Financial Modeling in a Fast Mean-Reverting Stochastic Volatility Environment

We present a derivative pricing and estimation methodology for a class of stochastic volatility models that exploits the observed 'bursty' or persistent nature of stock price volatility. Empirical analysis of high-frequency S&P 500 index data confirms that volatility reverts slowly to its mean in comparison to the tick-by- tick fluctuations of the index value, but it is fast mean- reverting when looked at over the time scale of a derivative contract (many months). This motivates an asymptotic analysis of the partial differential equation satisfied by derivative prices, utilizing the distinction between these time scales. The analysis yields pricing and implied volatility formulas, and the latter provides a simple procedure to 'fit the skew' from European index option prices. The theory identifies the important group parameters that are needed for the derivative pricing and hedging problem for European-style securities, namely the average volatility and the slope and intercept of the implied volatility line, plotted as a function of the log- moneyness-to-maturity-ratio. The results considerably simplify the estimation procedure. The remaining parameters, including the growth rate of the underlying, the correlation between asset price and volatility shocks, the rate of mean-reversion of the volatility and the market price of volatility risk are not needed for the asymptotic pricing formulas for European derivatives, and we derive the formula for a knock-out barrier option as an example. The extension to American and path-dependent contingent claims is the subject of future work. This revised version was published online in August 2006 with corrections to the Cover Date.     

Modeling Time-Varying Correlations in Volatility Between BRICS and Commodity Markets

This article investigates the asymmetric and long memory volatility properties and dynamic conditional correlations (DCCs) between Brazilian, Russian, Indian, Chinese, and South African (BRICS) stock markets and commodity (gold and oil) futures markets, using the trivariate DCC-fractionally integrated asymmetric power autoregressive conditional heteroskedasticity (FIAPARCH) model. We identify significant asymmetric and long memory volatility properties and DCCs for pairs of BRICS stock and commodity markets, and variability in DCCs and Markov Switching regimes during economic and financial crises. Finally, we analyze optimal portfolio weights and time-varying hedge ratios, demonstrating the importance of overweighting optimal portfolios between BRICS stock and commodity assets.     

The Predictive Power of the French Market Volatility Index: A Multi Horizons Study

The main purpose of this paper is to examine empirically the time series properties of the French Market Volatility Index (VX1). We also examine the VX1's ability to forecast future realized market volatility and finds a strong relationship. More importantly, we show how the index can be used to generate volatility forecasts over different horizons and that these forecasts are reasonably accurate predictors of future realized volatility.     

Periodic Stochastic Volatility and Fat Tails

This article provides a comprehensive analysis of the size andstatistical significance of the day of the week, month of theyear, and holiday effects in daily stock index returns and volatility.We employ data from the Dow Jones Industrial Average (DJIA),the S&P 500, the S&P MidCap 400, and the S&P SmallCap600 in order to test whether the seasonal patterns of mediumand small firms are similar to those of large firms. Using formalhypothesis tests based on bootstrapping, we demonstrate thatthere are more significant calendar effects in volatility thanin expected returns, especially for the two large cap indices.More importantly, we introduce the periodic stochastic volatility(PSV) model for characterizing the observed seasonal patternsof daily financial market volatility. We analyze the interactionbetween seasonal heteroskedasticity and fat tails by comparingthe performance of Gaussian PSV and fat-tailed PSVt specificationsto the plain vanilla SV and SVt benchmarks. Consistent withour model-free results, we find strong evidence of seasonalperiodicity in volatility, which essentially eliminates theneed for a fat-tailed conditional distribution, and is robustto the exclusion of the crash of 1987 outliers.     

Aggregation of Nonparametric Estimators for Volatility Matrix

An aggregated method of nonparametric estimators based on time-domainand state-domain estimators is proposed and studied. To attenuatethe curse of dimensionality, we propose a factor modeling strategy.We first investigate the asymptotic behavior of nonparametricestimators of the volatility matrix in the time domain and inthe state domain. Asymptotic normality is separately establishedfor nonparametric estimators in the time domain and state domain.These two estimators are asymptotically independent. Hence,they can be combined, through a dynamic weighting scheme, toimprove the efficiency of volatility matrix estimation. Theoptimal dynamic weights are derived, and it is shown that theaggregated estimator uniformly dominates volatility matrix estimatorsusing time-domain or state-domain smoothing alone. A simulationstudy, based on an essentially affine model for the term structure,is conducted, and it demonstrates convincingly that the newlyproposed procedure outperforms both time- and state-domain estimators.Empirical studies further endorse the advantages of our aggregatedmethod.     

