Cryptocurrency returns under empirical asset pricing

This study examines the predictability of cryptocurrency returns based on investors' risk premia. Prior studies that have examined the predictability of cryptocurrencies using various economic risk factors have reported mixed results. Our out-of-sample evidence identifies the existence of a significant return predictability of cryptocurrencies based on the cryptocurrency market risk premium. Consistent with capital asset pricing theory (CAPM), our results show that investors often require higher positive returns before taking on any additional risks, particularly in terms of riskier assets like cryptocurrencies. Tests involving the CAPM model demonstrates that the three largest cryptocurrencies have significant exposures to the proposed market factor with insignificant intercepts, demonstrating that the market factor explains average cryptocurrency returns very well.     

Adverse selection in cryptocurrency markets

In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.     

Are idiosyncratic volatility and MAX priced in the Canadian market?

The negative relationship between realized idiosyncratic volatility (RIvol) and future returns uncovered by Ang et al. (2006) for the U.S. market has been attributed to return reversals. For the Canadian market where return reversals are considerably less important, we find that RIvol is positively related to future returns, even after controlling for risk loadings, illiquidity and reversals. Unlike the findings of Bali et al. (2001) for the U.S. market, we find that the relationship between extreme positive returns (MAX) and future returns for the Canadian market is positive and that idiosyncratic volatility continues to be consistently positively related to future returns after controlling for MAX. We find evidence that suggests that reversals for stocks with extreme daily returns are confined to (typically small) stocks with low institutional holdings.     

Is idiosyncratic volatility priced in cryptocurrency markets?

This paper investigates how idiosyncratic volatility is priced in the cross-section of cryptocurrency returns. By conducting both portfolio-level analysis and Fama-MacBeth regression analysis, we demonstrate that idiosyncratic volatility is positively related to the expected returns of cryptocurrencies. This finding is not subsumed by effects of size, momentum, liquidity, volume, and price and is robust to different weighting schemes, holding periods, and sample sizes. Besides, we find no evidence of temporal relation between idiosyncratic volatility and returns in cryptocurrency markets.     

Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns of five currencies. The forecasting models that have been used in recent analyses of the predictability of daily realized volatility permit a comparison of the predictive power of different measures of intraday variation and intraday returns in forecasting exchange rate variability. The methods of computing quantile forecasts include making distributional assumptions for future daily returns as well as using the empirical distribution of predicted standardized returns with both rolling and recursive samples. Our main findings are that the Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts for currencies which experience shifts in volatility, such as the Canadian dollar, and that the use of the empirical distribution to calculate quantiles can improve forecasts when there are shifts.     

The volatility of consumption-based stochastic discount factors and economic cycles

This paper aims to assess the macroeconomic and financial impact of economic uncertainty using information contained in the second moments of financial risk factors employed in the asset pricing literature. Specifically, we propose the volatility of consumption-based stochastic discount factors (SDFs) as a predictor of future economic and stock market cycles. We employ both contemporaneous and ultimate consumption risk specifications with durable and non-durable consumption. Alternative empirical tests show that this volatility has significant forecasting ability from 1985 to 2006. The degree of predictability tends to dominate that shown by standard predictor variables. We argue that the significant predictability of the volatility of consumption-based SDFs reported in this paper relies mainly on the joint effect of their components.     

Correlated implied volatility with jump and cross section of stock returns

I derive the option‐implied volatility allowing for nonzero correlation between price jump and diffusive risk to examine the information content of implied diffusive, jump risks and their implied covariance in the cross‐sectional variation of future returns. This study documents a strong predictive power of realized volatility and correlated implied volatility spread (RV ? IV_C) in the cross section of stock returns. The difference of realized volatility with the implied diffusive volatility (RV ? σ_C), jump risk (RV ? γ_C) and covariance (RV ? ICov) can forecast future returns. These RV ? σ_C and RV ? γ_C anomalies are robustly persistent even after controlling for market, size, book‐to‐market value, momentum and liquidity factors.     

