, BNPL relies on ‘responsible spending’ in providing a potentially cheaper option than alternatives such as credit cards. We describe the interplay of regulation and responsibility with BNPL. A survey investigates whether a key demographic (young adults) have an appetite or skill for responsible use. We analyse the preference for BNPL relative to credit cards and the role of financial literacy and traits including propensity to plan and save. The findings suggest that financial literacy reduces perceived BNPL benefits and that lower financial literacy is associated with more benefits and less risks. JEL Classification: G53, D14, G51, G41, G18

Bitcoin is designed as a peer-to-peer cash system. To work as a currency, it must be stable or be backed by a government. In this paper, we show that the volatility of Bitcoin prices is extreme and almost 10 times higher than the volatility of major exchange rates (US dollar against the euro and the yen). The excess volatility even adversely affects its potential role in portfolios. Our analysis implies that Bitcoin cannot function as a medium of exchange and has only limited use as a risk-diversifier. In contrast, we use the deflationary design of Bitcoin as a theoretical basis and demonstrate that Bitcoin displays store of value characteristics over long horizons.

This paper studies a large number of bitcoin (BTC) options traded on the options exchange Deribit. We use the trades to calculate implied volatility (IV) and analyze if volatility forecasts can be improved using such information. IV is less accurate than AutoRegressive–Moving‐Average or Heterogeneous Auto‐Regressive model forecasts in predicting short‐term BTC volatility (1 day ahead), but superior in predicting long‐term volatility (7, 10, 15 days ahead). Furthermore, a combination of IV and model‐based forecasts provides the highest accuracy for all forecasting horizons revealing that the BTC options market contains unique information.

Gold is a globally traded asset and held in large quantities by investors and central banks. Since there is no established model to assess if the price of gold is overvalued or undervalued, we propose a relative valuation framework based on gold price ratios and focus on potential drivers of resulting misalignments. This idea is not confined to gold but offers the foundation for relative valuation of a broad range of different assets or asset classes in the spirit of Campbell and Shiller (2001). We analyze gold prices relative to commodity prices, consumer prices, stock prices, dividend and bond yields and find that the relative value of gold varies significantly over time indicating pronounced periods of mispricing of gold relative to other assets. An analysis of the factors which drive these variations demonstrates that inflation expectations and uncertainty have a strong influence on gold ratios while macroeconomic fundamentals are less important. More specifically, a boost in confidence decreases the relative price of gold while heightened uncertainty increases the relative price of gold which confirms the role of gold as a safe haven.

Climate-related criticism of Bitcoin is primarily based on the network's absolute carbon emissions, without considering its market value. Taking a relative emission perspective and utilizing the mean–variance portfolio optimization framework, we study the financial and carbon-related implications of Bitcoin investments. The results of our in-sample analysis show that adding Bitcoin to a diversified equity portfolio can both enhance the risk–return relationship of the portfolio and reduce the portfolio's aggregate carbon emissions. This finding persists under various assumptions regarding Bitcoin prices, carbon emission estimates, and carbon prices.

The Anna Karenina principle (AKP) based on the opening line of Leo Tolstoy’s Anna Karenina, “All happy families are all alike; each unhappy family is unhappy in its own way” is applied to financial markets. We test the AKP by defining happy firms as positive return firms and unhappy firms as negative return firms and analyse whether happy firms are more “alike” than unhappy firms. We use return correlations, cross-sectional return and cross-sectional volatility dispersion as measures and find for different stock index constituent samples totalling more than 8000 stocks that the AKP does not hold in general. In contrast, we find that the average return volatility of unhappy firms is larger than that of happy firms. This cross-sectional version of volatility asymmetry also implies that unhappy firms are more stressed than happy firms.

This study shows that changes in bitcoin exchange reserves are negatively related to contemporaneous and future bitcoin returns, consistent with the hypothesis that the transfer of bitcoin on exchanges implies increased price pressure and vice versa. We further identify an asymmetry between positive and negative reserve changes on bitcoin returns and volatility which in turn also affect exchange reserves in extreme market conditions. The results indicate that a significant fraction of bitcoin investors store their wealth off exchanges and only use exchanges to trade. This highlights a special feature of cryptocurrency trading that does not exist in traditional markets.

This paper proposes a new approach to identify safe haven assets and to characterize their relationship with the market. We use quantile regression to analyze the returns of potential safe haven assets conditional on all market conditions including periods of financial turmoil. We find a trade-off, i.e., stronger in-crisis performance of safe haven assets is associated with weaker out-crisis performance and vice versa for risky assets. Our analysis confirms the safe haven properties of gold and U.S. government bonds and presents clear differences between safe haven assets and safe assets, and between safe haven assets and risky assets such as Bitcoin.

