Level Of Market Volatility Research Articles

Systematic factors, information release and market volatility

Recent several months have demonstrated historical levels of market volatility; sometimes attributed to changes in previously hypothesized systematic risk factors, however many times without any known reason other than the overreaction by market participants. Times like these make it even more important that we have a better understanding of how markets receive and evaluate new information about systematic risk factors such as macroeconomic variables. Despite a strong intuitive notion and established theoretical relationships that these risk factors should influence equity values, few studies have been able to establish this relationship empirically. Previous research has used cash-market prices for equity indices, but perhaps options on those indices are more sensitive to the new information released to the market by the announcement of macroeconomic variables, as suggested by numerous empirical studies supporting the hypothesis that option traders might be a better informed segment of the population. In...

Strategic Asset Allocation and Markov Regime Switch with GARCH Model

During the financial crisis of 2008, the S&P 500 Implied Volatility Index (VIX), known as the “fear gauge”, jumped to 80% of the highest level it has ever reached. Portfolio managers faced tremendous pressures in these environments of such high levels market volatility. Because it is well known that asset allocation dominates portfolio performances, this paper focuses on asset allocation strategies. It develops a strategic asset allocation solution for portfolio management under all conditions and at all levels of market volatility. The approach is to derive a dynamic optimal portfolio that is based on the well-known asset allocation Black–Litterman [1991, 1992] framework. In addition, this paper proposes a methodology that considers the features of volatility regime-switching over time. This new strategic framework allows portfolio managers to derive a systematically optimal portfolio in a timely, accurate fashion.

The Cross-Section of Average Delta-Hedge Option Returns Under Stochastic Volatility

Existing evidence indicates that average returns of purchased market-hedge S&P 500 index calls, puts, and straddles are non-zero but large and negative, which implies that options are expensive. This result is intuitively explained by means of volatility risk and a negative volatility risk premium, but there is a recent surge of empirical and analytical studies which also attempt to find the sources of this premium. An important question in the line of a priced volatility explanation is if a standard stochastic volatility model can also explain the cross-sectional findings of these empirical studies. The answer is fairly positive. The volatility elasticity of calls and puts is several times the level of market volatility, depending on moneyness and maturity, and implies a rich cross-section of negative average option returns -- even if volatility risk is not priced heavily, albeit negative. We introduce and calibrate a new measure of option overprice to explain these results. This measure is robust to jump risk if jumps are not priced.

The cross-section of average delta-hedge option returns under stochastic volatility

Existing evidence indicates that average returns of purchased market-hedge S&P 500 index calls, puts, and straddles are non-zero but large and negative, which implies that options are expensive. This result is intuitively explained by means of volatility risk and a negative volatility risk premium, but there is a recent surge of empirical and analytical studies which also attempt to find the sources of this premium. An important question in the line of a priced volatility explanation is if a standard stochastic volatility model can also explain the cross-sectional findings of these empirical studies. The answer is fairly positive. The volatility elasticity of calls and puts is several times the level of market volatility, depending on moneyness and maturity, and implies a rich cross-section of negative average option returns—even if volatility risk is not priced heavily, albeit negative. We introduce and calibrate a new measure of option overprice to explain these results. This measure is robust to jump risk if jumps are not priced.

Tests of Idiosyncratic Risk: Informed Trading Versus Noise and Arbitrage Risk

We investigate the market efficiency implications of firm-specific return variation measured by absolute idiosyncratic volatility. We find that the absolute idiosyncratic volatility (the variance of the residual from an asset-pricing model) displays a positive and robust relationship to mispricing (based on both accounting information and alternatively abnormal stock returns). Thus, we find that larger values of absolute idiosyncratic volatility reflect an increasing role of noise traders. Previous literature has produced similar - or opposing - results. We deepen our understanding of the reasons for the lack of consensus in the previous literature by investigating the interaction between market volatility, idiosyncratic volatility, and R-squared. Thus, we show that 1) market volatility by itself is associated with mispricing, 2) absolute idiosyncratic volatility is associated with mispricing even when controlling for market volatility, 3) the strength of the association between absolute idiosyncratic volatility and mispricing depends on the level of market volatility, and 4) absolute and relative measures of idiosyncratic volatility have opposing associations with mispricing.

