Search

The paper proposes the thorough investigation of the in-sample and out-of-sample performance of four GARCH and two stochastic volatility models, which were estimated based on Russian financial data. The data includes Aeroflot and Gazprom’s stock prices, and the rouble against the US dollar exchange rates. In our analysis, we use the probability integral transform for the in-sample comparison, and a Mincer-Zarnowitz regression, along with classical forecast performance measures, for the out-of-sample comparison. Studying both the explanatory and the forecasting power of the models analyzed, we came to the conclusion that stochastic volatility models perform equally or in some cases better than GARCH models.

The illiquidity of long-maturity options has made it difficult to study the term structures of option spanning portfolios. This paper proposes a new estimation and inference framework for these option-implied term structures that addresses long-maturity illiquidity. By building a sieve estimator around the risk-neutral valuation equation, the framework theoretically justifies (fat-tailed) extrapolations beyond truncated strikes and between observed maturities while remaining nonparametric. New confidence intervals quantify the term structure estimation error. The framework is applied to estimating the term structure of the variance risk premium and finds that a short-run component dominates market excess return predictability.

We build a model of an IPO for firms with private information about their earnings profile over time and test the model’s predictions using a complete sample of newly listed Chinese companies between 1992 and 2007. The model predicts that IPO size is positively correlated with short-term operating performance that is not directly consistent with traditional theories. It also provides an explanation for negative correlation between debt and profitability that is not consistent with standard trade-off theory or signaling theory. The empirical results provide strong support for our model.

We introduce a dynamic statistical model for Skellam distributed random variables. The Skellam distribution can be obtained by taking differences between two Poisson distributed random variables. We treat cases where observations are measured over time and where possible serial correlation is modeled via stochastically time-varying intensities of the underlying Poisson counts. The likelihood function for our model is analytically intractable and we evaluate it via a multivariate extension of numerically accelerated importance sampling techniques. We illustrate the new model by two empirical studies and verify whether our framework can adequately handle large data sets. First, we analyze long univariate high-frequency time series of U.S. stock price changes, which evolve as discrete multiples of a fixed tick size of one dollar cent. In a second illustration, we analyze the score differences between rival soccer teams using a large, unbalanced panel of seven seasons of weekly matches in the German Bundesliga.In both empirical studies, the new model provides interesting and non-trivial dynamics with a clear interpretation.

We consider joint estimation of conditional Value-at-Risk (VaR) at several levels, in the framework of general conditional heteroskedastic models. The volatility is estimated by Quasi-Maximum Likelihood (QML) in a first step, and the residuals are used to estimate the innovations quantiles in a second step. The joint limiting distribution of the volatility parameter and a vector of residual quantiles is derived. We deduce confidence intervals for general Distortion Risk Measures (DRM) which can be approximated by a finite number of VaR’s. We also propose an alternative approach based on non Gaussian QML which, although numerically more cumbersome, has interest when the innovations distribution is fat tailed. An empirical study based on stock indices illustrates the theoretical findings

Systemic risk is a very important but very complex notion in banking and how to measure it adequately is challenging. We introduce a new framework for measuring systemic risk by using a risk-adjusted balance sheet approach. The measure models credit risk of banks as a put option on bank assets, a tradition that originated with Merton. We conceive of an individual bank’s systemic risk as its contribution to the potential sector-wide net. In this regard, the analysis of public commercial banks operating in 7 countries from Central and Eastern Europe, shows potential risk which could threaten all the financial system. The paper shows how risk management tools can be applied in new ways to measure and analyze systemic risk in European banking system. The research results is a systemic risk map for the CEE banking systems. The study finds also instability of systemic risk determinants.

Modeling crude oil volatility is of substantial interest for both energy researchers and policy makers. Many authors emphasize the link between this volatility and some exogenous economic variables. This paper aims to investigate the impact of the U.S. Federal Reserve monetary policy on crude oil future price (COFP) volatility. By means of the recently proposed generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) model, the Effective Federal Fund Rate (EFFR) - as a proxy of the monetary policy - is plugged into the mean-reverting unit GARCH(1,1) model. Strong evidence of an inverse relation between the EFFR and COFP volatility is found. This means that an expansionary monetary policy is associated with an increase of the COFP volatility. Conjecturing that the unusual behavior of the COFP in 2007-2008 was driven by a monetary policy shock, we test the presence of mildly explosive behavior in the prices. The sup Augmented Dickey-Fuller test (SADF) confirms the presence of a bubble in the COFP series that started in October 2007 and ended in October 2008. We expect that the COFP-EFFR association could be affected by such a bubble. Therefore, we apply the same experimental set-up to two sub-samples - before and after October 2007. Interestingly, the results show that EFFR influence on COFP volatility is greater in the aftermath of the bubble.

