Upside Volatility Research Articles

Smoothed semicovariance estimation for portfolio selection

AbstractDownside risk measures, such as semivariance, are essential for evaluating investment risk. Focusing on semivariance allows investors to emphasize loss mitigation without considering upside volatility as risk. However, minimizing the semivariance of a portfolio is an analytically intractable and numerically challenging problem due to the endogeneity of the parameters in the semicovariance matrix. We introduce a methodology for consistent estimation of the portfolio semivariance based on a smooth approximation of the empirical semicovariance matrix. Differently from existing methods, the new estimator does not rely on biased surrogate semicovariance models and enables the treatment of large problems with many assets. The extent of smoothing is determined by a single tuning constant, which allows our method to span an entire set of optimal portfolios with limit cases represented by the minimum semivariance and the minimum variance portfolios. The methodology is implemented through an iteratively reweighted algorithm, which is computationally efficient for high-dimensional problems with many assets. Our numerical studies confirm the theoretical convergence of the smoothed semivariance estimator to the traditional sample semivariance. The resulting minimum smoothed semivariance portfolio performs well in- and out-of-sample compared to other popular selection rules.

The Impact of Cryptocurrency Volatility Dynamics on the Islamic Equity Market: The Case of Emerging Asia

This paper investigates the impact of the realized volatility of positive and negative intraday Bitcoin returns on the sensitivity of Shariah-compliant stocks’ orthogonalized returns. We identify the impact in different market states and find that Bitcoin’s upside volatility negatively affects the returns of Islamic equities. The paper contributes to uncovering the properties of a niche Islamic Emerging Asian equity market. The findings offer important implications for investors’ diversification strategies.

Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?

AbstractWe analyze the out‐of‐sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high‐frequency intra‐day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside (“good”) volatility, realized downside (“bad”) volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection and (ii) a model‐based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR‐RV model and the HAR‐RV‐sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment.

Can mutual fund investors benefit from volatility managing? Evidence from China

Using a comprehensive sample of Chinese mutual funds from 2004 to 2021, we find strong evidence that managing the intertemporal total (downside) risk of actively managed equity mutual funds significantly improves the performance in terms of alphas, Sharpe ratios, and Sortino ratios. Each volatility-managed strategy studied in this paper can survive transaction costs. By testing the relation between risk and return of mutual funds, we find the gains from managing the total (downside) volatility are attributed to both volatility timing and return timing. Empirical results are robust to alternative measurements of total (downside) volatilities, leverage constraints, rebalancing frequency, factor models, sample filters, and a portfolio of all mutual funds. There is no obvious relationship between fund flows and past fund total volatility in the Chinese fund market. However, after dividing the total volatility into upside and downside volatility, the fund flows are positively (negatively) related to the upside (downside) volatility, and these relations are more pronounced for institution-dominated funds.

Forecasting stock market volatility: The sum of the parts is more than the whole

The volatility of financial assets can be decomposed into upside volatility and downside volatility. However, these two components have unique properties, so their predictability is completely different. In this paper, we explore a new forecasting method to predict the S&P 500 volatility by separately modeling upside volatility and downside volatility and summing the forecasts up. Our new method is proved to have better performance compared with directly modeling aggregate volatility. Moreover, the gains in forecast accuracy are robust concerning the individual and combined models.

Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning

Keeping risk under control is often more crucial than maximizing expected reward in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The most natural choice of risk measures is variance, while it penalizes the upside volatility as much as the downside part. Instead, the (downside) semivariance, which captures the negative deviation of a random variable under its mean, is more suitable for risk-averse proposes. This paper aims at optimizing the mean-semivariance (MSV) criterion in reinforcement learning w.r.t. steady rewards. Since semivariance is time-inconsistent and does not satisfy the standard Bellman equation, the traditional dynamic programming methods are inapplicable to MSV problems directly. To tackle this challenge, we resort to the Perturbation Analysis (PA) theory and establish the performance difference formula for MSV. We reveal that the MSV problem can be solved by iteratively solving a sequence of RL problems with a policy-dependent reward function. Further, we propose two on-policy algorithms based on the policy gradient theory and the trust region method. Finally, we conduct diverse experiments from simple bandit problems to continuous control tasks in MuJoCo, which demonstrate the effectiveness of our proposed methods.