The implied volatility term structure of stock index options

This paper tests the expectations hypothesis of the term structure of implied volatility for several national stock market indexes. The tests indicate that the slope of at-the-money implied volatility over different maturities has predictive ability for future short-dated implied volatility, although not to the extent predicted by the expectations hypothesis. The low forecast power may be due to failure to control for a risk premium in the prices of the options. Evidence is presented that a time-varying risk premium proportional to the level of market volatility is consistent with the results.

Changes in the dynamic behavior of emerging market volatility: Revisiting the effects of financial liberalization

In this paper we test whether the dynamic behavior of stock market volatility in six emerging economies has changed over the period 1976:01–2004:12. This period corresponds to years of profound development of both the financial and the productive sides in these emerging countries, but also to the years of the major financial crises. Our analysis suggests that changes in volatility behavior, while indeed present, may have been overstated in the past: simple specifications account for most of the dynamics of stock market volatility and therefore become powerful tools for volatility analysis. Additionally, we show that financial liberalization of emerging markets has generally reduced the level of market volatility and its sensitivity to news.

PORTFOLIO INSURANCE AND VOLATILITY REGIME SWITCHING

A new equilibrium model of portfolio insurance is presented in order to study the volatility effects of dynamic insurance strategies. While prior research has focused on the relationship between portfolio insurance and the overall level of market volatility, this article shows that the salient feature of portfolio insurance is volatility regime switching. Regime switching is shown to be a necessary condition for portfolio insurance, which provides a new explanation for the pervasive volatility clustering effect that is found in most equity markets. The equilibrium involves a free boundary and the local time of the equilibrium price process at the free boundary plays an important role in solving the model.

IDENTIFYING REGIME CHANGES IN MARKET VOLATILITY

AbstractA casual inspection of a graph of volatility indexes over time indicates that volatility has undergone infrequent, but significant, shifts in its average level. The purpose of this article is to test for multiple structural breaks in the mean level of market volatility measured by the VIX and VXO, and to identify statistically the dates of these mean shifts. We find evidence of three distinct periods: pre‐1992, 1992–1997, and post‐1997. We find that the mean volatility, as well as its standard deviation, was lowest during 1992–1997. Our findings provide statistical evidence consistent with popular beliefs that market volatility changes over time.

The Structural Break in the Equity Premium

This article uses Bayesian marginal likelihood analysis to compare univariate models of the stock return behavior and test for structural breaks in the equity premium. The analysis favors a model that relates the equity premium to Markov-switching changes in the level of market volatility and accommodates volatility feedback. For this model, there is evidence of a one-time structural break in the equity premium in the 1940s, with no evidence of additional breaks in the postwar period. The break in the 1940s corresponds to a permanent reduction in the general level of stock market volatility. Meanwhile, there appears to be no change in the underlying risk preferences relating the equity premium to market volatility. The estimated unconditional equity premium drops from an annualized 12% before to the break to 9% after the break.

Optimal trading frequency for active asset management: Evidence from technical trading rules

The investment horizon or expected trading frequency is an important factor in investment decision making, but there is little literature in this field. The primary motivation for this paper is to find the optimal trading frequency for different assets in the context of active asset management by applying technical trading rules, the most widely used forecasting technique in financial markets. In addition to the simple moving average crossover system, two volatility filters are also applied, where a different trading strategy is proposed when market volatility is high. A model switch strategy is also introduced, where signals from different technical rules are adopted at different levels of market volatility. The results show that the addition of the two volatility filters and the introduction of a model switch strategy add value to the model's performance in terms of annualised return, Sharpe ratio and maximum drawdown. Significant improvement is found at both the single asset and portfolio levels. Although the results for the optimal trading frequencies differ for different assets, similar results have been achieved between the two stock indexes S&P500 and STOXX50 and between FX currency rates. In the case of stock indexes, the optimal trading frequency is about two to four trades per year, while for the FX currency rates, it is about ten to 20 trades per year.