This article attempts to examine whether the equity premium in the United States can be predicted from a comprehensive

What determines the risk structure of financial portfolios of German households? In this paper we estimate the determinants of the share of financial wealth invested in three broad risk classes. We employ a new econometric approach - the so called fractional multinomial logit model - which allows for joint estimation of shares while accounting for their fractional nature. We extend the model to allow for unobserved heterogeneity across households via maximum simulated likelihood. We find that self-assessed appetite for risk as well as the level of wealth have strong positive effects on the riskiness of the average household’s portfolio. These findings largely stay true even after we control for the potential confounding effects of unobserved differences across households via correlated random effects.

In this paper, we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly Gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&amp, P 500 index and eight other indexes and stocks.

The rare disaster hypothesis suggests that the extraordinarily high postwar U.S. equity premium resulted because investors ex ante demanded compensations for unlikely but calamitous risks that they happened not to incur. While convincing in theory, empirical tests of the rare disaster explanation are scarce. We estimate a disaster-including consumption-based asset pricing model (CBM) using a combination of the simulated method of moments and bootstrapping. We consider several methodological alternatives that differ in the moment matches and the way to account for disasters in the simulated consumption growth and return series. Whichever specification is used, the estimated preference parameters are of an economically plausible size, and the estimation precision is much higher than in previous studies that use the canonical CBM. A comparable combination of plausibility and estimation precision has not been delivered in the related literature. Our results thus provide empirical support for the rare disaster hypothesis, and help restore the nexus between real economy and financial markets implied by the consumption-based asset pricing paradigm.

We consider improving estimating parameters of diffusion processes for interest rates by incorporating information in bond prices. This is designed to improve the estimation of the drift parameters, which are known to be subject to large estimation errors. It is shown that having the bond prices together with the short rates leads to more efficient estimation of all parameters for the interest rate models. It enhances the estimation efficiency of the maximum likelihood estimation based on the interest rate dynamics alone. The combined estimation based on the bond prices and the interest rate dynamics can also provide inference to the risk premium parameter. Simulation experiments were conducted to confirm the theoretical properties of the estimators concerned. We analyze the overnight Fed fund rates together with the U.S. Treasury bond prices. Supplementary materials for this article are available online.

Hedging strategies have become more and more complicated as assets being traded have become more interrelated to each other. Thus, the estimation of risks for optimal hedging does not involve only the quantification of individual volatilities but also include their pairwise correlations. Therefore a model to capture the dynamic relationships is necessary to estimate and forecast correlations of returns through time. Engle’s dynamic conditional correlation (DCC) model is compared with other models of correlation. Performance of the correlation models are evaluated in this paper using only the daily log returns of the closing prices of the Peso-Dollar Exchange Rate and Philippine Stock Exchange index. Ultimately, Engle’s DCC model is adopted because of its consistency with expectations. Though generally negative, correlation between these two returns is not really constant as the results indicated. The forecast evaluation of the models was divided into in-sample and out-of-sample forecast performance with short-term (i.e., 22-day, 60-day, and 125-day) and medium-term (250-day and 500-day) rolling window correlations, or realized correlations, as proxies for the actual correlation. Based on the root mean squared error and mean absolute error, the integrated DCC model showed optimal forecast performance for the in-sample correlation patterns while the mean-reverting DCC model had the most desirable forecast properties for dynamic long-run forecasts. Also, the Diebold-Mariano tests showed that the integrated DCC has greater predictive accuracy in terms of the 3-month realized correlations than the rest of the models.

The long-run consumption risk (LRR) model is a promising approach to resolve prominent asset pricing puzzles. The simulated method of moments (SMM) provides a natural framework to estimate its deep parameters, but caveats concern model solubility and weak identification. We propose a twostep estimation strategy that combines GMM and SMM, and for which we elicit informative macroeconomic and financial moment matches from the LRR model structure. In particular, we exploit the persistent serial correlation of consumption and dividend growth and the equilibrium conditions for market return and risk-free rate, as well as the model-implied predictability of the risk-free rate. We match analytical moments when possible and simulated moments when necessary and determine the crucial factors required for both identification and reasonable estimation precision. A simulation study|the first in the context of long-run risk modeling|delineates the pitfalls associated with SMM estimation of a non-linear dynamic asset pricing model. Our study provides a blueprint for successful estimation of the LRR model.