How do Islamic equity markets respond to good and bad volatility of cryptocurrencies? The case of Bitcoin

This paper investigates the differential sensitivity of Sharia-compliant stocks to Bitcoin's realized volatility of positive and negative intraday returns in bear, normal, and bull market states. We use a quantile regression approach, after orthogonalizing raw equity returns with respect to a variety of relevant global factors and accounting for structural breaks in the data. For developed markets, the results indicate that upside volatility tends to exert contemporaneous and lagged negative influences on Islamic stocks more in bear than in bull market conditions, whereas the downside counterpart positively affects returns when Sharia-compliant equities are in bear and bull phases. For emerging markets, we find that Bitcoin's upside (downside) volatility has lagged negative (positive) effects on returns across all market regimes. The dependence structures tend to be asymmetric and have noticeably become stronger in the last two years than in earlier periods of the sample. Our evidence offers important implications for investors.

Ethereum synchronicity, upside volatility and Bitcoin crash risk

The fundamental value of Bitcoin is difficult to evaluate because there is no underlying company or cashflow. Extant literature documented various macro-economic factors and technical factors to price Bitcoin. However, for a highly speculative instrument, investors’ behavior plays an important role in asset pricing. This paper incorporates Bitcoin Ethereum synchronicity conditional on upside volatility of Bitcoin as a proxy for the fear of high Bitcoin prices. Empirical results reveal that when upside volatility is high, Ethereum synchronicity exerts a significant positive influence on Bitcoin crash risk.

Informational content of options around analyst recommendations

PurposeMore studies have investigated the relation between option measures and stock returns during scheduled corporate events. This study adds to the literature and investigates the informational role of options concerning stock returns following unscheduled corporate news events. The authors focus on individual analysts' recommendation changes rather than consensus revisions, as the recommendation consensus might discard a large amount of potentially valuable information in the aggregation process.Design/methodology/approachBased on the econometric model, the authors follow Bakshi et al. (2003) to construct the model-free option implied measures. The authors further decompose the implied option variance into upside and downside components. In such a way, the different informational roles of call and put options can be distinguished. A variety of regression analyses are conducted to examine the predictive power of option implied measures, and the ordered probit model is used to test the tipping hypothesis of analyst recommendations.FindingsThis study’s results show that the option market impounds the “valuable” firm-specific news; thus, the pre-event option market is strongly related to stock returns around recommendations even though recommendation changes are largely “unscheduled”. At the same time, these results suggest that upside (good) and downside (bad) implied volatilities contain distinctive information on subsequent stock returns.Originality/valueThis study provides new evidence that an increase in upside (downside) volatility around analyst recommendation changes would increase the probability that analysts upgrade (downgrade) the stock. The findings provide implications for investors and risk managers in making investment decisions.

Index option trading and equity volatility: Evidence from the SSE 50 and CSI 500 stocks

Based on the proposed difference-in-difference design, we scrutinize the influence of stock derivatives on the underlying assets by differentiating the influence between stock futures and options. The empirical results indicate that option trading reduces equity volatility in the Shanghai Stock Exchange. The alleviation effect starts to show quickly after the introduction of SSE 50 ETF options. The component stocks in the index experience lower volatility than non-component stocks. We also document that the impact of option trading is stronger in downside volatility (bear markets) than upside volatility (bull markets). In addition, we conduct evolutionary trace analysis and show the empirical results are sensitive to the length of sample periods. This finding may explain the mixed conclusions in current literature.