Cross-section of option returns and volatility

We study the cross-section of stock option returns by sorting stocks on the difference between historical realized volatility and at-the-money implied volatility. We find that a zero-cost trading strategy that is long (short) in the portfolio with a large positive (negative) difference between these two volatility measures produces an economically and statistically significant average monthly return. The results are robust to different market conditions, to stock risks-characteristics, to various industry groupings, to option liquidity characteristics, and are not explained by usual risk factor models.     

Expected stock returns and volatility

This paper examines the relation between stock returns and stock market volatility. We find evidence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility.     

Asset volatility

We examine whether fundamental measures of volatility are incremental to market-based measures of volatility in (i) predicting bankruptcies (out of sample), (ii) explaining cross-sectional variation in credit spreads, and (iii) explaining future credit excess returns. Our fundamental measures of volatility include (i) historical volatility in profitability, margins, turnover, operating income growth, and sales growth; (ii) dispersion in analyst forecasts of future earnings; and (iii) quantile regression forecasts of the interquartile range of the distribution of profitability. We find robust evidence that these fundamental measures of volatility improve out-of-sample forecasts of bankruptcy and help explain cross-sectional variation in credit spreads. This suggests that an analysis of credit risk can be enhanced with a detailed analysis of fundamental information. As a test case of the benefit of volatility forecasting, we document an improved ability to forecast future credit excess returns, particularly when using fundamental measures of volatility.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.     

State‐preference pricing and volatility indices

This study uses a state‐preference pricing approach to develop a state‐price volatility index (SVX), as a forecast for market future realised volatility. We show that SVX is a more efficient forecaster than CBOE VIX for 30‐day realised volatility of SPX returns, using both in‐the‐sample and out‐of‐the‐sample tests. This result is robust to different measures of realised market volatilities. We also show that SVX provides a better volatility forecast than other alternative measures, including the at‐the‐money implied volatilities and GARCH (1, 1) volatility. Our results provide a foundation for forecasting higher risk‐neutral moments using the same state prices.     

Does country risks predict stock returns and volatility? Evidence from a nonparametric approach

We use the k-th order nonparametric causality test at monthly frequency over the period of 1984:1–2015:12 to analyze whether aggregate country risk, and its components (economic, financial and political) can predict movements in stock returns and volatility of eighty-three developed and developing economies. The nonparametric approach controls for the existing misspecification of a linear framework of causality, and hence, the weak evidence of causality obtained under the standard Granger tests cannot be relied upon. When we apply the nonparametric test, we find that, while there is no evidence of predictability of squared stock returns barring one case, at times, there are nearly 50 percent of the countries where the aggregate risks and its components tend to predict stock returns and realized volatility.     

Bond risk premia and realized jump risk

We find that augmenting a regression of excess bond returns on the term structure of forward rates with an estimate of the mean realized jump size almost doubles the R² of the forecasting regression. The return predictability from augmenting with the jump mean easily dominates that offered by augmenting with options-implied volatility and realized volatility from high-frequency data. In out-of-sample forecasting exercises, inclusion of the jump mean can reduce the root mean square prediction error by up to 40%. The incremental return predictability captured by the realized jump mean largely accounts for the countercyclical movements in bond risk premia. This result is consistent with the setting of an incomplete market in which the conditional distribution of excess bond returns is affected by a jump risk factor that does not lie in the span of the term structure of yields.     

Changes in sentiment on REIT industry excess returns and volatility

REIT characteristics pose unique risks and benefits to investors who seek liquid diversification and hedging vehicles to complement their portfolios. This paper tests for the asymmetric effect of individual and institutional investor sentiment on REIT industry returns and conditional volatility. We simultaneously model the impact of two markedly different groups of investors on the return generating process of the REIT industry. Our findings suggest that noise trading imposes significant systemic risk on the realization of REIT industry returns. Interestingly, corrections in institutional investor expectations have a larger effect on REIT industry returns and volatility than changes in individual investor expectations. More specifically, bearish shifts in institutional investor expectations of future market conditions have a significantly larger impact on returns and volatility than bullish shifts. Results align with the overreaction to negative information and loss aversion hypotheses.     

