Forecasting stock market volatility: Can the risk aversion measure exert an important role?

In this paper, we predict realized volatility of stock return by utilizing time-varying risk aversion based on a simple linear autoregressive model. Our in-sample results suggest that time-varying risk aversion have significant impact for stock return volatility. In terms of out-of-sample forecasting performance, the empirical results indicate that the incorporation of time-varying risk aversion in the benchmark model can yield more accurate stock return volatility forecasts. Notably, the out-of-sample forecasting results confirm that our conclusions are robust when we apply alternative lag orders and alternative prediction evaluation periods. Finally, we study links between the prediction ability of time-varying risk aversion and the volatility of other stock indices and two kinds of crude oil, and find that the new predictor can effectively strengthen forecasting performance in most case. In view of the importance of volatility risk in the asset pricing process, our research is of great significance for financial asset participants.     

Stock volatility and trading

Both, rational and behavioral models predict that stock and market volatility affect trading by investors. Tax-induced trading hypothesis predicts that investors increase realization of capital losses short term and capital gains only long term as volatility increases. Behavioral models predict that disposition biases of holding on to losers and disposing of winners intensifies with volatility. We document that market and stock volatility influence stock trading. Evidence on trading in response to rise in market volatility supports tax-loss harvesting hypothesis – abnormal trading of losers increases and winners decreases. However, evidence on trading patterns conditional on individual stock volatility is in support of both tax-loss-harvesting and behavioral models: trading in both losing stocks (tax-loss-harvesting hypothesis) and winning stocks (disposition effect hypothesis) increases with rise in stock volatility.     

Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model

In this paper, we investigate the relation between time-varying risk aversion and renminbi exchange rate volatility using the conditional autoregressive range-mixed-data sampling (CARR-MIDAS) model. The CARR-MIDAS model is a range-based volatility model, which exploits intraday information regarding the intraday trajectory of the price. Moreover, the model features a MIDAS structure allowing for time-varying risk aversion to drive the long-run volatility dynamics. Our empirical results show that time-varying risk aversion has a significantly negative effect on the long-run volatility of renminbi exchange rate. Moreover, we observe that both intraday ranges and time-varying risk aversion contain important information for forecasting renminbi exchange rate volatility. The range-based CARR-MIDAS model incorporating time-varying risk aversion provides more accurate out-of-sample forecasts of renminbi exchange rate volatility compared to a variety of competing models, including the return-based GARCH, GARCH-MIDAS and GARCH-MIDAS incorporating time-varying risk aversion as well as range-based CARR, CARR-MIDAS and heterogeneous autoregressive (HAR), for forecast horizons of 1 day up to 3 months. This result is robust to alternative risk aversion measure, alternative MIDAS lags as well as alternative out-of-sample periods. Overall, our findings highlight the value of incorporating intraday information and time-varying risk aversion for forecasting the renminbi exchange rate volatility.     

Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities

This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment confirms that the procedure works well in practice. Implementing the procedure with actual S&P500 option-implied volatilities and high-frequency five-minute-based realized volatilities indicates significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns.     

Priced risk and asymmetric volatility in the cross section of skewness

We investigate the sources of skewness in aggregate risk factors and the cross section of stock returns. In an ICAPM setting with conditional volatility, we find theoretical time series predictions on the relationships among volatility, returns, and skewness for priced risk factors. Market returns resemble these predictions; however, size, book-to-market, and momentum factor returns are not always consistent with our predictions. We find evidence that size and book-to-market may be priced post-crisis but not in the decade before. Momentum does not appear priced by our test. We link aggregate risk and skewness to individual stocks and find empirically that the risk aversion effect manifests in individual stock skewness. Additionally, we find several firm characteristics that explain stock skewness. Smaller firms, value firms, highly levered firms, and firms with poor credit ratings have more positive skewness.     

Overconfidence and Disappointment in Decision‐making under Risk: The Triumph of Hope over Experience

Overconfidence in economic decision‐making is a well‐established phenomenon; however, assessment of its scale and impact are largely absent from the literature. This paper uses Bell's disappointment metric to quantify its impact on individual decision‐makers in a risky investment setting. We are able to quantify the sensitivity of disappointment to overconfidence. We examine disappointment in both deterministic and stochastic models, finding that in the most likely cases, individual investors experience lower disappointment when variance is higher. We are also able to quantify and compare calibration‐based overconfidence and better‐than‐average overconfidence. Contrary to prior research, we find that their magnitudes are the same. Copyright © 2016 John Wiley & Sons, Ltd.     

