Dissertations / Theses on the topic 'Asset Volatility'

Asset pricing modeling is a wide range area of research in Financial Engineering. In this thesis, which consists of an introduction, three papers and appendices; we deal with asset pricing models with stochastic volatility. Here stochastic volatility modeling includes diffusion models and regime-switching models. Stochastic volatility models appear as a response to the weakness of the constant volatility models. In Paper A , we present a survey on popular diffusion models where the volatility is itself a random process and we present the techniques of pricing European options under each model. Comparing single factor stochastic volatility models to constant factor volatility models it seems evident that the stochastic volatility models represent nicely the movement of the asset price and its relations with changes in the risk. However, these models fail to explain the large independent fluctuations in the volatility levels and slope. We consider Chiarella and Ziveyi model, which is a subclass of the model presented in Christoffersen and in paper A, we also explain a multi-factor stochastic volatility model presented in Chiarella and Ziveyi. We review the first-order asymptotic expansion method for determining European option price in such model. Multiscale stochastic volatilities models can capture the smile and skew of volatilities and therefore describe more accurately the movements of the trading prices. In paper B, we provide experimental and numerical studies on investigating the accuracy of the approximation formulae given by this asymptotic expansion. We present also a procedure for calibrating the parameters produced by our first-order asymptotic approximation formulae. Our approximated option prices will be compared to the approximation obtained by Chiarella and Ziveyi. In paper C, we implement and analyze the Regime-Switching GARCH model using real NordPool Electricity spot data. We allow the model parameters to switch between a regular regime and a non-regular regime, which is justified by the so-called structural break behaviour of electricity price series. In splitting the two regimes we consider three criteria, namely the intercountry price di_erence criterion, the capacity/flow difference criterion and the spikes-in-Finland criterion. We study the correlation relationships among these criteria using the mean-square contingency coe_cient and the co-occurrence measure. We also estimate our model parameters and present empirical validity of the model.

This dissertation consists of four papers that examine various aspects of the temporal patterns in the volatility of asset returns. The first paper compares the predictive performance of various parametric ARCH models. We find that ARCH models are generally good descriptions of the timevarying volatility of UK stock returns. There appears to be asymmetry in the conditional volatility, although no single model outperforms the rest in all instances. In the second paper, we uncover evidence of asymmetric predictability in the conditional variance of firms of different size. Large firms shocks affect the future volatility of small firms, but not vice versa. We also find that trading period shocks have a significant impact on future volatility, but not nontrading period shocks. In the third paper, we document a contemporaneous volatility-volume relationship. We find that volatility is related to change in trading volume, and we propose a conditional volatility model that incorporate this contemporaneous volatility-volume relationship. In the final paper, we examine the various method of adjusting for nontrading effects in ARCH models, and we propose a new diagnostic test to detect the validity of such adjustments. We also uncover evidence that conditional volatility increases prior to market closure, but declines after market opening.

For most developing countries, the predominant source of sovereign wealth is commodity related export income. However, over-reliance on commodity related income exposes countries to significant terms of trade shocks due to excessive price volatility. The spillovers are pro-cyclical fiscal policies and macroeconomic volatility problems that if not adequately managed, could have catastrophic economic consequences including sovereign bankruptcy. The aim of this study is to explore new ways of solving the problem in an asset-liability management framework for an exporting country like Ghana. Firstly, I develop an unconditional commodity investment strategy in the tactical mean-variance setting for deterministic returns. Secondly, in continuous time, shocks to return moments induce additional hedging demands warranting an extension of the analysis to a dynamic stochastic setting whereby, the optimal commodity investment and fiscal consumption policies are conditioned on the stochastic realisations of commodity prices. Thirdly, I incorporate jumps and stochastic volatility in an incomplete market extension of the conditional model. Finally, I account for partial autocorrelation, significant heteroskedastic disturbances, cointegration and non-linear dependence in the sample data by adopting GARCH-Error Correction and dynamic Copula-GARCH models to enhance the forecasting accuracy of the optimal hedge ratios used for the state-contingent dynamic overlay hedging strategies that guarantee Pareto efficient allocation. The unconditional model increases the Sharpe ratio by a significant margin and noticeably improves the portfolio value-at-risk and maximum drawdown. Meanwhile, the optimal commodities investment decisions are superior in in-sample performance and robust to extreme interest rate changes by up to 10 times the current rate. In the dynamic setting, I show that momentum strategies are outperformed by contrarian policies, fiscal consumption must account for less than 40% of sovereign wealth, while risky investments must not exceed 50% of the residual wealth. Moreover, hedging costs are reduced by as much as 55% while numerically generating state-dependent dynamic futures hedging policies that reveal a predominant portfolio strategy analogous to the unconditional model. The results suggest buying commodity futures contracts when the country's current exposure in a particular asset is less than the model implied optimal quantity and selling futures contracts when the actual quantity exported exceeds the benchmark.

In this dissertation, I investigate three related topics on asset pricing: the consumption-based asset pricing under long-run risks and fat tails, the pricing of VIX (CBOE Volatility Index) options and the market price of risk embedded in stock returns and stock options. These three topics are fully explored in Chapter II through IV. Chapter V summarizes the main conclusions. In Chapter II, I explore the effects of fat tails on the equilibrium implications of the long run risks model of asset pricing by introducing innovations with dampened power law to consumption and dividends growth processes. I estimate the structural parameters of the proposed model by maximum likelihood. I find that the stochastic volatility model with fat tails can, without resorting to high risk aversion, generate implied risk premium, expected risk free rate and their volatilities comparable to the magnitudes observed in data. In Chapter III, I examine the pricing performance of VIX option models. The contention that simpler-is-better is supported by the empirical evidence using actual VIX option market data. I find that no model has small pricing errors over the entire range of strike prices and times to expiration. In general, Whaley’s Black-like option model produces the best overall results, supporting the simpler-is-better contention. However, the Whaley model does under/overprice out-of-the-money call/put VIX options, which is contrary to the behavior of stock index option pricing models. In Chapter IV, I explore risk pricing through a model of time-changed Lévy processes based on the joint evidence from individual stock options and underlying stocks. I specify a pricing kernel that prices idiosyncratic and systematic risks. This approach to examining risk premia on stocks deviates from existing studies. The empirical results show that the market pays positive premia for idiosyncratic and market jump-diffusion risk, and idiosyncratic volatility risk. However, there is no consensus on the premium for market volatility risk. It can be positive or negative. The positive premium on idiosyncratic risk runs contrary to the implications of traditional capital asset pricing theory.

The objective for this master thesis is to investigate the possibility to predict the risk of stocks in financial markets. The data used for model estimation has been gathered from different branches and different European countries. The four data series that are used in the estimation are price series from: Münchner Rück, Suez-Lyonnaise des Eaux, Volkswagen and OMX, a Swedish stock index. The risk prediction is done with univariate GARCH models. GARCH models are estimated and validated for these four data series.

Conclusions are drawn regarding different GARCH models, their numbers of lags and distributions. The model that performs best, out-of-sample, is the APARCH model but the standard GARCH is also a good choice. The use of non-normal distributions is not clearly supported. The result from this master thesis could be used in option pricing, hedging strategies and portfolio selection.