VCRIX — A volatility index for crypto-currencies

Public interest, explosive returns, and diversification opportunities gave stimulus to the adoption of traditional financial tools to crypto-currencies. While the CRIX offered the first scientifically-backed proxy to the crypto-market (analogous to S&P 500), measuring the forward-oriented risk in the crypto-currency market posed a challenge of a different kind. Following the intuition of the “fear index” VIX for the American stock market, the VCRIX volatility index was created to capture the investor expectations about the crypto-currency ecosystem. VCRIX is built based on CRIX and offers a forecast based on the Heterogeneous Auto-Regressive (HAR) model. The HAR model was selected as the most suitable out of a horse race of volatility models, with two proxies for implied volatility, namely the 30 days mean annualized volatility and realized volatility. The model was further examined by the simulation of VIX (resulting in a correlation of 78% between the actual VIX and a “VIX” version estimated with the VCRIX technology). Trading strategies confirmed the predictive power of VCRIX and supported the selection of the 30 days means annualized volatility proxy. The best performing trading strategy with the use of VCRIX outperformed the benchmark strategy for 99.8% of the tested period and 164% additional returns. VCRIX provides forecasting functionality and serves as a proxy for the investors’ expectations in the absence of a developed crypto derivatives market. These features provide enhanced decision making capacities for market monitoring, trading strategies, and potentially option pricing.     

Stock market volatility: friend or foe?

Although a good deal of research effort has been allocated to understanding the time-series volatility of stock returns – as both market (or systematic) volatility and idiosyncratic (or non-systematic) volatility – the relationship of such volatility with cross-sectional volatility or dispersion of outcomes is sparse. Nevertheless, the quest to understand one must involve the quest to understand the other. In this paper, we investigate the dispersion of returns in relation to inter-temporal volatility, as well as the dynamic of dispersion of returns in generating a portfolio’s return outcome. We find that the level of such dispersion is highly significant for portfolio performance and the notion of risk.     

Credit default swaps,the leverage effect,and cross-sectional predictability of equity and firm asset volatility

Leverage represents both a fundamental component of equity volatility and a long-run selection variable. Based on this premise, we investigate the influence of leverage on the long-run cross-sectional predictability of future realized equity volatility. Leverage makes equity volatility significantly less predictable than underlying firm asset volatility, a result that is robust to different predictors of future realized volatility: credit default swap implied, historical, and option implied volatility. A simple model of optimal capital structure, wherein companies maximize tax benefits subject to a common maximum default probability (minimum credit rating) target, helps explain this finding.     

Investor attention in cryptocurrency markets

We examine the relationship between investor attention, and measures of uncertainty, with the market dynamics of Bitcoin and other cryptocurrencies. We find that increases in investor attention are associated with higher returns, more volatility, and greater illiquidity in cryptocurrency markets. In contrast, cryptocurrency uncertainty (UCRY) and financial market uncertainty (VIX) are also positively related to volatility and illiquidity but have a negative contemporaneous relationship with returns. The identified relationships are accentuated during the COVID-pandemic, and are robust to different measures of investor attention, volatility, and illiquidity. Our results suggest that monitoring investor attention could assist both investors and policymakers.     

On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia

This paper categorizes Australian listed cryptocurrency-linked stocks (CLS) by their involvement as a user, developer and diffuser, and investor of blockchain technology and cryptocurrencies based on company announcements and published information on the company websites. By distinguishing CLS engagement with blockchain technology, we examine their returns and volatility spillover with the cryptocurrency market over the period 1 September 2017 to 7 June 2018, spanning important episodes and dynamics in the cryptocurrency market in 2017-2018, and the emergence of Australian CLS. Utilizing the Diebold and Yilmaz (2012) spillover methodology, we find significant unidirectional return spillover and weak volatility spillover from the cryptocurrency market to CLS, after controlling return dynamics of the Australian dollar, Gold and commodity. However, CLS with high involvement in blockchain technology displays stronger connectedness to the cryptocurrency market through return spillover relative to low involvement CLS. Our findings indicate that investors incorporate the price dynamics of cryptocurrencies into their trading decisions for CLS.