What is the optimal weight of gold in an investment portfolio? Suggestions range from 0% to more than 20%, but there is no study that systematically investigates this issue. We analyze the gold holdings (gold ETFs) of institutional investors based on 13-F filings and find that 30% of all institutions have at least some exposure to gold in their portfolio, with an average percentage holding of 1.7% with large differences across institutions and over time. The results further identify key determinants of gold holdings and demonstrate that gold holdings positively affect the performance of institutional investor portfolios.

This paper shows that gold’s safe haven property crucially depends on gold’s exchange rate with the respective currency for local investors. A sample of 68 international equity market indices demonstrates that currencies significantly affect the strength of the safe haven effect, enhancing or diminishing the effect. Volatile and “risk-on” currencies boost gold’s safe haven property while “risk-off” currencies weaken the safe haven effect.

This paper examines the predictability of fear of coronavirus on future returns and volatility of five major cryptocurrencies during the COVID-19 outbreak. Adopting Google search volume on a comprehensive list of coronavirus-related terms to construct a gauge of fear, we show that daily innovation in fear of coronavirus is associated with next-day negative returns and positive volatility. The results indicate that (i) cryptocurrencies (particularly bitcoin) are not a safe haven for investors against the COVID-19 pandemic, and (ii) Google search contains important information that might be useful to predict cryptocurrency market movements during times of crisis.

There is a large and growing literature on spillovers but no study that systematically evaluates the importance of spillovers for portfolio management. This paper provides such an analysis and demonstrates that spillovers are fully embedded in estimates of expected returns, variances, and correlations and that estimation of spillovers is not necessary for asset allocation. Simulations of typical empirical spillover settings further show that same-frequency spillovers are often negligible and spurious.

We propose to estimate the conditional variance of a time series of financial returns through a quantile autoregressive (AR) model and demonstrate that it contains all information commonly captured in two separate equations for the mean and variance of a generalized AR conditional heteroscedasticity-type model. We show that the inter-quantile range spanned by conditional quantile estimates identifies the asymmetric response of volatility to lagged returns, resulting in wider conditional densities for negative returns than for positive returns. Finally, we estimate the conditional variance based on the estimated conditional density and illustrate its accuracy in a forecast evaluation.

There is a well documented asymmetric return-volatility effect of equity returns, that is, negative shocks increase volatility by more than positive shocks. This paper analyzes the return-volatility relationship of commodity prices and finds a positive (inverted) asymmetric effect with a tendency to weaken and converge towards an equity-like effect since the mid 2000s and particularly during the global financial crisis. A comparison of the findings with equity prices also reveals a strengthening of the asymmetric effect in equity markets. The change in the asymmetric volatility effect is consistent with the financialization of commodity markets and has strong portfolio implications.

This paper analyzes the oil price sensitivity of the world's largest automobile manufacturers. After controlling for systematic effects we identify a negative oil price sensitivity consistent with a fuel-cost demand effect. This effect has strengthened recently potentially due to the increased popularity of SUVs and despite efforts of major producers to start or increase the production of hybrid and electric vehicles. Tesla is the only company that displays a positive oil price sensitivity consistent with a substitution effect between combustion-engine cars and electric cars.

This paper demonstrates that the Eurozone sovereign debt crisis constitutes a special case in the contagion literature with general implications. Perfectly correlated bond markets imply that contagion can only occur if there is a decoupling to lower correlation levels with increased idiosyncratic shocks leading to more severe but less systemic spillovers. This theoretical prediction is fully supported by the empirical analysis. We also show that dynamic coexceedance estimates provide a more robust and more general picture of contagion than correlation‐based tests. Coexceedances identify only one major incidence of contagion that affected five periphery Eurozone countries in May 2010 and coincided with flight to quality from the periphery to the core and the 2010 “flash crash” in US equity markets.

Dyhrberg [2016. Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters 16, 85–92] analyzes the relationship between Bitcoin, gold and the US dollar and states that Bitcoin can be classified as something in between gold and the US dollar. This paper uses the same sample and econometric models to replicate the findings and demonstrates that exact replication is not possible and that alternative statistical methods provide more reliable, yet very different results. The findings based on the original sample and an extended sample period show that Bitcoin exhibits distinctively different return, volatility and correlation characteristics compared to other assets including gold and the US dollar.