I present a simple model of an evolutionary financial market with heterogeneous agents, based on the concept of adaptive belief systems introduced by Brock and Hommes (1997a). Agents choose between different forecast rules based on past performance, resulting in an evolutionary dynamics across predictor choice coupled to the equilibrium dynamics. The model generates endogenous price fluctuations with similar statistical properties as those observed in real return data, such as fat tails and volatility clustering. These similarities are demonstrated for data from the British, German, and Austrian stock market. (author's abstract)
Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

This thesis contains two essays. In the first essay, we investigate the impact of time varying volatility of consumption growth on the cross-section and time-series of equity returns. While many papers test consumption-based pricing models using the first moment of consumption growth, less is known about how the time-variation of consumption growth volatility affects asset prices. In a model with recursive preferences and unobservable conditional mean and volatility of consumption growth, the representative agent's estimates of conditional moments of consumption growth affect excess returns. Empirically, we find that estimated consumption volatility is a priced source of risk, and exposure to it predicts future returns in the cross-section. Consumption volatility is also a strong predictor of aggregate quarterly excess returns in the time-series. The estimated negative price of risk together with the evidence on equity premium predictability suggest that the elasticity of intertemporal substitution of the representative agent is greater than unity, a finding that contributes to a long standing debate in the literature. In the second essay, I present a simple model to show that if agents face binding portfolio constraints, stocks with high volatility in states of low market returns demand a premium beyond the one implied by systematic risks. Assets whose volatility positively covaries with market volatility also have high expected returns. Both effects of this idiosyncratic volatility risk premium are strongest for assets that face more binding trading restrictions. Unlike the prior empirical literature that obtains mixed results when focusing on the level of idiosyncratic volatility, I investigate the dynamic behavior of idiosyncratic volatility and find strong support for my predictions. Comovement of innovations of idiosyncratic volatility with market returns negatively predicts returns for trading restricted stocks relative to unrestricted stocks, and comovement of idiosyncratic volatility with market volatility positively predicts returns for restricted assets.

The thesis evaluates the benefit of allowing for long memory volatility dynamics in forecasts of the variance-covariance matrix for asset allocation. First, I compare the forecast performance of multivariate long memory conditional volatility models (the long memory EWMA, long memory EWMA-DCC, FIGARCH-DCC and Component GARCH-DCC models) with that of short memory conditional volatility models (the short memory EWMA and GARCH-DCC models), using the asset allocation framework of Engle and Colacito (2006). The research reports two main findings. First, for longer horizon forecasts, long memory volatility models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory volatility models. Second, the two parsimonious long memory EWMA models outperform the other models – both short memory and long memory – in a majority of cases across all forecast horizons. These results apply to both low and high dimensional covariance matrices with both low and high correlation assets, and are robust to the choice of estimation window. The research then evaluates the application of multivariate long memory conditional volatility models in dynamic asset allocation, applying the volatility timing procedure of Fleming et al. (2001). The research consistently identifies the economic gains from incorporating long memory volatility dynamics in investment decisions. Investors are willing to pay to switch from the static to the dynamic strategies, and especially from the short memory volatility timing to the long memory volatility timing strategies across both short and long investment horizons. Among the long memory conditional volatility models, the two parsimonious long memory EWMA models, again, generally produce the most superior portfolios. When transaction costs are taken into account, the gains from the daily rebalanced dynamic portfolios deteriorate; however, it is still worth implementing the dynamic strategies at lower rebalancing frequencies. The results are robust to estimation error in expected returns, the choice of risk aversion coefficients and the use of a long-only constraint. To control for estimation error in forecasts of the long memory high dimensional covariance matrix, the research develops a dynamic long memory factor (the Orthogonal Factor Long Memory, or OFLM) model by embedding the univariate long memory EWMA model of Zumbach (2006) into an orthogonal factor structure. The factor-structured OFLM model is evaluated against the six above multivariate conditional volatility models in terms of forecast performance and economic benefits. The results suggest that the OFLM model generally produces impressive forecasts over both short and long forecast horizons. In the volatility timing framework, portfolios constructed with the OFLM model consistently dominate the static and other dynamic volatility timing portfolios in all rebalancing frequencies. Particularly, the outperformance of the factor-structured OFLM model to the fully estimated LM-EWMA model confirms the advantage of the factor structure in reducing estimation error. The factor structure also significantly reduces transaction costs, making the dynamic strategies more feasible in practice. The dynamic factor long memory volatility model also consistently produces more superior portfolios than those produced by the traditional unconditional factor and the dynamic factor short memory volatility models.

The aim of this thesis is to show that news analytics data can be utilised to improve the predictive ability of existing models that have useful roles in a variety of financial applications. The modified models are computationally efficient and perform far better than the existing ones. The new modified models offer a reasonable compromise between increased model complexity and prediction accuracy. I have investigated the impact of news sentiment on volatility of stock returns. The GARCH model is one of the most common models used for predicting asset price volatility from the return time series. In this research, I have considered quantified news sentiment as a second source of information and its impact on the movement of asset prices, which is used together with the asset time series data to predict the volatility of asset price returns. Comprehensive numerical experiments demonstrate that the new proposed volatility models provide superior prediction than the "plain vanilla" GARCH, TGARCH and EGARCH models. This research presents evidence that including news sentiment term as an exogenous variable in the GARCH framework improves the prediction power of the model. The analysis of this study suggested that the use of an exponential decay function is good when the news flow is frequent, whereas the Hill decay function is good only when there are scheduled announcements. The numerical results vindicate some recent findings regarding the utility of news sentiment as a predictor of volatility, and also vindicate the utility of the new models combining the proxies for past news sentiments and the past asset price returns. The empirical analysis suggested that news augmented GARCH models can be very useful in estimating VaR and implementing risk management strategies. Another direction of my research is introducing a new approach to construct a commodity futures pricing model. This study proposed a new method of incorporating macroeconomic news into a predictive model for forecasting prices of crude oil futures contracts. Since these futures contracts are iii iv more liquid than the underlying commodity itself, accurate forecasting of their prices is of great value to multiple categories of market participants. The Kalman filtering framework for forecasting arbitrage-free (futures) prices was utilized, and it is assumed that the volatility of oil (futures) price is influenced by macroeconomic news. The impact of quantified news sentiment on the price volatility is modelled through a parametrized, nonlinear functional map. This approach is motivated by the successful use of a similar model structure in my earlier work, for predicting individual stock volatility using stock-specific news. Numerical experiments with real data illustrate that this new model performs better than the one factor model in terms of accuracy of predictive power as well as goodness of fit to the data. The proposed model structure for incorporating macroeconomic news together with historical (market) data is novel and improves the accuracy of price prediction quite significantly.

Cette thèse de doctorat comprend trois articles indépendants sur la finance des ménages et l’évaluation des actifs. Les deux premiers articles sont étroitement liés, utilisent des données similaires et étudient le rôle du risque de revenu du travail dans le choix du portefeuille. Le troisième article étudie un modèle de volatilité basé sur le model de Markov-switching multifractal. Le premier article est intitulé "Countercyclical Income Risk and Portfolio Choices" (avec Sylvain Catherine et Paolo Sodini). En utilisant les données du panel administratif suédois sur les salaires et les choix de portefeuille des particuliers, nous montrons que le risque de du revenu contracyclique réduit la volonté des ménages d'investir sur le marché financier. Le deuxième article est intitulé "Seeking Skewness". À l'aide de données administratives détaillées des ménages suédois sur les portefeuille et le revenu du travail, cet article examine le comportement des investisseurs de la demande d'asymétrie dans leur choix de portefeuille. Le troisième article est "Multifractal Volatility with Shot-noise Component" (avec Laurent Calvet). Baser sur le modèle Markov Switching Multifractal (MSM) de Calvet et Fisher (2004), nous développons dans cet article un modèle de volatilité multifractale à temps discret pour capturer des sauts et des décroissances dans le processus de volatilité
This doctoral thesis consists of three independent papers in household finance and empirical asset pricing. The first two papers are closedly related, use similar data, and investigate the role of labor income risk in portfolio choice. The third paper studies volatility model based on Markov switching multifractal. The first paper is “Countercyclical Income Risk and Portfolio Choices” (with Sylvain Catherine and Paolo Sodini). Using Swedish administrative panel data on individual's wages and portfolio holdings, we show that countercyclical labor income downside risk reduces households' willingness to invest in financial market. The second paper is “Seeking Skewness”. Using detailed disaggregated Swedish household administrative data on portfolio holdings and labor income, this paper investigates retail investors’ behavior of seeking skewness in their portfolio choice. The third paper is “Multifractal Volatility with Shot-noise Component” (with Laurent Calvet). Based on the Markov Switching Multifractal (MSM) model of Calvet and Fisher (2004), we develop in this paper a discrete-time multifractal volatility model to capture the jump and decay pattern in the volatility process along with other stylized facts