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact the Dynamic Conditional Correlation (DCC) model is extended by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to the clustering structure, and the second focusing on dynamic parameters. Differently from the traditional two-step DCC estimation, large system feasibility of the joint estimation of the whole set of model's dynamic parameters is achieved. A new approach to the clustering of GARCH processes is also introduced. Such an approach embeds the asymptotic properties of the univariate quasi-maximum-likelihood GARCH estimators into a Gaussian mixture clustering algorithm. Unlike other GARCH clustering techniques, the proposed method provides a natural estimator of the number of clusters.

We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model that incorporates realized measures of variances and covariances. Realized measures extract information about the current levels of volatilities and correlations from high-frequency data, which is particularly useful for modeling financial returns during periods of rapid changes in the underlying covariance structure. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model specification of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than usually found with rolling-window regressions based exclusively on daily returns. In the empirical part of the paper, we examine the cross-sectional as well as the time variation of the conditional beta series during the financial crises.

The article provides a rationale for carrying out the analysis of investment attractiveness of a bank at choosing a target bank for merger or acquisition. The author's own methodology is suggested for bank investment attractiveness assessment which enables a well-grounded bank choice for such a type of agreement considering its comparable investment attractiveness.

This paper proposes a nonlinear impulse-response matching procedure explicitly designed to estimate nonlinear dynamic models, and illustrates its applicability by estimating a macro-fi nance model of asset pricing under skewness risk. As auxiliary model, a new class of nonlinear vector autoregressions (NVAR) based on Mittnik (1990) is proposed.

Se parametriza de forma conjunta la heteroscedasticidad condicional autorregresiva generalizada que corresponde al comportamiento de la varianza de tres variables: (a) el índice de precios y cotizaciones (IPC), indicador principal del mercado bursátil mexicano, (b) el emerging markets bond index para México (EMBI), como indicador de riesgo país y (c) el precio de la canasta mexicana de tres crudos de exportación (MEZCLA). Las variables se emplean como estimadores de la tendencia de los precios de las acciones, los bonos y los energéticos, respectivamente, con el objetivo final de conformar un portafolio de inversión diversificado que incluya dichos activos. Se presentan los resultados empíricos de un modelo econométrico GARCH trivariado asimétrico. El modelo permite incorporar la covarianza entre las variables para explicar su interrelación y en la estimación se considera el efecto de los choques generados por las innovaciones positivas y negativas. El estudio contempla el periodo de 2002 a 2013.------------------------------------We jointly parameterized the generalized autoregressive conditional heteroskedasticity that corresponds to the behavior of the variance of three variables: (a) the core Mexican stock market index (IPC), (b) the Emerging Markets Bond Index for Mexico (EMBI) as country risk pointer and, (c) the Mexican three oil basket exports mix (MEZCLA). The variables are used as trend indicators of stocks, bonds and energetics respectively with the ultimate goal of forming a diversified portfolio including such assets. This paper presents the empirical results of an asymmetric econometric trivariate GARCH model. The model incorporates the covariance between the variables in order to explain their relationship and we considered the shocks generated by positive and negative innovations. The study involves the period 2002- 2013., Artículo revisado por pares

First externalities risk due to the size of the companies or the principle that large companies are also at risk of bankruptcy (too big to fail) are examined. The problem is illustrated by a case in which extreme risks with negative consequences for savers and investors are taken. If we accept-so conservatively that the risk exposure of a company is limited by its capital, while -ocasionales- external losses may adversely affect the general public, have placed to explain how and why the big break companies; or better understand why the big break also. In particular, considering the conditions to contain the risk foreseeable losses with positive externalities, then, what can happen with negative derivatives risk capital. Following Taleb / Tapiero, hypotheses are contrasted based on partial information of firms had losses (including external risk factors); the policy implications of this analysis are projected after evaluating two fundamental issues that continue to preoccupy the public opinion: how failures occur in markets for the case of large firms, corporations or companies, and what are the criteria for regulation and rescue available to governments, institutions and citizens to control them.