Price and Volatility Spillovers in the Case of Stock Markets Located in Different Time Zones

This paper investigates the interdependence of price volatility across the U.S. stock market and two emerging markets: Poland and Hungary. Using daily data for countries located in different time zones, we point out the problems caused by the presence of nonsynchronous trading effects. To address this problem we use open-to-close logarithmic returns of major stock market indexes. The asymmetric impact of good and bad news is described by a multivariate exponential general autoregressive conditional heteroskedastic model. We investigate the sample from May 2004 to December 2011. The evidence is that the U.S. prices spill over to other markets. Our results show no pronounced volatility spillovers among the three examined markets. Moreover, we observe the presence of negative asymmetry in the case of all markets.     

Positivity Conditions for a Bivariate Autoregressive Volatility Specification

We derive necessary and sufficient conditions for the positivedefiniteness of the predicted volatility matrix in a bivariateautoregressive volatility specification. These nonlinear inequalityrestrictions have strong implications in terms of causalitybetween volatilities and covolatilities.     

Testing for Volatility Jumps in the Stochastic Volatility Process

This paper considers the stochastic volatility process with contemporaneous and correlated jumps in returns and volatility, which was proposed by Eraker, B., Johannes, M. and Poison, N. G. (Journal of Finance 53, 2003, 1269--1300) and proposes the Lagrange multiplier test for the presence of jumps in volatility. The test statistic is derived by regarding the degenerate density of volatility jumps with zero variance under the null as Dirac's delta function. The correlation parameter between jumps, which is a nuisance parameter unidentified under the null, is cancelled out in this test statistic and hence the test is free from the Davies problem (Davies, R. B., Biometrika 64, 1977, 247–254).     

Capital Asset Pricing Model and Stochastic Volatility: A Case Study of India

The existing literature demonstrates that under a general equilibrium model, the performance of the Capital Asset Pricing Model (CAPM) can be improved significantly by using conditional consumption and market return volatilities as factors. This article tests the validity of these factors explaining stock return differences using a less developed country (India) as a case study. While the earlier studies used panel data to test CAPM, we use portfolios sorted by size and book-to-market equity (BE/ME) ratio. We found that conditional volatility has a limited effect on firms with large capitalization but a significant impact on small-growth and small-value firms.     

How Persistent is Stock Return Volatility? An Answer with Markov Regime Switching Stochastic Volatility Models

Abstract:  We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed.     

Modelling the Stochastic Dynamics of Volatility for Equity Indices

The paper develops a class of continuous timestochastic volatility models, which generate asset price returnsthat are approximately Student t distributed. Using thecriterion of local risk minimisation in an incomplete marketsetting, option prices are computed. It is shown that impliedvolatility smile and skew patterns of the type often observed inthe markets can be obtained from this class of stochasticvolatility models.     

人民币汇率波动与我国通胀、产出波动：一个扩展泰勒曲线的研究

发挥市场供求对汇率的调节作用,增强人民币汇率双向浮动弹性,是当前我国汇率体制改革的主要方向。本文在泰勒曲线的框架下考察人民币汇率波动对我国宏观经济波动和货币政策实施的影响。通过实证研究发现,1994—2006年通货膨胀波动对人民币汇率波动是不敏感的,人民币汇率传递效应不显著,人民币汇率波动对宏观经济波动没有显著的影响;2007年以后人民币汇率波动推动泰勒曲线向内移动,因此更大的人民币汇率弹性对货币政策传导和货币政策有效性是有利的,逐步扩大的人民币汇率弹性区间对我国宏观经济运行是适宜和可接受的。另外人民币汇率波动也使得泰勒曲线更加陡峭,稳定通货膨胀所导致的产出缺口波动减小了,因而更有利于货币政策当局追求一个低而稳定的通胀目标。     

A Comparison of Option Prices Under Different Pricing Measures in a Stochastic Volatility Model with Correlation

This paper investigates option prices in an incomplete stochastic volatility model with correlation. In a general setting, we prove an ordering result which says that prices for European options with convex payoffs are decreasing in the market price of volatility risk.As an example, and as our main motivation, we investigate option pricing under the class of q-optimal pricing measures. The q-optimal pricing measure is related to the marginal utility indifference price of an agent with constant relative risk aversion. Using the ordering result, we prove comparison theorems between option prices under the minimal martingale, minimal entropy and variance-optimal pricing measures. If the Sharpe ratio is deterministic, the comparison collapses to the well known result that option prices computed under these three pricing measures are the same.As a concrete example, we specialize to a variant of the Hull-White or Heston model for which the Sharpe ratio is increasing in volatility. For this example we are able to deduce option prices are decreasing in the parameter q. Numerical solution of the pricing pde corroborates the theory and shows the magnitude of the differences in option price due to varying q.JEL Classification: D52, G13     