Estimating the market risk premium

This paper provides a method for estimating the market risk premium that accounts for shifts in investment opportunities by explicitly modeling the underlying process governing the level of market volatility. I find that approximately 50% of the measured risk premium is related to the risk of future changes in investment opportunities. Evidence of a structural shift in the underlying volatility process suggests that the simple historical average of excess market returns may substantially overstate the magnitude of the market risk premium for the period since the Great Depression.

Do tracking errors reliably estimate portfolio risk?

An active equity investment strategy aims to select a portfolio of stocks that are likely to outperform the market without exposing the investor to unacceptable levels of risks. The purpose of this paper is to assess whether tracking errors have historically accurately quantified these levels of risk in the UK. The results suggest that over the short-term horizons, tracking errors have performed reasonably well, but over the longer-term horizons (>1 year), they have tended to underestimate risk. We find evidence that this underestimation can be explained by a combination of transitory market momentum effects and changes to overall levels of market volatility. We also investigate how investment strategies of sector neutrality reduce levels of portfolio risk.

Portfolio Construction Techniques for Asia

Asian equities are a small fraction of the global equity markets but are a good source of diversification and worthy of consideration in an asset allocation context. Asset allocation within the Asian markets has its own unique issues, such as high levels of market volatility, appropriate allocation techniques, and a lack of client familiarity with strategic asset allocation concepts. These peculiarities can be overcome by common approaches to asset allocation, including mean–variance methodologies and risk–return distributions created through bootstrapping and Monte Carlo simulation.

Estimating the Market Risk Premium

This paper provides a methodology for estimating the market risk premium based on the underlying process governing the level of market volatility. My model provides a test for a structural shift in the historical risk premium and an unbiased estimate of its value. I provide evidence of a structural shift in the volatility process following the 1930s that implies an upward bias in ex post realized returns during the subsequent period. Controlling for this bias, my estimate of the market risk premium for the period after 1940 is 5.9% over the yield on Treasury bills. My model also provides a lower-bound on forward-looking estimates of the current risk premium of 4.2% over the yield on Treasury bills.

State-Dependent Volatility and the Market Risk Premium

The market risk premium is estimated based on the equilibrium relationship between risk and expected returns in an intertemporal setting characterized by periods of distinctly different levels of risk. In this environment of discrete volatility states, expected returns compensate investors for current volatility risk as well as the risk associated with a change in the level of market volatility. More importantly, expected returns include the expected change in market value associated with a change in the level of market risk. For this reason, average realized returns within each volatility state do not reflect the risk premium required by investors for state specific risk. By explicitly modelling the process governing the evolution of volatility states, I recover the required risk premium for each volatility state. The dynamic nature of the volatility process implies a term structure of expected forward risk premia that is dependent upon the current level of market volatility. During the period from 1926 to 1997, my estimates indicate that the economy is likely to have been in the high-volatility state approximately 13 percent of the time and that there is a strong statistical relationship between these high-volatility episodes and business cycle contraction periods. In addition, I find evidence of a structural shift in the volatilty process corresponding to the end of the Great Depression Era. As a result of this structural change, the likelihood of being in the high-volatility state decreases dramatically after 1940, implying a significant decrease in the level of market risk. Consistent with this reduction in risk, I find evidence of an abnormal increase in market values during the period from 1940 to 1960. After controlling for this windfall capital gain, my analysis suggests that the long-run market risk premium for the period after 1940 is substantially less than that implied by the simple historical average of excess market returns.