This thesis presents an empirical investigation of the behaviour of financial markets and also the relationship on the real economy. The thesis will focus on Ireland, a small open economy with increased dependence on international developments. Two important aspects of the Irish economy, the term structure of interest rates and impact of exchange rate volatility, will be analysed. The motivation for the analysis of the term structure of interest rates in part I is two fold. Central banks can control very short-term interest rates, but of course the real economy will only really be affected by the long-term interest rate. Therefore the transmission mechanism from monetary policy to the real economy will depend on the relationship between short-term interest rates and long-term interest rates, i.e. the term structure of interest rates. The second important issue is that of market efficiency, and whether asset prices and returns are correctly valued by the market. A number of different interest rate maturities will be used to test the Expectations Hypothesis (EH) of term structure. The EH will also be tested assuming constant and time varying term premia. The results give support for the EH, and fmd no evidence of a time varying term premium. Given the recent extraordinary growth in the share of Irish exports in GDP, the impact of exchange rate volatility on Irish exports is analysed in part 2. The moti vation behind part 2 is to test whether the resulting monetary union will lead to a rise in exports, as a result of the end of exchange rate risk. Using the cointegration-ECM methodology I fmd that in the long-run there is no significant effect on Irish exports to the UK, while there is actually a positive impact on exports to European countries (UK included). I tentatively conclude that in the long-run the involvement in a single European currency will have no impact on trade.

We present two option pricing models, both different from the classical Black-Scholes-Merton model. The first model, suggested by Heston, considers the case where the asset price volatility is stochastic. For this model we study the asset price process and give in detail the derivation of the European call option price process. The second model, suggested by Brody-Hughston-Macrina, describes the observation of certain information about the claim perturbed by a noise represented by a Brownian bridge. Here we also study in detail the properties of this noisy information process and give the derivations of both asset price dynamics and the European call option price process.

Thesis (Ph.D.)--George Mason University, 2009.
Vita: p. 160. Thesis director: James E. Gentle. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Sciences and Informatics. Title from PDF t.p. (viewed Oct. 12, 2009). Includes bibliographical references. Also issued in print.

Traditional portfolio theory suggests that, in equilibrium, only the market risk is priced in the cross-section of expected stock returns. However, if the market is not perfect and investors are constantly changing investing behaviors based on their perceptions about future market outlook, then non-traditional risk factors could potentially provide significant power of describing the expected stock returns. This dissertation has two essays on the pricing of volatility, in which the market is not assumed to be frictionless or perfect. Essay 1 focuses on the pricing of individual volatility in penny stocks. Empirical results show that individual volatility plays an important role in describing the average cross-sectional returns of penny stocks. Resorting to the rolling portfolio approach, evidences indicate that portfolios consisting of penny stocks with high individual volatilities, on average, earned much higher returns than portfolios consisting of penny stocks with low individual volatilities. This effect is statistically significant when multiple factors are controlled simultaneously. Essay 2 focuses on the pricing of the market volatility among individual stocks. Following the rolling portfolio method, Essay 2 constructs portfolios that consist of individual stocks with various market volatility exposures. Traditional risk factors such as market beta, size, book-to-market, and momentum are controlled respectively to obtain more detailed analyses. Empirical results yield a negative pricing of the market volatility and it is more prominent in stocks that have high market beta, small size, and high book-to-market.

In this thesis the problem of model uncertainty is under scrutiny along with its implications in attaining optimal forecastability. To account for that averaging techniques are adopted including Bayesian model averaging, Bayesian Approximation and Thick Modelling. After an introductory chapter and a second one where some of the most celebrated conditional-volatility modelling proposals are discussed the third chapter investigates volatility forecasting and its direct association to option pricing. Some novel approaches to perform averaging are suggested here including variations of the predetermined methods together with more sophisticated algorithmic propositions such as Neural Networks. The fourth chapter extends the focal point of averaging to the whole predictive volatility density as this can be inferred first from derivatives on the underlying volatility index and second directly from the asset class under consideration (here the equity index) using bootstrap based - GARCH type models. The fifth chapter introduces some widely used variable selection techniques to the Finance continuum while averaging schemes once more are used in order to avoid model misspeci cation risk. Extensions to a nonlinear regression framework are also suggested while investment strategies are implemented in all chapters substantiating the ultimate supremacy of averaging schemes against single model alternatives. The last chapter concludes the research and makes some future suggestions for additional investigation.

This dissertation consists of two chapters. The first chapter examines the role of growth options on stock return continuation. Growth options are both difficult to value and risky. Daniel, Hirshleifer and Subrahmanyam (1998) argue that higher momentum profits earned by high market-to-book firms stem from investorsâ higher overconfidence due to the difficulty of valuing growth options. Johnson (2002) and Sagi and Seasholes (2007) offer an alternative rational explanation wherein growth options cause a wider spread in risk and expected returns between winners and losers. This paper suggests that firm-specific uncertainty helps disentangle these two different explanations. Specifically, the rational explanation is at work among firms with low firm specific uncertainty. However, the evidence is in favor of the behavioral explanation for firms with high firm specific uncertainty. This is consistent with the notion that investors are more prone to behavioral biases in the presence of firm-specific uncertainty and the resulting mispricings are less likely to be arbitraged away. The second chapter examines how investors capitalize differences of opinion when disagreements are common knowledge. We conduct an event study of the market's reaction to analysts' dispersed earnings forecast revisions. We find that investors take differences of opinion into account and do not exhibit an optimism bias. Our findings indicate that the overpricing of stocks with high forecast dispersion is not due to investors' tendency to overweight optimistic expectations, but rather due to investor credulity regarding analysts' incentives. Our findings support the notion that assets may become mispriced when rational investors face structural uncertainties as proposed by Brav and Heaton (2002).
Ph. D.

There has been an on-going debate about choices of the most suitable model amongst a variety of model specifications and parameterizations. The first dissertation essay investigates whether asymmetric leptokurtic return distributions such as Hansen's (1994) skewed tdistribution combined with GARCH specifications can outperform mixed GARCH-jump models such as Maheu and McCurdy's (2004) GARJI model incorporating the autoregressive conditional jump intensity parameterization in the discrete-time framework. I find that the more parsimonious GJR-HT model is superior to mixed GARCH-jump models. Likelihood-ratio (LR) tests, information criteria such as AIC, SC, and HQ and Value-at-Risk (VaR) analysis confirm that GJR-HT is one of the most suitable model specifications which gives us both better fit to the data and parsimony of parameterization. The benefits of estimating GARCH models using asymmetric leptokurtic distributions are more substantial for highly volatile series such as emerging stock markets, which have a higher degree of non-normality. Furthermore, Hansen's skewed t-distribution also provides us with an excellent risk management tool evidenced by VaR analysis. The second dissertation essay provides a variety of empirical evidences to support redundancy of stochastic volatility for SP500 index returns when stochastic volatility is taken into account with infinite activity pure Lévy jumps models and the importance of stochastic volatility to reduce pricing errors for SP500 index options without regard to jumps specifications. This finding is important because recent studies have shown that stochastic volatility in a continuous-time framework provides an excellent fit for financial asset returns when combined with finite-activity Merton's type compound Poisson jump-diffusion models. The second essay also shows that stochastic volatility with jumps (SVJ) and extended variance-gamma with stochastic volatility (EVGSV) models perform almost equally well for option pricing, which strongly imply that the type of Lévy jumps specifications is not important factors to enhance model performances once stochastic volatility is incorporated. In the second essay, I compute option prices via improved Fast Fourier Transform (FFT) algorithm using characteristic functions to match arbitrary log-strike grids with equal intervals with each moneyness and maturity of actual market option prices.