Contest success function with the possibility of a draw: Axiomatization

In imperfectly discriminating contests the contestants contribute effort to win a prize but the highest contributed effort does not necessarily secure a win. The contest success function (CSF) is the technology that translates an individual's effort into his or her probability of winning. This paper provides an axiomatization of CSF when there is the possibility of a draw (the sum of winning probabilities across all contestants does not add up to one).     

Market impact of international sporting and cultural events

We study market reaction to the announcements of the selected country hosting the Summer and Winter Olympic Games, the World Football Cup, the European Football Cup and World and Specialized Exhibitions. We generalize previous results analyzing a large number and different types of mega-events, evaluate the effects for winning and losing countries, investigate the determinants of the observed market reaction and control for the ex ante probability of a country being a successful bidder. Average abnormal returns measured at the announcement date and around the event are not significantly different from zero. Further, we find no evidence supporting that industries, that a priori were more likely to extract direct benefits from the event, observe positive significant effects. Yet, when we control for anticipation, the stock price reactions around the announcements are significant.     

Uncertain dynamics,correlation effects,and robust investment decisions

We analyze a firm׳s investment problem when the dynamics of project value and investment cost are uncertain. We provide an explicit solution using a robust method for an ambiguity averse firm taking this into account. Ambiguity aversion regarding a common risk factor impacts differently than ambiguity aversion regarding investment cost residual risk. Correlation between project value and investment cost matters; ambiguity aversion regarding common risk can decrease the investment probability only if correlation is positive. Ambiguity aversion regarding residual risk always increases the investment probability. When only project value is risky, volatility can monotonically decrease the investment threshold; this does not hold with the multiple prior method.     

News and noise: do investors react to stock split announcements differently during periods of high and low market volatility?

In this paper, we find evidence that stock split announcements have a greater wealth effect when market volatility, as measured by the VIX index, is low. This effect is driven primarily by small firms. These results support the hypothesis that when market volatility is high, signals sent by small firms are more likely to be obscured by noise than when market volatility is low.     

Innovation contests with temporary and endogenous monopoly rents

In this article, a Tullock contest success function is used to model an innovation contest with endogenous innovation height. We can prove stability for this endogenous prize contest. The winner of the contest gains a monopoly rent, which has two dimensions. In the first dimension the winning firm influences the innovation height. The second dimension is the life span of the temporary monopoly. This life span is determined by the contest designer, who can be asocial planner or the consumers. We find interior solutions in both cases, whereas consumers prefer a monopoly life span below the social optimum. Furthermore, the optimal number of firms in the contest is two.     

Robustness of stable volatility strategies

The paper analyzes the robustness of stable volatility strategies, i.e. strategies in which the portfolio weight of the stock is inversely proportional to its local volatility. These strategies are optimal for a CRRA investor if the stock follows a diffusion process, the expected excess return is proportional to its volatility, and the hedging demand is zero. We assess the performance of stable volatility strategies when these restrictive assumptions do not hold, in particular, when the risk premium is not proportional to volatility and when the stock price is subject to jumps. We find that stable volatility strategies are indeed robust or close to robust under a maxmin decision rule. In addition to our theoretical results, we perform a simulation analysis to evaluate strategies that scale the portfolio weight by the volatility, variance or a constant portfolio weight, and also analyze the strategies using empirical excess returns. Both analyses confirm the robustness of stable volatility strategies.     

Synergies in Wireless Telephony: Evidence from the Broadband PCS Auctions

We examine bid data from the first two broadband PCS spectrum auctions for evidence of value synergies. First, we estimate a benchmark regression for the determinants of final auction prices. Then, we include variables reflecting the extent to which bidders ultimately won or already owned the adjacent wireless properties. Consistent with geographic synergies in an ascending-bid auction, prices were higher when the highest losing bidder had adjacent licenses. The footprints of winning bidders suggest that they were often successful in realizing these synergies.     