We investigate the long-run stock-bond correlation using a novel model that combines the dynamic conditional correlation model with the mixed-data sampling approach. The long-run correlation is affected by both macro-finance variables (historical and forecasts) and the lagged realized correlation itself. Macro-finance variables and the lagged realized correlation are simultaneously significant in forecasting the long-run stock-bond correlation. The behavior of the long-run stock-bond correlation is very different when estimated taking the macro-finance variables into account. Supporting the flight-to-quality phenomenon for the total stock-bond correlation, the long-run correlation tends to be small/negative when the economy is weak.

We define the vector of conditional coverage values generated over the business cycle by a constant capital figure. Using a convenient analytical framework, we explore its properties and propose two applications based on it. For the former, we state a result that links the concepts of conditional and unconditional solvency and offers an alternative interpretation of the unconditional capital. For the latter, we propose using the minimum of the conditional coverage vector in the determination of long-term capital requirements, as well as using its minimum and its standard deviation in the long-term assessment of a given capital figure. Both applications are illustrated empirically. The entire analysis can be understood as an attempt to recognize and incorporate capital cyclicality into the measurement and analysis of default risk.

This paper examines whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002-2012. In the univariate portion of the paper, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures. In the multivariate context, the single-state and Markov-switching models are comparatively assessed according to their usefulness in constructing optimal stock portfolios. Accounting for structural breaks in the conditional variance process, conventional GARCH effects remain important in capturing heteroscedasticity. However, those univariate models including a GARCH term perform comparatively poorly when used for forecasting purposes. In the multivariate study, the use of Markov-switching variance-covariance estimates improves risk-adjusted portfolio returns relative to portfolios constructed using the more conventional single-state models. While there is evidence that some Markov-switching models can provide better forecasts and higher risk-adjusted returns than those models which include GARCH effects, the inability of the simpler Markov-switching models to fully capture heteroscedasticity in the data remains problematic.

This research considers the strategies on the initial public offering of company equity at the stock exchanges in the imperfect highly volatile global capital markets with the nonlinearities. We provide the IPO definition and compare the initial listing requirements on the various markets. We analyze the IPO techniques: the fixed-price offerings, auctions, book-building. We focus on the IPO initial underpricing, long-run performance and after market liquidity problems. 1. We propose that the information absorption by the investors occurs in the evolving learning process about the company’s value, taking to the consideration the fundamental purpose of investing and the responsibilities of investors. 2. We think that the information absorption capacity by the investors on the IPOs impacts the investor’s investment decisions and serves as a pre-determinant for the successful IPO deal completion. We propose the Ledenyov theory on the origins of the IPO underpricing and long term underperformance effects, which states that the IPO underpricing and long term underperformance can be explained by the changing information absorption capacity by the investors on the IPO value. 3. We think that the IPO winning virtuous investment strategies can only be selected by the investors with the highest information absorption capacity through the decision making process on the IPO investment choices at the selected stock exchange in the imperfect highly volatile global capital markets with the nonlinearities; applying the econophysical econometrical analysis with the use of the inductive, deductive and abductive logics in the frames of the strategic choice structuring process, that is the winning through the distinctive choices process.

This article examines the nature of the empirical instability in dynamic term structure models. I show that using survey forecasts is an effective solution because it directly addresses the information imbalance at the heart of the instability: it increases the (cross-section) information on actual dynamics, bridging the gap with the large (cross-section) information on the risk-adjusted dynamics. I relate this to other information spanning problems, particularly spanning of macro factors, and discuss the desirability of anchoring models to surveys. I also show that restricting prices of risk is not effective in ensuring stable and sensible implied expectations.

In the Schumpeterian creative disruption age, the authors firmly believe that an increasing application of electronic technologies in the finances opens a big number of new unlimited opportunities toward a new era of the ultra high frequency electronic trading in the foreign currencies exchange markets in the conditions of the discrete information absorption processes in the diffusion - type financial systems with the induced nonlinearities. Going from the academic literature, we discuss the probability theory and the statistics theory application to accurately characterize the trends in the foreign currencies exchange rates dynamics in the short and long time periods. We consider the financial analysis methods, including the macroeconomic analysis, market microstructure analysis and order flow analysis, to forecast the volatility in the foreign currencies exchange rates dynamics in the short and long time periods. We discuss the application of the Stratanovich-Kalman-Bucy filtering algorithm in the Stratanovich – Kalman – Bucy filter and the particle filter to accurately estimate the time series and predict the trends in the foreign currencies exchange rates dynamics in the short and long time periods. We research the influence by discrete information absorption on the ultra high frequency electronic trading strategies creation and execution during the electronic trading in the foreign currencies exchange markets. We formulate the Ledenyov law on the limiting frequency (the cut-off frequency) for the ultra high frequency electronic trading in the foreign currencies exchange markets.