We consider the stochastic volatility model with smooth transition and persistent la- tent factors. We argue that this model has advantages over the conventional stochastic model for the persistent volatility factor. Though the linear filtering is widely used in the state space model, the simulation result, as well as theory, shows that it does not work in our model. So we apply the density-based filtering method; in particular, we develop two methods to get solutions. One is the conventional approach using the Maximum Likelihood estimation and the other is the Bayesian approach using Gibbs sampling. We do a simulation study to explore their characteristics, and we apply both methods to actual macroeconomic data to extract the volatility generating process and to compare macro fundamentals with them. Next we extend our model into multivariate model extracting common and id- iosyncratic volatility for multivariate processes. We think it is interesting to apply this multivariate model into measuring time-varying uncertainty of macroeconomic variables and studying the links to market returns via a consumption-based asset pric- ing model. Motivated by Bansal and Yaron (2004), we extract a common volatility factor using consumption and dividend growth, and we find that this factor predicts post-war business cycle recessions quite well. Then, we estimate a long-run risk model of asset prices incorporating this macroeconomic uncertainty. We find that both risk aversion and the intertemporal elasticity of substitution are estimated to be around two, and our simulation results show that the model can match the first and second moments of market return and risk-free rate, hence the equity premium.

Asset value volatility is at the heart of the capital structure optimisation theory as proposed by Leland (1994). Asset value and volatility are two key unobservable inputs required for pricing equity and debt using structural models that are based on option pricing theory. These inputs are normally calculated using equity prices, book value of debt and leverage. However, when tested empirically, the prediction errors of structural models suggest that such calculations of asset value and volatility may be inaccurate. Furthermore, in alI structural models it is assumed that asset value volatility is constant over time, which may also lead to inaccurate credit spread predictions. This is the first empirical study into bond-implied asset'values and volatility. This study uses three-year time series of daily bond- and equity prices for 36 Western-European companies to derive bo?d-implied asset values and asset value volatilities. Bond-implied values were calculated using a structural credit model developed by Leland and Toft (1996). The values were obtained by simultaneously solving a system of two equations and two unknowns. The two equations were the value of a bond and the value of equity; the .two unknowns were asset value and asset value volatility. An equity-based method for calculating asset value and volatility was used to generate an alternative data set for the same daily observations. The two sets of data for each firm were compared to determine whether there are statistically significant differences between bond-implied and equity-based asset values and volatility, and whether bond-implied volatility is stable over time. Additionally,. the correlation between bond-implied volatility and theibond's time to maturity was tested to determine whether there is a constant relationship between these two variables. TIlls study shows that there are significant difference~ between asset values and asset value volatilities derived from bond prices and those derived using equity values and leverage. Additionally, it is shown that bond-implied asset value volatility is not constant over time. The relationship between bond-implied asset value volatility and a bond's time to maturity as measured by their correlation coefficient was not uniform across the sample, in some cases demon,strating strong positive correlation, in other cases strong negative correlation and in some cases no strong relationship at alI.

The financial markets have a complex structure and the modelling techniques have recently been more and more complicated. So for a portfolio manager it is very important to find better and more sophisticated modelling techniques especially after the 2007-2008 banking crisis. The idea in this thesis is to find the connection between the components in macroeconomic environment and portfolios consisting of assets from OMX Stockholm 30 and use these relationships to perform Tactical Asset Allocation (TAA). The more specific aim of the project is to prove that dynamic modelling techniques outperform static models in portfolio theory.
Den finansiella marknaden är av en väldigt komplex struktur och modelleringsteknikerna har under senare tid blivit allt mer komplicerade. För en portföljförvaltare är det av yttersta vikt att finna mer sofistikerade modelleringstekniker, speciellt efter finanskrisen 2007-2008. Idéen i den här uppsatsen är att finna ett samband mellan makroekonomiska faktorer och aktieportföljer innehållande tillgångar från OMX Stockholm 30 och använda dessa för att utföra Tactial Asset Allocation (TAA). Mer specifikt är målsättningen att visa att dynamiska modelleringstekniker har ett bättre utfall än mer statiska modeller i portföljteori.

Accurate measurement of asset return volatility and correlation is an important problem in financial econometrics. The presence of market microstructure noise in high-frequency data complicates such estimations. This study extends a prior application of a model-based volatility estimator with autocorrelated market microstructure noise to estimation of correlation. The model is applied to a high-frequency dataset including a stock and an index, and the results are compared to some existing models. This study supports previous findings that including an autocorrelation factor produces an estimator potentially less vulnerable to market microstructure noise, and finds that the same is true about the extended correlation estimator that is introduced here.

Created in 2009, the digital currency of bitcoin is a relatively new phenomenon. During this short period of time, it has however displayed a strong development of both price and trade volume. This has led to increased media attention, but also regulators and researchers have developed an interest. At this moment, the amount of available research is however limited. With a focus on the price volatility of bitcoin and an aim of finding drivers of this volatility, this study is taking a unique position. The research has its basis in the philosophical position of positivism and objectivism. This has shaped the research question as well as the construction of the study. The result is a describing and explaining research with a deductive research approach, a quantitative research method and an archival research strategy. This has in turn stimulated an extensive literature review and information search. Areas of discussion are microstructure theory, the efficient market hypothesis, behavioural finance and informational structures. Due to the limited amount of previous bitcoin research within the area of price volatility, the study has drawn extensively on research performed on more classical assets such as stocks. Nevertheless, when available, bitcoin research has been used as a foundation/reference and an inspiration. Reviews of academic literature and economic theories, as well as public news helped to identify the variables for the empirical study. These variables are; information demand, trade volume, world market index, trend and six specified events, occurring during the chosen sample period and included in the study as dummy variables. The variables are all analysed and included in a GARCH (1,1) model, modified following a similar research by Vlastakis & Markellos (2012) on stocks. This GARCH (1,1) model is then fitted to the bitcoin volatility registered for the sample period and is able thereby able to generate data of if and how the variables affect the bitcoin volatility. The test result suggests that five of the ten variables are significant on a 5 %-level. More specifically it suggests that information demand is a significant variable with a positive influence on the bitcoin volatility, something that corresponds to the literature on information demand and price volatility. This also relates to the events found significant, as they generated bitcoin related information. The significant events of the Cypriot crisis and the failure of the bitcoin exchange MtGox are thus specific examples of how information affects price volatility. Another significant variable is trade volume, which also displays a positive influence on the volatility. The last significant variable turned out to be a constructed positive trend, suggesting that increasing acceptance of bitcoin decreases its volatility.