© 2017 Elsevier B.V. Bitcoin is defined as digital money within a decentralized peer-to-peer payment network. It is a hybrid between fiat currency and commodity currency without intrinsic value and independent of any government or monetary authority. This paper analyses the question of whether Bitcoin is a medium of exchange or an asset and more specifically, what is its current usage and what usage will prevail in the future given its characteristics. We analyse the statistical properties of Bitcoin and find that it is uncorrelated with traditional asset classes such as stocks, bonds and commodities both in normal times and in periods of financial turmoil. The analysis of transaction data of Bitcoin accounts shows that Bitcoins are mainly used as a speculative investment and not as an alternative currency and medium of exchange.

Bitcoin is regularly referred to as new gold, digital gold or gold 2.0. If Bitcoin is indeed gold-like the correlation of Bitcoin and gold returns should be positive. We estimate the correlation of the two assets across time, across different return frequencies and across quantiles and find a near-zero correlation inconsistent with the claimed similarity. We offer two explanations for this puzzle: either the similarity is only a narrative and not accepted by investors or there are other forces at play that depress the true correlation. Such forces could be a substitution effect, investors sell gold and buy Bitcoin, and a catching up effect, investors buy Bitcoin to catch up with the market weight of gold.

This paper analyses the impact of the coronavirus pandemic on the share prices of gold firms, whose activities are spread across gold exploration, project development and gold mining with markedly different risk characteristics. We find evidence for COVID-induced stock market contagion leading to a decoupling of gold companies from the price of gold illustrating that gold shares are not a safe haven. The equity market and gold exposures differ significantly between explorers, developers and producers in normal times and are higher and more similar in crisis times. Our findings demonstrate that investors treat different firms differently in normal times and more equally in crisis times leading to mispricing and profit opportunities.

This paper proposes a simple framework to distinguish lagged effects (spillovers) from contemporaneous effects and to estimate their relative importance. We use an eclectic sample of assets from five different asset classes and find that spillovers have low explanatory power of returns and volatility on average but high explanatory power during the COVID-19 pandemic and that volatility spillovers have higher explanatory power than return spillovers. The results indicate that past information is more efficiently incorporated in stock prices in normal times than in crisis times.

The time between order submission and order confirmation is crucial for high frequency traders as they risk slippage when latency is too high. We hypothesize that high latency in cryptocurrency markets implies correlations well below one across exchanges at high frequencies. To evaluate this conjecture, we measure the correlation of returns across exchanges at increasing sampling frequencies. Since all exchanges trade the same asset, correlations must eventually approach one. However, at high frequencies, correlations are close to zero. The correlation is approaching one after about 10 minutes which we link to the median confirmation time on the blockchain highlighting the importance of the underlying blockchain design. The analysis also shows considerable differences of cryptocurrency trading with stock market trading.

This paper analyzes the stability of stablecoins and proposes a framework to test for absolute and relative stability of stablecoins. Based on high-frequency data we find strong evidence of excess price variations. While Bitcoin is a likely source of this excess volatility because stablecoin returns, volatility and volumes are highly correlated with corresponding Bitcoin time-series, we also demonstrate through a quasi-natural experiment that stablecoins increase the trading volume of Bitcoin. The findings suggest stablecoins play a key role in cryptocurrency markets.

This paper analyzes the impact of the coronavirus pandemic on Australian companies’ share prices. We use daily new infections as a risk factor and proxy for the severity of the pandemic. Based on the loading to this risk factor and abnormal returns during the crisis we categorize firms into (i) severely infected, (ii) infected and (iii) mildly or not infected. We find large differences across firms and sectors highlighting that the virus does not affect all firms and not in the same way contrasting evidence of financial contagion and excess co-movement from past crises. The increased cross-sectional dispersion of returns in the pandemic suggests a sophisticated response of investors resulting in significant diversification benefits.

This paper analyzes the demand for jewelry, bars & coins, and gold ETFs across high-income and middle-income countries. We employ a simultaneous equations model that allows a joint determination of the various categories of gold demand and find significant differences across gold demand categories and across countries. Jewelry and bars & coins demand are positively related in middle-income countries but unrelated in high-income countries. Jewelry demand is negatively related to gold prices in middle-income countries but not in high-income countries. Bars and coins demand positively reacts to global risk (VIX) in high-income countries but not in middle-income countries. The findings indicate that not all gold is equal and serves different purposes in different countries. Gold is generally more integrated with financial markets in high-income countries than in middle-income countries and jewelry is the least integrated among the three categories of gold demand.

Companies increasingly announce goals to reduce carbon emissions. How do firms in the oil and gas industry achieve such goals without fundamentally changing their business model? We analyze this question and aim to identify any changes in firms' exposures to oil and to clean and renewable energy. We find a decreased systematic risk exposure and an increased exposure to oil and to clean and renewable energy. We also identify a substantially increased positive correlation of these exposures, i.e. the higher the oil exposure the higher the clean energy exposure suggesting that firms compensate their oil exposure with investments in clean and renewable energy.