Optimal growth under model uncertainty

The optimal growth of a wealth process toward a goal is studied under ambiguous markets with first- and second-order moment uncertainties relating to stock returns. Optimal strategies and value functions are solved explicitly. A verification theorem is proved to show that the results solve the original stochastic control problem. Quantitative analyses of the investment strategies indicate that a rational individual with ambiguity aversion reduces market participation when return and volatility are uncorrelated, while there is an exception for synchronous return and volatility. The welfare of shorting a discounted reward is computed, which demonstrates that in an ambiguous pricing economy, investors can generate a positive premium via appropriate asset allocations.     

Risk sharing and counter-cyclical variation in market correlations

I present a consumption-based dynamic asset pricing model in which international market correlations vary counter-cyclically over time. The driving force in the model is the time-varying effective risk aversion induced by external habit formation. Market returns are driven by fundamental outputs and discount rates. When risk aversion is high, the effect of discount rates on market returns rises with the market price of risk. To the extent that countries share risk, the cross-country correlation of discount rates exceeds the cross-country correlation of fundamental outputs. In bad times, market correlations rise as returns are mostly driven by discount rates. Thus, consistent with the empirical evidence, periods of high risk aversion are associated with high market correlations and high market volatility. After calibration, my model is consistent with the observed variation in market correlations, as well as other features of asset prices including the equity premium and market volatility.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Aggregate volatility expectations and threshold CAPM

We propose a volatility-based capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to changes in investors’ expectations regarding near-term aggregate volatility. Using a novel measure to proxy uncertainty about expected changes in aggregate volatility, i.e. monthly range of the VIX index (RVIX), we find that portfolio betas change significantly when uncertainty about aggregate volatility expectations is beyond a certain threshold level. Due to changes in their market betas, small and value stocks are perceived as riskier than their big and growth counterparts in bad times, when uncertainty about aggregate volatility expectations is high. The proposed model yields a positive and significant market risk premium during periods when investors do not expect significant uncertainty in near-term aggregate volatility. Our findings support a volatility-based time-varying risk explanation.     

The impact of major global events on volatility shifts: Evidence from the Asian crisis and 9/11

We analyze whether the Asian crisis and the terrorist attacks of September 11 caused permanent volatility shifts in the world stock markets. In doing so, we test for the presence of structural breaks in volatility during 1997–2002 by resorting to the iterative cumulative sum of squares (ICSS) algorithm and wavelet-based variance analysis.We find that the number of shifts detected by the two methods decreases substantially when both correlated volatility and inertia are taken into account. Specifically, the ICSS algorithm fails to find any breakpoints, while a wavelet-based variance test detects breakpoints at the high-frequency components of the filtered data.     

Risk aversion and business cycles: An empirical analysis

Unlike investors, who tend to maintain highly-diversified portfolios, private entrepreneurs usually lack access to complete risk-pooling for idiosyncratic risks, thus more directly internalize the cost of volatility. Risk aversion, however, modifies the optimal contract between entrepreneurs and lenders by incorporating the risk premium that entrepreneurs demand for the uninsurable risk: the private equity premium. Consequently, real shocks tend to be amplified as changes in entrepreneurs’ net worth affect the private equity premium and so the rental rate of capital, investment and output. This theoretical framework suggests that economies where the private entrepreneurial sector is a relatively larger, and therefore more vulnerable to uninsurable risk, all else equal, should present higher volatility. I test this prediction by (1) conducting a simple reduced-form analysis that shows that output volatility is negatively associated with the relative importance of the corporate vs. the privately-held sector; and (2) estimating the model's structural parameters. Intuitively, countries where private entrepreneurs are predominant and so risk aversion is likely to impose stronger impacts, positive risk aversion coefficients should be found. Results suggest that risk aversion is empirically more relevant for economies like Argentina, Brazil, Chile, Korea, Mexico and Thailand than for Canada, France, Germany, the U.K. and the U.S.     

Are low-frequency data really uninformative? A forecasting combination perspective

In this study, we investigate whether low-frequency data improve volatility forecasting when high-frequency data are available. To answer this question, we utilize four forecast combination strategies that combine low-frequency and high-frequency volatility models and employ a rolling window and a range of loss functions in the framework of the novel Model Confidence Set test. Out-of-sample results show that combination forecasts with GARCH-class models can achieve high forecast accuracy. However, the combination forecast methods appear not to significantly outperform individual high-frequency volatility models. Furthermore, we find that models that combine low-frequency and high-frequency volatility yield significantly better performance than other models and combination forecast strategies in both a statistical and economic sense.