Thesis advisor: Christopher Baum
Thesis advisor: Fabio Ghironi
Do events in the natural gas market cause repercussions in the crude oil market? In light of the enormous impact that price movements in the two largest U.S. energy markets have on the economy, it is important to understand not just the individual markets but also how they relate to one another. On this front, the literature presents a puzzle: while economic theory suggests that the oil and gas markets are interlinked through a bi-directional causal relationship, empirical research has concluded that the oil market affects the gas market but not vice versa. The first chapter of this dissertation improves on the previous studies in two ways: by using high-frequency, intraday oil and gas futures prices and by analyzing the effect of specific news announcements from the weekly oil and gas inventory reports. The results dispel the notion of one-way causality and provide support for the theory. The reaction of the futures volatility and returns is asymmetric, although this asymmetry does not follow the "good news" vs. "bad news" pattern from stock and bond markets; the response depends on whether the shock is driven by oil or gas inventory gluts or shortages. The two-way causality holds not only for the nearby futures contract but also for contracts of longer maturities. These findings underscore the importance of analyzing financial markets in a multi-market context. The second chapter of this dissertation asks whether volatility and trading volume evolve in a unidirectional or bidirectional, contemporaneous or lagged relationship in the crude oil and natural gas futures markets. This question is important because it affects trading and government regulation but previous studies have come to conflicting conclusions. Their main shortcoming is the low frequency of data used in the analysis. This chapter improves on the previous studies in three ways: by using high-frequency, intraday oil and gas futures prices and volume, by including trading not only during the day but also during the night, and by analyzing not only the nearby futures contract but also contracts with longer maturities. For the nearby contract, Granger-causality tests show that past values of volume help explain volatility which agrees with the Sequential Information Arrival Hypothesis. Past values of volatility have explanatory power for volume only when absolute return is used as the volatility measure; when the conditional variance from GARCH models is used as the volatility measure, the causality in this direction disappears. These results change when low-frequency daily data is applied. It is also shown that the volatility-volume relationship differs for contracts with longer maturities. These findings are relevant for regulations, such as trader position limits recently adopted by the U.S. Commodity Futures Trade Commission. The third chapter of this dissertation investigates whether the production structure of firms affects international optimal portfolios, risk-sharing, and response of terms of trade (TOT) to shocks. The answer to this question would enhance our understanding of the home equity bias, yet it has not been addressed in the theoretical literature. This chapter studies the question in a two-country dynamic stochastic general equilibrium model with endogenous portfolio allocation. It shows that the optimal portfolio includes more home equity as the production structure changes from exporter-only, i.e., firms operating in their home countries and serving foreign markets by exports, to multi-national-company-extreme (MNC), i.e., firms hiring labor in both countries and producing locally in both countries. This shift occurs because changing the firms' production structure eliminates exposure to technology differences and allows the home household to accomplish the same diversification with less foreign equity. The production structure also has implications for the effect of technology shocks on the TOT. Under the exporter-only setup, a shock to technology causes a standard TOT deterioration, whereas under the MNC-extreme setup, a shock to technology leads to a TOT improvement. By producing testable predictions, this chapter underscores the need to take firms' production structure into account when analyzing international optimal portfolios, risk sharing, and response of the TOT to technology shocks. This is especially important since empirical research has generated conflicting results
Thesis (PhD) — Boston College, 2012
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Economics

Understanding financial asset return correlation is a key facet in asset allocation and investor’s portfolio optimization strategy. For the last decades, several studies have investigated this relationship between stock and bond returns. But, fewer studies have dealt with multi-asset return dynamics. While initial literature attempted to understand the fundamental pattern of comovements, later studies model the economic state variables influencing such time-varying comovements of primarily stock and bond returns. Research widely acknowledges that return distributions of financial assets are non-normal. When the joint distributions of the asset returns follow a non-elliptical structure, linear correlation fails to provide sufficient information of their dependence structure. In particular two issues arise from this existing empirical evidence. The first is to propose a more reliable alternative density specification for a higher-dimensional case. The second is to formulate a measure of the variables’ dependence structure which is more instructive than linear correlation. In this work I use a time-varying conditional multivariate elliptical and non-elliptical copula to examine the return comovements of three different asset classes: financial assets, commodities and real estate in the US market. I establish the following stylized facts about asset return comovements. First, the static measures of asset return comovements overestimate the asset return comovements in the economic expansion phase, while underestimating it in the periods of economic contraction. Second, Student t-copulas outperform both elliptical and non-elliptical copula models, thus confirming the ii dominance of Student t-distribution. Third, findings show a significant increase in asset return comovements post August 2007 subprime crisis ... [cont.].

This thesis studies the use of nonparametric econometric methods to reconcile the empirical behaviour of financial asset prices with theoretical valuation models. The confrontation of economic theory with asset price data requires various functional form assumptions about the preferences and beliefs of investors. Nonparametric methods provide a flexible class of models that can prevent misspecification of agents’ utility functions or the distribution of asset returns. Evidence for potential nonlinearity is seen in the presence of non-Gaussian distributions and excessive volatility of stock returns, or non-monotonic stochastic discount factors in option prices. More robust model specifications are therefore likely to contribute to risk management and return predictability, and lend credibility to economists’ assertions. Each of the chapters in this thesis relaxes certain functional form assumptions that seem most important for understanding certain asset price data. Chapter 1 focuses on the state-price density in option prices, which confounds the nonlinearity in both the preferences and the beliefs of investors. To understand both sources of nonlinearity in equity prices, Chapter 2 introduces a semiparametric generalization of the standard representative agent consumption-based asset pricing model. Chapter 3 returns to option prices to understand the relative importance of changes in the distribution of returns and in the shape of the pricing kernel. More specifically, Chapter 1 studies the use of noisy high-frequency data to estimate the time-varying state-price density implicit in European option prices. A dynamic kernel estimator of the conditional pricing function and its derivatives is proposed that can be used for model-free risk measurement. Infill asymptotic theory is derived that applies when the pricing function is either smoothly varying or driven by diffusive state variables. Trading times and moneyness levels are modelled by marked point processes to capture intraday trading patterns. A simulation study investigates the performance of the estimator using an iterated plug-in bandwidth in various scenarios. Empirical results using S&P 500 E-mini European option quotes finds significant time-variation at intraday frequencies. An application towards delta- and minimum variance-hedging further illustrates the use of the estimator. Chapter 2 proposes a semiparametric asset pricing model to measure how consumption and dividend policies depend on unobserved state variables, such as economic uncertainty and risk aversion. Under a flexible specification of the stochastic discount factor, the state variables are recovered from cross-sections of asset prices and volatility proxies, and the shape of the policy functions is identified from the pricing functions. The model leads to closed-form price-dividend ratios under polynomial approximations of the unknown functions and affine state variable dynamics. In the empirical application uncertainty and risk aversion are separately identified from size-sorted stock portfolios exploiting the heterogeneous impact of uncertainty on dividend policy across small and large firms. I find an asymmetric and convex response in consumption (-) and dividend growth (+) towards uncertainty shocks, which together with moderate uncertainty aversion, can generate large leverage effects and divergence between macroeconomic and stock market volatility. Chapter 3 studies the nonparametric identification and estimation of projected pricing kernels implicit in the pricing of options, the underlying asset, and a riskfree bond. The sieve minimum-distance estimator based on conditional moment restrictions avoids the need to compute ratios of estimated risk-neutral and physical densities, and leads to stable estimates even in regions with low probability mass. The conditional empirical likelihood (CEL) variant of the estimator is used to extract implied densities that satisfy the pricing restrictions while incorporating the forwardlooking information from option prices. Moreover, I introduce density combinations in the CEL framework to measure the relative importance of changes in the physical return distribution and in the pricing kernel. The nonlinear dynamic pricing kernels can be used to understand return predictability, and provide model-free quantities that can be compared against those implied by structural asset pricing models.

