Volatility Structure Research Articles

Impact of Trading Volume on Prediction of Stock Market Development

The paper focuses on the influence of trading volume on quality of prediction of stock market development. The main objective of this article is to assess the influence of stock trading volume level on quality of prediction with use of technical analysis. The research was applied on stocks included in the S & P 500 index. Based on average daily trading volume, three aggregate indexes were constructed. The dynamics of index return volatility was modeled by GARCH-class models. GARCH(1,1), GJR and EGARCH models were estimated for each time series. The in-sample evidence indicated that the return volatility of the indexes can be characterized by significant persistence and asymmetric effects. The best estimate of each model was produced for the index of stocks with the highest average trading volume.However the result could differ based on the observed period, the volatility structure of the examined data supports the idea that influential investors respond to various shocks in the market primarily by closing or opening their largest position.The importance of the level of trading volume for the prediction of financial time series development was shown in the paper. This finding could help generate such volatility structure of time series which would allow to explain development of the time series by various models with better results.

Capital distribution and portfolio performance in the mean-field Atlas model

We study a mean-field version of rank-based models of equity markets such as the Atlas model introduced by Fernholz in the framework of Stochastic Portfolio Theory. We obtain an asymptotic description of the market when the number of companies grows to infinity. Then, we discuss the long-term capital distribution. We recover the Pareto-like shape of capital distribution curves usually derived from empirical studies, and provide a new description of the phase transition phenomenon observed by Chatterjee and Pal. Finally, we address the performance of simple portfolio rules and highlight the influence of the volatility structure on the growth of portfolios.

It's Spending that Matters: From Robust Control Theory to Practical Heuristics

The problems caused by state and local revenue volatility are inherently challenging. The State of Oregon has an especially volatile revenue structure, which can cause all sorts of problems as the state moves through the business cycle. Nevertheless, we have concluded that these problems are caused less by revenue volatility per se than by adjusting spending up and down to match current revenue flows, from growing spending at unsustainable rates during booms and cutting back precipitously during busts. The mild recession of 2001-2 had a particularly severe aftermath in Oregon. This event shocked the state’s policy makers into addressing the revenue volatility issue, making sense of it, and taking steps that greatly mitigated the adverse effects of the ‘Great Recession.’ This essay describes three things: the public-policy processes that produced this moderately happy outcome, my own voyage of discovery as I observed/participated in these processes, and a set of practical mechanisms states can use to stabilize spending.

Terrorist Attacks, Saber Rattling, Other Crisis Events and American Depository Receipt Returns and Volatility: If We Stop Shopping, the Evil Doers Win

I examine the impact of 51 terrorist attacks, three wars, an assassination, and other national security-related shocks on the returns of American Depository Receipts (ADRs). ADRs are securities that trade on American markets that entitle investors to the cash flows (denominated in dollars) of foreign firms. I find that small-scale terrorist attacks that receive little media attention do not affect ADR returns. Large scale terrorist attacks, the assassination of Benazir Bhutto, highly provocative actions by North Korea, and small-scale wars affect ADR returns while natural disasters do not. The negative effect on ADRs is short lived. I also examine the volatility structures of two ADR indexes (Korea and India) and their underlying home market indexes. I find that there is bi-directional information flow between the ADR markets and the home market indexes. I find evidence that various political shocks affect the volatility structures of these indexes, both ADR and home market indexes.

The Financial Crisis and the Behavior of S&P 500 Index Option Prices

Stronger volatility skew and smile effects accompanied by a risk-neutral distribution that is closer to the Normal seem unconventional at first thought. But that is what we find during the financial crisis, with the unconventionally high risk-neutral volatility level playing a major role. Additionally, the term structure of implied volatility became inverted (negatively sloped) during the crisis, driven by the inversion of the term structure of risk-neutral volatility and by the rise in the shorter term unconditional volatility. The term structures of risk-neutral skewness and kurtosis appear relatively flat with only negligible change during the crisis. The scale and its term structure rather than the shape factors of the unconditional risk neutral distribution to maturity thus appear more important for the structure of crisis time option prices.