Downside Volatility-Managed Portfolios

Downside volatility and volatility typically co-move but are not highly correlated during the most volatile times. The authors show that portfolios scaled by downside volatility expand the ex post mean–variance frontiers constructed using the original portfolios and volatility-managed portfolios and improve the Sharpe ratios of the ex post tangency portfolios. Their results follow from the empirical finding that downside volatility-managed portfolios are not spanned by the original portfolios or volatility-managed portfolios. Whereas downside volatility-managed portfolios expand the investment opportunity set, upside volatility-managed portfolios do not. TOPICS:Volatility measures, portfolio theory Key Findings • Downside volatility and volatility do not always comove. • Downside volatility-managed portfolios expand the investor’s opportunity set. • Upside volatility does not appear to help in portfolio management.

What is a Hedge or Safe Haven Asset for Bitcoin Investors?

This paper investigates a hedge and safe haven asset for Bitcoin investors. Bitcoin has been receiving high attention from finance investors because of its high upside return and volatility. The recent finance literature focused upon Bitcoin characteristics as an alternative asset. We take Bitcoin investors’ perspectives and consider how to manage the high volatility of Bitcoin. We employ the definitions of hedge and safe haven based on the finance literature and conduct the respective statistical analyses. Our definition distinguishes a weak and strong hedge (safe haven). Our empirical results show that traditional assets such as global equities and global bonds are weak hedges for Bitcoin. Furthermore, we observe that gold acts as a strong hedge against Bitcoin during an extreme bearish Bitcoin market, although the impact is marginal. There is no strong safe haven asset identified in our data period. Our results imply that the fundamental value of Bitcoin is still unclear, and it is difficult for Bitcoin investors to manage their portfolio risk.

Downside and Upside Volatility, Value-at-Risk, Expected Shortfall and Pricing of Options Based on a Skewed Generalized Logistic Distribution

This paper derives the moment functions of the truncated skewed type III generalized logistic (SGL). These are then applied in finance for the development of value-at-risk, expected shortfall and downside risk measures for investment returns and values. The SGL distribution provides and good fit to the empirical distribution of a representative set of long series of financial data. Moreover, the SGL generates accurate value at risk measures.

Managing Downside Risk With Upside Volatility: Enhancing The Performance Of An Investment Portfolio With Managed Futures

<p class="MsoNormal" style="text-align: justify; margin: 0in 27pt 0pt 0.5in;"><span style="font-size: 10pt; mso-bidi-font-weight: bold;" lang="EN-GB"><span style="font-family: Times New Roman;">Managed futures refer to the active trading of forward and futures contracts on physical commodities, financial assets, and currencies.<span style="mso-spacerun: yes;">  </span>Since managed futures have little to no correlation with stocks and bonds, advocates claim this asset class provides much-needed diversification, allowing investors to profit from price volatility. <span style="mso-spacerun: yes;"> </span>However, naysayers believe this volatility makes managed futures a highly risky investment. Do the unique features of this asset class make it worthy of inclusion in an investment portfolio?<span style="mso-spacerun: yes;">  </span>This article asserts managed futures provide investment managers a powerful tool to manage downside risk by capitalising on upside price volatility.</span></span></p>

Low Risk and High Returns: Evidence from the German Stock Market

Although most of the empirical and theoretical asset pricing literature predicts a positive or no signi ficant relationship between idiosyncratic volatility and returns, Ang et al. (2006, 2009) find that high idiosyncratic volatility stocks have low returns and vice versa. We deliver further evidence and show that the cross-section of the German stock market re effects a negative premium for idiosyncratic risk. Even after controlling for the three Fama-French factors we estimate a signi ficant risk premium of -0.8% per month. In addition, we undertake further robustness checks like the differentiation between upside and downside idiosyncratic volatility, the application of the (E)GARCH approach to estimate idiosyncratic risk and the use of monthly instead of daily data. However, the puzzle still prevails.

Separating Up from Down: New Evidence on the Idiosyncratic Volatility - Return Relation

The finding that stocks with high idiosyncratic volatility tend to have low future returns has been dubbed an empirical anomaly in the finance literature. We seek to understand this puzzle by separating the upside volatility associated with positive idiosyncratic returns from the downside risk associated with negative idiosyncratic returns. We find that downside risk is not inversely related to future stock returns, thus easing the concern that the empirical anomaly is a mispricing of risk. Rather, our results suggest that it is upside volatility that drives the inverse idiosyncratic volatility and return relation. We further examine whether the relation of future returns with downside and upside volatility accords with investor underreaction to bad news and overreaction to good news. Finally, we show that momentum strategies may be enhanced by taking into account stocks' upside volatility.