This dissertation presents four essays. The first chapter builds a real-options, term structure model of the firm incorporating stochastic volatility and endogenous default to shed new light on the value premium, financial distress, momentum, and credit spread puzzles. The paper uses recently developed methodologies based on asymptotic expansions to solve the model. The second chapter, coauthored with Adam Guren, presents a model that shows how foreclosures can exacerbate a housing bust and delay the housing market's recovery. Quantitatively, the model successfully explains aggregate and retail price declines, the foreclosure share of volume, and the number of foreclosures both nationwide and across MSAs. The third and fourth chapters, coauthored with Stephen W. Salant and Jason Winfree, discuss the economics of untraceable experience goods in a variety of settings. The third chapter drops the "small country" assumption in the trade literature on collective reputation and shows how large exporters like China can address severe problems assuring the quality of its exports. The fourth chapter demonstrates how regulations in the formal sector of developing countries can lead to a quality gap between formal and informal sector goods. It moreover investigates how changes in regulation affect quality, price, aggregate production, and the number of firms in each sector.
Economics

L’objectif de cette thèse est d’étudier les divers aspects de la crise financière 2007-09. Dans l’ensemble, les deux types d’objectifs sont poursuivis dans cette thèse: le premier objectif est de déchiffrer les liaisons entre les différents marchés boursiers, immobiliers et pétroliers afin d’évaluer les retombées du rendement et de la volatilité. L’accent dans ce champ est mis sur le niveau d’intégration entre les marchés pendant différents périodes de temps y compris la crise. Ce domaine est examiné par le développement de trois essais distincts. Le premier essai examine la déclaration du gouvernement Russe affirmant que ce sont les chocs initiés par les marchés étrangers qui ont été essentiellement responsables de la panique sur leur marché boursier pendant la période Septembre - Octobre 2008. En utilisant l’approche de la contagion financière, les résultats indiquent que le marché boursier Russe est intégré faiblement avec les marchés Américain et Européen ce qui met à l’écart l’affirmation du gouvernement. Les résultats de la comparaison bivariée des marchés montrent que le marché Russe émet un niveau élevé des chocs en affectant la structure de corrélation entre la Russie et les marchés étrangers tandis que l’inverse est vrai dans le cas des retombées de la volatilité. Il est conclu que les gouvernements ne devraient pas utiliser la justification des chocs étrangers qui affectent les marchés locaux pendant la crise globale. Comme dans l’analyse précédente, nous examinons la transmission des chocs et de la volatilité sur les marchés des sociétés d’investissements immobiliers cotées (SIIC). Etant donné que la loi exige des SIIC de consacrer une grande partie de leurs investissements dans les actifs immobiliers, le rôle des SIIC dans la propagation de la crise hypothécaire des subprimes à travers le globe a été évalué. L’analyse préliminaire démontre que pendant la crise tous les marchés possèdent entre eux des liens de causalité dans le sens de Granger. Ce résultat est en accord avec le point de vue largement répandu que les marchés boursiers se comportent de la même manière pendant la crise globale. Ensuite l’intégration entre les SIIC américaines (USREITs) et les SIIC globales et le S&P500 a été examiné. Les résultats indiquent que les SIIC américaines sont faiblement intégrées avec les SIIC globales impliquant un niveau faible des retombées bidirectionnelles du choc et de la volatilité tandis que l’inverse est vrai dans le cas des SIIC américaines (USREITs) - S&P500. Enfin, l’intégration entre le S&P500 et les SIIC globales a été exploré. Les résultats suggèrent une faible intégration entre le S&P500 et les SIIC globales. Les chocs sont essentiellement transmis du S&P500 vers les SIIC globales. D’une manière générale, l’étude amène à la conclusion que ni les SIIC américaines ni le S&P500 ne peuvent pas créer une panique plus grande sur les marchés des SIIC globales pendant la crise. Ces liens faibles indiquent également les avantages de la diversification d’un portefeuille.En étudiant la crise au niveau suivant, nous analysons la relation à court ainsi qu’à long terme entre le prix du pétrole brut et les marchés boursiers pour le Brésil, la Russie, l’Inde et la Chine (BRIC) dans le cadre des modèles structurels contraints. Nos conclusions indiquent que les marchés boursiers du BRIC suivent dans certaine mesure l’hypothèse de l’efficience des marchés comme dans le cas d’un pays importateur du pétrole un choc positif de prix du pétrole entraîne une chute du marché boursier et l’inverse est vrai pour tous les pays exportateurs du pétrole. Les deux comportements importants ont été identifiés qui sont liés au taux d’intérêt à court terme et à la production industrielle. La montée des prix du pétrole engendre l’inflation qui est enrayée par une hausse du taux d’intérêt à court terme. En même temps, la production industrielle a tendance à s’accroître en termes réels au lieu de diminuer vu le choc des prix du pétrole (une hausse des prix du pétrole). Ce résultat peut être imputé à la couverture du risque d’une hausse des prix du pétrole avec la livraison physique. Dès que le contrat de couverture commence à expirer après 30, 90 ou 180 jours l’impact des prix du pétrole commence à réduire la production industrielle. Le deuxième objectif de la thèse est d’étudier l’intervention gouvernementale particulièrement sur les marchés boursiers et dans l’économie en général. D’un point de vue boursier, nous analysons le cas de l’intervention répétée du gouvernement Russe sur ses marchés boursiers nationaux pendant la fin d’année 2008. En utilisant la méthodologie des études d’événements, les résultats sont peu concluants sur l’efficacité de l’intervention gouvernementale pour protéger le marché boursier contre des chocs financiers extérieurs. Ainsi l’étude préconise aux gouvernements de ne pas intervenir pendant la crise des marchés boursiers.En étudiant le cas de l’économie en général, une nouvelle idée a été développée et lancée concernant l’intervention de la banque centrale pour contrecarrer une Bulle des Prix des Actifs (BPA). Nous avons détecté différents problèmes dans la théorie économique concernant l’intervention de la banque centrale sur le marché monétaire en cas d’apparition d’une BPA comme par exemple, - un décalage dans le temps ne peut pas avoir une incidence sur le secteur formant une bulle spéculative tout seul ainsi que l’inadéquation des canaux traditionnels des prêts bancaires. Pour faire face à ces problèmes l’étude fait avancer l’idée d’une intervention réglementaire basée sur certaines suppositions classiques. L’idée implique que contrairement à l’intervention traditionnelle de la politique monétaire la banque centrale devrait imposer aux institutions de crédit des limites d’exposition au risque de crédit pour chaque secteur. Ces limites devraient être imposées une fois que la banque centrale découvre une hausse anormale des prix dans un secteur économique donné. Nos résultats préliminaires suggèrent que l’idée d’une intervention réglementaire a du potentiel de contrecarrer la BPA
The objective of the dissertation is to study various aspects of financial crisis 2007-09. Overall there are two kinds of objectives that are pursued in this dissertation: the first objective is to decipher the linkages between different stock markets, real estate markets and oil markets in order to assess the return and volatility spillover effects. The focus in this area is on the level of integration among the markets during different periods of time including crisis. This area is investigated through developing three separate essays. The first essay tests the Russian government claim that shocks originating in foreign markets were primarily responsible for its stock market panic during September-October 2008. Using financial contagion framework, the results indicate that the Russian stock market is weakly integrated with the US and European market in turn discarding the government claim. In bivariate market comparison, the results indicate that Russian market emits high level of shocks affecting the correlation structure between Russia and foreign markets while the reverse is true in case of volatility spillover effects. It is concluded that the governments should not use the justification of foreign shocks affecting the local markets during global crisis. Akin to foregoing analysis, we look at the transmission of shock and volatility in the Real Estate Investment Trust (REIT) markets. Since by law REITs are required to invest a large portion of their investments in real estate, the role of REITs in spreading the subprime mortgage crisis across the globe has been assessed. The initial analysis indicates that during crisis all markets are granger causing each other. The result is in compliance with the widely held view that the stock markets behave alike during global crisis. Next the integration between USREITs and global REITs and S&P500 has been examined. The results indicate USREITs is weakly integrated with the global REITs implying low level of bidirectional shock and volatility spillover while the reverse is true in case of USREITs- S&P500. Finally the integration between S&P500 and global REITs has been explored. The results suggest weak integration between S&P500 and global REITs. The shocks are mainly transmitted from S&P500 to global REITs. Over all the study concludes that neither USREITs nor S&P500 can create a wider panic in the global REIT markets during crisis. These weak linkages points towards portfolio diversification benefits as well.Studying the crisis at the next level, we analyze short-run as well as long-run relationship between crude oil price and stock markets for Brazil, Russia, India and China (BRIC) within a constrained structural modeling framework. Our findings indicate that BRIC stock markets to certain extent follow the efficient market hypothesis such that in case of oil importing country a positive oil price shock cause the stock market to fall and the reverse is true for an oil exporting country. Two important behaviors have been identified related to short-run interest rate and industrial production. The rise in oil prices generate inflation which is countered by increase in short-run interest rate. At the same time, industrial production tends to increase in real terms instead of decreasing in view of oil price shock (increase in oil price). The result can be attributed to hedging oil price risk with physical delivery. Once the hedge contract starts expiring after 30, 90 or 180 days the impact of oil price starts reducing the industrial production. The second objective of the dissertation is to study the government intervention specifically in the stock markets and generally in the economy. From stock market perspective, we analyze the case of Russian government repeated intervention in its national stock markets during late 2008. Using event-study methodology the findings indicate weak evidence that government intervention can in fact prevent stock market from external financial shocks. The study strongly recommends that the governments should not intervene during stock market crisis.Studying the case of general economy, a new idea has been developed and floated regarding central bank’s intervention directed to preempt an Asset Price Bubble (APB). The economic theory regarding central bank monetary policy intervention has been found to suffer from various problems in the event an APB occurs, such as, -time lag, -cannot affect bubbled sector alone as well as –irrelevance of traditional bank-lending channel. To deal with these issues the study brings forward the idea of regulatory intervention based on certain text book assumptions. The idea entails that contrary to traditional monetary policy intervention, the central bank should impose credit exposure limits for a particular sector on credit institutions. These limits should be imposed once the central bank finds out the abnormal increase in prices in a given sector of the economy. Our preliminary findings suggest that idea of regulatory intervention has the potential to preempt the APB