Chasing market dislocation: The LIBOR shifted model

We introduce a flexible multivariate extension to the LIBOR market model that is analytically tractable. The flexibility of the model is a solution to the interest rate market dislocation that happened on the credit crunch aftermath in August 2007. The extension is based on a parameter uncertainty modeled through a random variable whose value is drawn at an infinitesimal time after zero. The proposed shift captures the skew commonly seen in the cap market, whereas the uncertain volatility component of the model allows us to obtain more general implied volatility structures. We show how this model can be calibrated to cap prices. We also propose an analytical approximated formula to price swaptions from the cap calibrated model. Finally, we build a bridge between the caps and swaptions markets by calibrating the correlation structure to swaption prices, and analysing some implications of the calibrated model parameters.

Implied Adjusted Volatility by Leland Option Pricing Models: Evidence from Australian Index Options

With the implied volatility as an important factor in financial decision-making, in particular in option pricing valuation, and also the given fact that the pricing biases of Leland option pricing models and the implied volatility structure for the options are related, this study considers examining the implied adjusted volatility smile patterns and term structures in the S&P/ASX 200 index options using the different Leland option pricing models. The examination of the implied adjusted volatility smiles and term structures in the Australian index options market covers the global financial crisis in the mid-2007. The implied adjusted volatility was found to escalate approximately triple the rate prior the crisis.

On the comparison of Schwartz and Smith’s two- and three-factor models on commodity prices

Since Schwartz and Smith (2000) published their study on two-factor model on commodity prices, many studies have used this model and others have extended it. The authors also proposed the three-factor model due to the poor fitting of the two-factor one on long-term futures prices. At that time the authors had only long-term prices from a private source to calibrate, test and compare these models. No public data on long-term future contracts were available. On the other hand, during the last decade the commodity prices soared as did the liquidity of long-term contracts. This means that the interest of the agents in the management of their risk on long-term positions increased the same way and this is the motivation for this study. In this article, we revisit the comparison between two- and three-factor models using public data for short- and long-term contracts (we use up to the 67-month-ahead contract). We also provide a detailed derivation of the three-factor model differently from that of the original article. Following the original article of Schwartz and Smith, we used oil futures prices traded on the New York Mercantile Exchange to calibrate the model. The results show a better fit of the three-factor model for the term structure of prices and volatilities mainly for long maturities contracts, while the two-factor model in most portions of the curve underestimates the risk premiums. This type of analysis is important not only for daily agents negotiating the physical commodities through long-term contracts but also for investment decisions on development of real projects.

Investment timing under hybrid stochastic and local volatility

We consider an investment timing problem under a real option model where the instantaneous volatility of the project value is given by a combination of a hidden stochastic process and the project value itself. The stochastic volatility part is given by a function of a fast mean-reverting process as well as a slowly varying process and the local volatility part is a power (the elasticity parameter) of the project value itself. The elasticity parameter controls directly the correlation between the project value and the volatility. Knowing that the project value represents the market price of a real asset in many applications and the value of the elasticity parameter depends on the asset, the elasticity parameter should be treated with caution for investment decision problems. Based on the hybrid structure of volatility, we investigate the simultaneous impact of the elasticity and the stochastic volatility on the real option value as well as the investment threshold.

Value-at-risk analysis of the asymmetric long-memory volatility process of dry bulk freight rates

This study aims to apply value-at-risk (VaR) models to evaluate the risk of dry bulk freight rates when there is an asymmetric long-memory volatility process. The VaR estimations as well as expected shortfalls for both short and long trading positions are conducted. We use the Fractionally Integrated GARCH, Hyperbolic GARCH and Fractionally Integrated APARCH models to analyse the performance of the VaR models with the normal, Student-t and skewed Student-t distributions. Empirical results suggest that precise VaR estimates may be obtained from an asymmetric long-memory volatility structure with the skewed Student-t distribution. Moreover, the asymmetric FIAPARCH model outperforms than other models in out-of-sampling forecasting. Therefore, our findings provide a more accurate estimation of VaR for dry bulk freight rates. These results present several potential implications for dry bulk freight market risk quantification and hedging strategies.

Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model

Appropriate modelling of the process of volatility has implications for portfolio selection, the pricing of derivative securities and risk management. Further, a large body of research has suggested that both long memory and structural changes simultaneously characterize the structure of financial returns volatility. Given this, in this article, we aim to model conditional volatility of the returns of the Dow Jones Islamic Market World Index (DJIM), interest on which has come to the fore following the need for renovation of the conventional financial system, in the wake of the recent global financial crisis. To model the conditional volatility of the DJIM returns, accounting for both long memory and structural changes, we allow the parameters in the conditional variance equation of the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model to be time dependent, such that the parameters evolve smoothly over time based on a logistic smooth transition function, yiel...

Implied Volatility and Forward Price Term Structures

This paper discusses the relation between forward price models (FPM) and the so called implied volatility term structure (VTS). We start by considering the case of pure deterministic forward price volatilities and suppose both forward contracts and at-the-money (ATM) options, on a same underlying, are liquidly traded in the market. We, then, derive no arbitrage conditions between the functional form of the ATM implied VTS and the functional form of forward price volatilities. We conclude the first part by characterizing a parametric family of ATM implied VTS that is compatible with a finite dimensional realization (FDR) of forward prices. Finally, we consider the possibility of stochastic forward price volatilities and derive a no arbitrage drift condition that must hold for the dynamics of ATM implied VTS.

THE CARMA INTEREST RATE MODEL

In this paper, we present a multi-factor continuous-time autoregressive moving-average (CARMA) model for the short and forward interest rates. This model is able to present an adequate statistical description of the short and forward rate dynamics. We show that this is a tractable term structure model and provides closed-form solutions to bond prices, yields, bond option prices, and the term structure of forward rate volatility. We demonstrate the capabilities of our model by calibrating it to a panel of spot rates and the empirical volatility of forward rates simultaneously, making the model consistent with both the spot rate dynamics and forward rate volatility structure.

Empirical analysis of structural change in Credit Default Swap volatility

The purpose of this paper is to study the structural change in Credit Default Swap volatility. We use statistical properties and a network approach to better understand the behavior of CDS volatility. We hypothesize that structural change occurs in CDS index during a financial crisis and it requires subperiod analysis, rather than full period analysis, to investigate properly. Our results show that the probability of large volatility is related to the structure of volatility but it is more significantly related to the size of volatility. Both the memory property and the size of volatility are confirmed to have dependence on the structure of volatility. The linked degree of CDS volatilities is highly related to the probability of large volatility and its predictability, regardless of structural change in volatility. Another interesting result is that the CDS volatility of a country is more related to the behavior of other volatilities, not the geographical location.

Measuring level of risk exposure in tanker shipping freight markets

This is an attempt to study the volatility structure of the tanker freight market and its exposure to market shocks. Therefore, we introduce a two state regime to investigate the possibility of two different volatility structures in shipping tanker freight markets. Empirical evidence is found that in general terms, shipping tanker freight returns, shift between two regimes, a high volatility regime and a low volatility regime and that market shocks in general increase the volatility of freight returns and has a lasting effect. In regards to measuring freight risk, it seams that semi-parametric approaches are appropriate methods for measuring level of risk exposure for shipping freight markets.

A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix

In this article, we consider a novel regression model with observed factors. To allow for the prediction of future observations, we model the observed factors using a flexible multivariate stochastic volatility (MSV) structure with separate dynamics for the volatilities and the correlation matrix. The correlation matrix of the factors is time varying, and its evolution is described by an inverse Wishart process. We develop an estimation procedure based on Bayesian Markov chain Monte Carlo methods, which has two major advantages compared to existing methods for similar models in the literature. First, the procedure is computationally more efficient. Second, it can be applied to calculate the predictive distributions for future observations. We compare the proposed model with other multivariate volatility models using Fama-French factors and portfolio weighted return data. The result shows that our model has better predictive performance.