Leverage and Volatility Feedback Effects in High-Frequency Data

We examine the relationship between volatility and past andfuture returns using high-frequency aggregate equity index data.Consistent with a prolonged "leverage" effect, we find the correlationsbetween absolute high-frequency returns and current and pasthigh-frequency returns to be significantly negative for severaldays, whereas the reverse cross-correlations are generally negligible.We also find that high-frequency data may be used in more accuratelyassessing volatility asymmetries over longer daily return horizons.Furthermore, our analysis of several popular continuous-timestochastic volatility models clearly points to the importanceof allowing for multiple latent volatility factors for satisfactorilydescribing the observed volatility asymmetries.     

The Effect of Futures Market Volume on Spot Market Volatility

There has been considerable interest, both academic and regulatory, in the hypothesis that the higher is the volume in the futures market, the greater is the destabilizing effect on the stock market. We show that conventional approaches, such as adding exogenous variables to GARCH models, may lead to false inferences in tests of this question. Using a stochastic volatility model, we show that, contrary to regulatory concern and the results of other papers, contemporaneous informationless futures market trading has no significant effect on spot market volatility.     

Volatility Spillover in Regional Emerging Stock Markets: A Structural Time-Series Approach

Volatility spillovers among the stock markets of Bahrain, Kuwait, and Saudi Arabia are investigated using the concept of stochastic volatility and structural time-series modeling. The results reveal volatility spillovers, in which the Kuwait market plays the major role. It is also found that volatility in one market cannot be explained fully in terms of volatility in the other two markets, but that, out of the three markets, the Kuwait market seems to be the most influential. Some explanations are put forward for why this is the case.     

Volatility Linkages Among Gold Futures in Emerging Markets

We aim to detect the cross-border volatility linkages among gold futures in emerging markets, which still remain an untapped area. China, India, Japan, Taiwan, Turkey, and U.S. futures markets are included in the sample. The volatility linkage analyses confirm the existence of volatility transmission among the majority of the sample countries’ gold futures. This article carries vital inferences and implications for policy makers and investors. The policy making is particularly important for China, which is a relatively isolated market. From investors’ perspective, the results indicate that the risk diversification and cross-market hedging opportunities in the emerging gold futures markets are quite limited.     

A Complete Markovian Stochastic Volatility Model in the HJM Framework

This paper considers a stochastic volatility version of the Heath, Jarrow and and Morton (1992) term structure model. Market completeness is obtained by adapting the Hobson and Rogers (1998) complete stochastic volatility stock market model to the interest rate setting. Numerical simulation for a special case is used to compare the stochastic volatility model against the traditional Vasicek (1977) model.     

The Impact of the Volatility of Monetary Policy Shocks

This paper studies the impact of the volatility of monetary policy using a structural vector auroregression (SVAR) model enriched along two dimensions. First, it allows for time‐varying variance of monetary policy shocks via a stochastic volatility specification. Second, it allows a dynamic interaction between the level of the endogenous variables in the VAR and the time‐varying volatility. The analysis establishes that the nominal interest rate, output growth, and inflation fall in reaction to an increase in the volatility of monetary policy. The analysis also develops a dynamic stochastic general equilibrium model enriched with stochastic volatility to monetary policy that generates similar responses and provides a theoretical underpinning of these findings.     

Return and Volatility Spillovers Between Large and Small Stocks in the UK

Abstract:  This paper investigates return and volatility spillover effects between the FTSE 100, FTSE 250 and FTSE Small Cap equity indices using the multivariate GARCH framework. We find that return and volatility transmission mechanisms between large and small stocks in the UK are asymmetric. In particular, there are significant spillover effects in both returns and volatility from the portfolios of larger stocks to the portfolios of smaller stocks. For volatility, there is also evidence of limited feedback from the portfolios of smaller stocks to the portfolios of larger stocks, although sub-period analysis suggests that this is to some extent period-specific. Simulation evidence shows that non-synchronous trading potentially explains some, but not all, of the spillover effects in returns, and that it explains none of the spillover effects in volatility. These results are consistent with a market in which information is first incorporated into the prices of large stocks before being impounded into the prices of small stocks.