There are several assets that are frequently labelled a safe haven such as gold, U.S. and German government bonds, currencies such as the Swiss franc and the U.S. dollar and, more recently, Bitcoin. This paper proposes a safe haven index to benchmark safe haven assets and identify some stylized facts. The analysis shows that (i) safe haven assets are risky (in general and in crises) despite their "safe" label, (ii) the crisis-specific riskiness is a necessary condition to function as a strong safe haven, and (iii) safe haven asset risk can be diversified. An analysis of the COVID-19 shock in March 2020 reveals that the safe haven index briefly fell with the market contrasting previous crises over the last 40 years.

This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.

Gold is special as it is influenced by a wide range of factors such as commodity prices, interest rates, inflation expectations, exchange rate changes and stock market volatility. Hence, forecasting the price of gold is a difficult task and the main problem a researcher faces is to select the relevant regressors at each point in time. This model uncertainty in combination with parameter uncertainty is explicitly accounted for by Dynamic Model Averaging (DMA) which allows both the forecasting model and the coefficients to change over time. Based on this framework, we systematically evaluate a large set of possible gold price determinants and find that DMA (1) improves forecasts compared to other frameworks, (2) yields strong time-variation of gold price predictors and (3) favors parsimonious models. The results also show that typical in-sample features of gold such as its hedge property are weaker in an out-of-sample context.

Gold is a prominent safe haven asset but risky compared to other safe haven assets such as US government bonds. We identify unique features of gold that explain why investors under stress buy the riskier alternative gold. We argue that the decision to buy gold is rooted in behavioral biases associated with gold’s history as a currency, a store of value and a safe haven. The empirical analysis shows that gold was a particularly strong safe haven in the aftermath of September 11, 2001 and the Lehman bankruptcy in September 2008. The Global Financial Crisis also exemplifies the role of the US dollar as a safe haven currency and how it can mask the safe haven effect of gold. Finally, we find that safe haven assets do not exacerbate crises via a negative feedback effect.

Initial Coin Offerings (ICOs) are a new form of venture capital financing that raised more than 30 billion US dollars between 2016 and 2019. ICOs opened up the venture capital market to a more diverse investor base previously limited to “sophisticated” investors. This study analyses 1,258 white papers and finds that the length is positively correlated with the amount raised during the ICO and the likelihood of successfully completing the ICO. In contrast, the readability grade of the white paper is only positively correlated with the amount raised. The study highlights the role of voluntary disclosure in the ICO market.

The literature on gold is dominated by empirical studies on its diversification, hedging, and safe haven properties. In contrast, the question “When to invest in gold?” is generally not analyzed in much detail. We test more than 4000 seasonal, technical, and fundamental timing strategies for gold and find evidence for some market timing ability and economic gains relative to a passive buy-and-hold benchmark. However, since the results are not robust to data-snooping biases and limited to specific evaluation periods, we conclude that our findings support the efficiency of the gold market.

We analyse the relationship between geopolitical risk and asset prices and show that geopolitical risk is distinct from existing measures of economic, financial, and political risk and that the response of precious metals to geopolitical risk differs considerably from that of other assets. Precious metals are hedges against geopolitical risk in general and geopolitical threats (as opposed to acts) in particular. Conversely, stocks and bonds respond negatively to geopolitical risk and geopolitical threats. For extreme geopolitical risks, only gold and silver display consistent safe haven properties. Our results show that holding precious metals within a diversified portfolio lowers the impact of geopolitical risk.

The design of Bitcoin is closely related to gold which has led to the idea that Bitcoin has gold-like features such as being a store of value and a safe haven. However, given Bitcoin’s extreme volatility investors may rather need a safe haven against Bitcoin. We hypothesize that stablecoins provide such a safe haven and analyze high-frequency price changes of the largest stablecoins. We find that they act as safe havens, and Tether showing the strongest effect. The results demonstrate that Bitcoin investors seek out stablecoins when Bitcoin experiences extreme negative prices changes. Since stablecoins react to such price changes they are not stable at all times.

Insider trading laws are designed to ensure a level-playing field and trust in financial markets at the expense of less efficient markets. This paper argues that insider trading laws fail to ensure a level-playing field and instead facilitate fraud and undermine trust and fairness. We use a model to show that insider trading laws result in delayed price movements, lower liquidity, increased volatility and crash risk. A welfare analysis further shows that informed traders benefit the most from insider trading laws. An empirical example, the Volkswagen Diesel scandal, supports the predictions of the theoretical model and relates the emissions testing defeat device to insider trading laws that act as an informational defeat device.