Conditional Markov regime switching model applied to economic modelling

In this paper we discuss the calibration issues of regime switching models built on mean-reverting and local volatility processes combined with two Markov regime switching processes. In fact, the volatility structure of these models depends on a first exogenous Markov chain whereas the drift structure depends on a conditional Markov chain with respect to the first one. The structure is also assumed to be Markovian and both structure and regime are unobserved. Regarding this construction, we extend the classical Expectation–Maximization (EM) algorithm to be applied to our regime switching model. We apply it to economic data (Euro/Dollar (USD) foreign exchange rate and Brent oil price) to show that such modelling clearly identifies both mean reverting and volatility regime switches. Moreover, it allows us to make economic interpretations of this regime classification as in some financial crises or some economic policies.

Jumps in Bond Yields at Known Times

We construct a no-arbitrage term structure model with jumps in the entire state vector at deterministic times but of random magnitudes. Jump risk premia are allowed for. We show that the model implies a closed-form representation of yields as a time-inhomogenous affine function of the state vector. We apply the model to the term structure of US Treasury rates, estimated at the daily frequency, allowing for jumps on days of employment report announcements. Our model can match the empirical fact that the term structure of interest rate volatility has a hump-shaped pattern on employment report days (but not on other days). The model also produces patterns in bond risk premia that are consistent with the empirical finding that much of the time-variation in excess bond returns accrues at times of important macroeconomic data releases.

Transmission of Financial Stress in Europe: The Pivotal Role of Italy and Spain, but not Greece

This paper proposes a stochastic volatility model to measure sovereign financial distress. It examines how key European sovereign credit default swap (CDS) spreads affect each other; specifically, the paper analyses the volatility structure of Germany, Greece, Ireland, Italy, Spain and Portugal. The stability of Germany is a close proxy for the resilience of the euro area as markets use Germany’s sovereign CDS as a hedge for systemic risk. Although most of the CDS changes for Germany during 2009–12 were due to idiosyncratic factors, market developments in Italy and Spain contributed significantly, likely due to their relative importance in the region. Changes in Greece’s sovereign CDS had no significant effect on Germany’s sovereign CDS despite initial widespread concerns about such linkages. Spain and Italy show a notable co-dependence in explaining each other’s volatility while Germany also plays an important role. It is found that extreme bad news led to persistent and nearly permanent effects on the stochastic volatility of European sovereign CDS spreads.

A two-state Markov-switching distinctive conditional variance application for tanker freight returns

The few papers that explore different ways to measure shipping freight dynamics have differed in their interpretation of the most suitable measure for conditional freight volatility and consequently for the most appropriate freight risk measure. Furthermore, recent empirical work in maritime studies suggests the possibility of conditional freight volatility switching between different regime states that are dynamically distinct. This paper attributes these dissimilarities in findings within maritime literature to the possibility of freight returns switching between distinctive volatility structures. Therefore, it proposes a two-state Markov-switching distinctive conditional variance model by matching the two-state conditional freight variance to the most suitable GARCH specification. This provides for the first time a distinctive empirical insight into the dynamics of tanker freight rates by explaining the dissimilarities within the maritime literature in measuring freight risk that improves our understanding of the changes in volatility dynamics of the freight supply curve. Thus, this study postulates that the dynamics of freight rates are distinct and conditional on the freight volatility regime-state that prevails at the time. Empirical findings postulate that volatilities within tanker freight returns are better modelled by a framework that is capable of capturing volatility dynamics within the tanker freight market. This study attempts to explain the dissimilarities within the maritime literature in measuring freight risk by improving our understanding of the changes in volatility dynamics of the freight supply curve.