The objective of this thesis is to examine real exchange rate volatility, with a particular focus on investigating the causes of exchange rate jumps. While the predominant approach in the literature is to examine the interaction between nominal rigidities and nominal shocks, this thesis examines the volatility that arises from real rigidities and shocks. Trying to better understand the transmission of real shocks to the exchange rate is a worthwhile task, given the substantial evidence that these shocks and rigidities are important for explaining other economic fluctuations. This thesis develops theoretical models that examine the contributions of specific real rigidities to exchange rate volatility. Chapter 1 introduces our baseline specification - a frictionless model, with the exception of capital adjustment costs. This baseline generates very mild exchange rate fluctuations. Additional rigidities are required to generate volatility of the magnitude that is typically observed. Chapter 2 finds that introducing imperfect asset substitutability - specifically, home asset bias - goes a little towards achieving this. When investors are biased, the exchange rate must adjust by more to equilibrate asset markets. This greater burden of adjustment on the exchange rate along the short run path typically translates to larger jumps after shocks. Similarly, Chapter 3 shows that augmenting the baseline with banks and financial frictions raises exchange rate volatility. The key point is that, in the presence of financial frictions, there is a risk premium that widens after negative shocks, increasing the required adjustment of the exchange rate. A fourth chapter extends Chapter 3 and shows that unconventional credit policy, while beneficial in some respects, nonetheless entails nontrivial costs because it invites moral hazard by encouraging banks to be more highly leveraged, which increases exchange rate and consumption volatility. So, the overall message is that, in the presence of plausible real frictions - including (i) capital adjustment costs, (ii) imperfect asset substitutability, and (iii) financial frictions - real shocks can generate a plausibly significant degree of real exchange rate volatility. This thus posits an additional explanation of exchange rate jumps that complements the predominantly monetary literature.

This research assessed market efficiency, volatility behaviour, asset pricing, and oil price risk exposure of the oil and gas companies quoted on the London Stock Exchange with the aim of providing fresh evidence on the pricing dynamics in this sector. In market efficiency analysis, efficient market hypothesis (EMH) and random walk hypothesis were tested using a mix of statistical tools such as Autocorrelation Function, Ljung-Box Q-Statistics, Runs Test, Variance Ratio Test, and BDS test for independence. To confirm the results from these parametric and non-parametric tools, technical trading and filter rules, and moving average based rules were also employed to assess the possibility of making abnormal profit from the stocks under study. In seasonality analysis, stock returns were tested for the day-of-the-week and month-of-the-year effects. Volatility processes, estimation, and forecasting were undertaken using both asymmetric and symmetric volatility models such as GARCH (1,1) and Threshold ARCH or TARCH (1,1,1) to investigate the volatility behaviour of stock returns. To determine the effect of an exogenous variable on volatility, Brent crude oil price was used in the models formulated as a variance regressor for the assessment of its impact on volatility. The models were then used to forecast the price volatility taking note of the forecasting errors for the determination of the most effective forecasting model. International oil price risk exposure of the oil and gas sector was measured using a multi-factor asset pricing model similar to that developed by Fama and French (1993). Factors used in the asset pricing model are assessed for statistical significance and relevance in the pricing of oil and gas stocks. Data used in the study were mainly the adjusted daily closing prices of oil and gas companies quoted on the exchange. Five indices of FTSE All Share, FTSE 100, FTSE UK Oil and Gas, FTSE UK Oil and Gas Producers, and FTSE AIM SS Oil and Gas were also included in the analysis. Our findings suggest that technical trading rules cannot be used to gain abnormal returns, which could be regarded as a sign for weak form market efficiency. The results from seasonality analysis have not shown any day-of-the-week or monthly effect in stock returns. The pattern of stock returns’ volatility can be estimated and forecasted, although the relationship between risk and return cannot be generalised. On a similar note, the relationship between volatility attributes and the efficient market hypothesis cannot be clearly established. However, we have established that volatility modelling can significantly measure the quantum of risk in the oil and gas sector. Market risk, oil price risk, size and book-to-market related factors in asset pricing models were found to be relevant in the determination of asset prices of the oil and gas companies.

Do market frictions influence asset prices? The first part examines whether financial intermediaries’ balance sheet capacity, their funding liquidity, can influence market liquidity, volatility, and price patterns. Using a historical case study this part suggests that when a liquidity provider is balance sheet constrained, markets become illiquid and prices move. The second part looks at Germany’s 1927 stock market crash. It sheds light on the relationship between leverage and asset price behavior. The results indicate that a bank’s credit policy influenced asset prices – an expansive policy dampened volatility and increased returns. A sharp cut in margin credit led to larger price fluctuations. The third part looks at the connection between the financial side and the real side of the economy. Testing the theory of rational bubbles, it suggests that in 18th century England government debt increased consumers’ welfare by giving them a safe store of value.
Les friccions del mercat influeixen en els preus dels actius financers? La primera part examina si la capacitat del balanç ̧ dels intermediaris financers, si la seva liquiditat financera, pot influir sobre la liquiditat del mercat, la volatilitat i l’estructura de preu. Mitjançant l’estudi d’un cas históric aquesta part suggereix que quan un proveidor de liquiditat té limitat el balanç, els mercats passen a no tenir liquiditat i els preus varien. La segona part se centra en el crac borsari alemany el 1927. Explica la relació entre l’apalancament i el comportament dels preus dels actius financers. El resultat indica que la poltica de crédit dels bancs va influir els preus dels actius financers una política expansiva reduia la volatilitat i feia augmentar els rendiments. Una gran retallada en el marge de crédit portava a majors fluc- tuacions dels preus. La tercera part analitza la connexió entre el sector financer i el sector real de l’economia. La teoria de les bombolles racionals suggereix que a l’Anglaterra del segle divuit el deute del govern va fer augmentar el benestar dels consumidors, tot donant-los dipósits de valor segurs.