International capital asset pricing: Evidence from options

We study the risk dynamics and pricing in international economies through a joint analysis of the time-series returns and option prices on three equity indexes underlying three economies: the S&P 500 Index of the United States, the FTSE 100 Index of the United Kingdom, and the Nikkei-225 Stock Average of Japan. We develop an international capital asset pricing model, under which the return on each equity index is decomposed into two orthogonal jump-diffusion components: a global component and a country-specific component. We apply separate stochastic time changes to the two components so that stochastic volatility can come from both global and country-specific risks. For each economy, we assign separate market prices for the two return risk components and the two volatility risk components. Under this specification, we obtain tractable option pricing solutions. Model estimation reveals several interesting insights. First, global and country-specific return and volatility risks show different dynamics. Global return movements contain a larger discontinuous component, and global return volatility is more persistent than the country-specific counterparts. Second, investors charge positive prices for global return risk and negative prices for volatility risk, suggesting that investors are willing to pay positive premiums to hedge against downside global return movements and upside volatility movements. Third, the three economies contain different risk profiles and also price risks differently. Japan contains the largest idiosyncratic risk component and smallest global risk component. Investors in the Japanese market also price more heavily against future volatility increases than against future market downfalls.

International Capital Asset Pricing: Evidence from Options

We study the risk dynamics and pricing in international economies through a joint analysis of the time-series returns and option prices on three equity indexes underlying three economies: the S&P 500 Index of the United States, the FTSE 100 Index of the United Kingdom, and the Nikkei-225 Stock Average of Japan. We develop an international capital asset pricing model, under which the return on each equity index is decomposed into two orthogonal jump-diffusion components: a global component and a country-specific component. We apply separate stochastic time changes to the two components so that stochastic volatility can come from both global and country-specific risks. For each economy, we assign separate market prices for the two return risk components and the two volatility risk components. Under this specification, we obtain tractable option pricing solutions. Model estimation reveals several interesting insights. First, global and country-specific return and volatility risks show different dynamics. Global return movements contain a larger discontinuous component, and global return volatility is more persistent than the country-specific counterparts. Second, investors charge positive prices for global return risk and negative prices for volatility risk, suggesting that investors are willing to pay positive premiums to hedge against downside global return movements and upside volatility movements. Third, the three economies contain different risk profiles and also price risks differently. Japan contains the largest idiosyncratic risk component and smallest global risk component. Investors in the Japanese market also price more heavily against future volatility increases than against future market downfalls.

Re-thinking country risk: insights from entrepreneurship theory

Though country risk analysis is a well-established field within international business, evidence indicates that established measures of country risk are unreliable predictors of actual volatility. Conventional strategies aimed at minimizing or otherwise avoiding downside risk are likely to yield limited results at best; at worst, these strategies will lead managers to miss entrepreneurial opportunities, which are likely to be greatest during conditions of disequilibrium. Drawing from entrepreneurship theory, I propose an alternative perspective from which to approach country risk. By focusing on both the downside and upside elements of country risk, strategies may be devised to harvest upside volatility while containing downside volatility. Rather than being something to always avoid, country risk becomes an opportunity to profit from uncertainty.

The Fear and Exuberance from Implied Volatility of S&P 100 Index Options

I study the relation between option traders’ risk perception and contemporaneous market conditions. Risk perception tends to increase when downside volatility increases more than upside volatility. The risk‐return relation is asymmetric and nonlinear, best described as a downward‐sloping reclined S‐curve. That prior gains appear to have some mitigating effect on the fear of loss relative to prior losses points to a “house money” effect. Broader market conditions influence the perception of risk in a manner consistent with the “keeping up with the Joneses” effect. Leverage is a weak explanation for the risk‐return relation.