Understanding stock-bond return correlation is a key facet in asset mix, asset allocation and in an investor’s portfolio optimisation strategy. For the last couple of decades, several studies have probed this cardinal relationship. While initial literature tries to understand the fundamental pattern of co-movements, later studies aim to model the economic state variables influencing such time-varying volatility behaviour of stock-bond returns. This study provides a systematic literature review in the field of stock and bond return correlation. The review investigates the existing literature in three key dimensions. First, it examines the effect of macro-economic variables on SB return co-movements. Second, it illustrates the effect of financial integration on the asset correlation dynamics. Third, it reviews the existing models that are employed to estimate the dynamic relationship. In addition to the systematic review, I conduct an empirical analysis of stock-bond return co-movements on U.S. capital market. Both the literature and the empirical investigation substantiate my claims on existing research gaps and respective scope for further research. Evidence shows that existing models impose strong restrictions on past stock-bond return variance dynamics and yield inconclusive results. I, therefore, propose an alternative method, i.e. copula function approach, to model stock and bond time-varying co-movements. Since the previous studies largely focus on developed economies, I suggest an empirically investigation of emerging economies as well. This will allow me to examine the effect of financial integration on the dynamic asset return correlation. Apart from this academic contribution, the study provides an illustration of the economic implications which relate to portfolio optimization and minimal-risk hedge ratio.

O objetivo deste trabalho foi estudar o comportamento da volatilidade do preço da soja negociada em contratos futuros na BM&FBOVESPA (série SFI). O estudo foi realizado por meio da comparação entre duas abordagens: na primeira, foi utilizada a série de retornos absolutos da série em questão para representar a volatilidade da mesma, que se mostrou persistente ao longo do tempo, comprovando o fato de que a série possui o comportamento de memória longa. Por ter apresentado tal comportamento, fez-se necessária a utilização de modelos ARFIMA (\"Autorregressivos Fracionários Integrados de Médias Móveis\") estes, que são capazes de capturar de maneira efetiva tal comportamento. Ainda dentro desta abordagem, os modelos foram estimados de duas maneiras distintas: a primeira, em que todos os parâmetros foram estimados simultaneamente e a segunda, em que primeiramente foi estimado o parâmetro de memória longa, diferenciada a série e, posteriormente, foram ajustados os modelos ARIMA nos dados diferenciados. Por fim, a segunda abordagem utilizada no trabalho é a mais comum em pesquisas acadêmicas: foi realizada a estimação dos modelos GARCH (\"Autorregressivos Generalizados de Heteroscedasticidade Condicional\") diretamente na série de retornos. Neste estudo, concluímos que a primeira abordagem se mostrou mais eficiente, dados os critérios de comparação utilizados.
The purpose of this article was to study the volatility of the soybean price traded in futures contracts on the BM&FBOVESPA (SFI series). The study was conduct by comparison between two approaches: first, was use the series of absolute returns of the respective series, to represent its volatility, which was persistent over time, proving the fact that the series has a long memory behavior. Because of such behavior, it was necessary to use ARFIMA models (\"Autoregressive Fractional Integrated Moving Average\"), which are able to capture effectively such behavior. Still using this approach, the models were estimate in two different ways: first, which all parameters were estimate simultaneously, and the second one, that was first estimated the long memory parameter, differentiated the series and, later, adjusted the ARIMA models in differentiated data. Finally, the second approach used in this work is the most common in academic research: the estimation of GARCH models (\"Generalized Autoregressive Conditional Heretoscskedasticity\") directly in the returns series of the studied series. In this study, we conclude that the first approach was more effective, given the comparison criteria used.

I denna studie undersöks guld som safe haven-tillgång och om den erbjuder tillflykt mot volatilitet, vilket är studiens huvudsakliga syfte. För att åstadkomma detta används data från VIX- och GVZ-indexet samt priset på guld under perioden 1994–2018. Guldets egenskaper testas mot safe haven-teorin via ett kvantitativt angreppsätt där korrelation, avkastning och standardavvikelse är ämne för undersökning i utvalda perioder. Guld visar sig bevara sitt värde under oroligheter – vilket syns genom att den genomsnittliga avkastningen för guld är högre när VIX befinner sig på höga nivåer. Dock misslyckas tillgången att förbli lågvolatil då guldets volatilitet stiger i takt med den förväntade volatiliteten på aktiemarknaden. Den förväntade volatiliteten i guldpriset, uttryckt genom GVZ, korrelerar dessutom med den förväntade volatiliteten på aktiemarknaden mätt av VIX-indexet. Däremot upptäcks knappt någon korrelation mellan guldets pris och förändringar i VIX-indexets värde.
The purpose of this study was to analyse whether gold fulfils the criteria for being a safe haven asset in certain conditions. Through the use of data pooled from CBOE’s VIX, GVZ and the spot price of gold, we employ a quantitative approach to analyse correlation, rate of return and standard deviation during times of market volatility. The chosen period for this analysis is the time between 1994 and 2018. Gold proved its ability to retain value during such conditions, which is evidenced by higher average returns when VIX has been at high levels. This strengthens its role as a safe haven asset. However, gold failed to keep a low level of volatility in periods of rising implied volatility on the stock market, as expressed by the VIX index. Moreover, the implied volatility of gold, expressed through the GVZ index, has shown a strong correlation with the VIX, indicating that gold is not a safe haven. Finally, the gold spot price was shown to have little to no correlation with changes in VIX.

To provide further evidences on jumps in asset prices, in this thesis we conduct an empirical analysis on high-frequency data from a stock index and consider the problem of identifying jumps at intraday intervals. Our approach generalizes two existing methods in the literature in terms of estimating spot volatility and of correcting for the spurious rejection problem due to multiple testing. The proposed procedure directly depends on a credible volatility model that we specify and calibrate from the index data. By simulating the volatility model, it is shown that a relevant parameter which governs the shape of the generalized extreme value (GEV) distribution determines the critical regions of jump tests. Empirical sizes of jump tests can then be held at nominal level approximately when the testing procedure is applied to high-frequency returns. We also study the dynamics of detected jumps and model their time-varying intensities with a linear self-exciting point process.

In this research, we will focus on investigating the relationship between risk and return. We will propose a new model which leads to a more sensible approach to modelling the relationship between risk and return under different market conditions. It is an extension of the traditional single-index capital asset pricing model (CAPM) which reads as: The return R[subscript]i on individual Security i can be decomposed into the specific return α[subscript]I + ε[subscript]i (expected specific return α[subscript]i and random specific return ε[subscript]i) and the systematic return β[subscript]iR[subscript]m owing to the common market return R[subscript]m.In our new model, we suggest a functional-beta single-index CAPM, extending the work of three-beta CAPM (Galagedera and Faff, 2004) that takes into account the condition of market volatility. Differently from the three-beta CAPM, we allow β[subscript]i changing functionally with the market volatility σ[subscript]m, which is more flexible and adaptable to the changing structure of financial systems. The main contributions of this thesis are summarised as follows:• A new functional-beta CAPM, taking into account the conditions of market volatility, is proposed under the framework of widely applicable data generating processes of near epoch dependence (NED).• A semi-parametric estimation procedure based on least squares local linear modelling technique under NED is suggested with the large sample distributions of the estimators established.• Simulation study is fully made, illustrating that the suggested estimation procedure for the proposed functional-beta CAPM under near epoch dependence can work well. It provides reasonable estimates of the functional beta in the condition of moderate market volatility.• By using a set of stocks data sets collected from Australian stock market in the past ten years, empirical evidences of the functional-beta CAPM in Australia are carefully examined under both nonparametric and parametric model structures. Differently from the three- or multi-beta (constant) CAPM in Galagedera and Faff (2005), our new findings show that the functional beta can be reasonably parameterized as threshold (regime-switching) linear functions of market volatility with two or three regimes of market condition. In the condition of extreme market volatility, a threshold functional-beta CAPM is suggested.The CAPM provides a usable measure of risk that helps investors determine what return they deserve for putting their money at risk. Our new model is no doubt helpful to better understand the relationship between risk and return under different market conditions. It can be potentially applied widely, for example, it may be useful both for market investors and financial risk managers in their investment/management decision-making.