In this paper we consider several time-varying volatility and/or heavy-tailed models to explain the dynamics of return time series and to fit the volatility smile for exchange-traded options where the underlying is the main �Borsa Italiana� stock index. Given observed prices for the time period we investigate, we calibrate both continuous-time and discrete-time models. First, we estimate the models from a time-series perspective (i.e. under the historical probability measure) by investigating more than ten years of daily index price log-returns. Then, we explore the risk-neutral measure by fitting the values of the implied volatility for numerous strikes and maturities during the highly volatile period from April 1, 2007 (prior to the subprime mortgage crisis in the U.S.) to March 30, 2012. We assess the extent to which time-varying volatility and heavy-tailed distributions are needed to explain the behavior of the most important stock index of the Italian market.

We use the Conditionally Exponential Decay (CED) model to explain the scaling behavior in currency exchange (FX) rates. This approach enables us not only to show that FX returns satisfy scaling with an exponent qualitatively different from that of a random walk, but also to identify the distributions of these returns corresponding to the empirical scaling laws. The study is conducted via three different estimation methods and using intra-daily FX data which offers the great advantage of large samples and high significance.

This study accounts for the time-varying pattern of price shock transmission, exploring stock market co-movements using continuous wavelet coherency methodology to find the correlation analysis between stock market indices of Malaysia, Thailand (Asian), Greece (Europe) and United States, in the time-frequency domain of time-series data. We employ the Wavelet Coherence method with the consideration of the financial crisis episodes of 1997 Asian Financial Crisis, 1998 Russian Sovereign Debt Default, 9/11 Attack on World Trade Centre US, 2008 US Sub-Prime Mortgage Crisis and the recent 2010-2011 Greece Debt Crisis. Results tend to indicate that the relations among indices are strong but not homogeneous across time scales, that local phenomena are more evident than others in these markets and that there seems to be no quick transmission through markets around the world, but a significant time delay. The relations among these indices have changed and evolved through time, mostly due to the financial crises that occurred at different time periods. Results also favour the view that regionally and economically closer markets exhibit higher correlation and more short run co-movements among them. The high correlation between the two regional indices of Malaysia and Thailand, indicates that for the international investors, it is little gain to include both in their portfolio diversification. Strong co-movement is mostly confined to long-run fluctuations favouring contagion analysis. This indicates that shocks in the high frequency but low period are short term but shocks in the low frequency but high period are long term with the trend elements affecting the co-movements of the indices. The study of market correlations on the frequency-time scale domain using continuous wavelet coherency is appealing and can be an important tool in decision making for different types of investors.

This paper shows how to use adaptive particle filtering and Markov chain Monte Carlo methods to estimate quadratic term structure models (QTSMs) by likelihood inference. The procedure is applied to a quadratic model for the United States during the recent financial crisis. We find that this model provides a better statistical description of the data than a Gaussian affine term structure model. In addition, QTSMs account perfectly for the lower bound whereas Gaussian affine models frequently imply forecast distributions with negative interest rates. Such predictions appear during the recent financial crisis but also prior to the crisis.

Empirical evidence from time series methods which assume the usual I(0)/I(1) paradigm suggests that the efficient market hypothesis, stating that spot and futures prices of a commodity should co-integrate with a unit slope on futures prices, does not hold. However, these statistical methods are known to be unreliable if the data are fractionally integrated. Moreover, spot and futures price data tend to display clear patterns of time-varying volatility which also has the potential to invalidate the use of these methods. Using new tests constructed within a more general heteroskedastic fractionally integrated model we are able to find a body of evidence in support of the efficient market hypothesis for a number of commodities. Our new tests are bootstrap implementations of score-based tests for the order of integration of a fractionally integrated time series. These tests are designed to be robust to both conditional and unconditional heteroskedasticity of a quite general and unknown form in the shocks. We show that neither the asymptotic tests nor the analogues of these which obtain from using a standard i.i.d. bootstrap admit pivotal asymptotic null distributions in the presence of heteroskedasticity, but that the corresponding tests based on the wild bootstrap principle do. A Monte Carlo simulation study demonstrates that very significant improvements in finite sample behaviour can be obtained by the bootstrap vis-Ã -vis the corresponding asymptotic tests in both heteroskedastic and homoskedastic environments.

This paper presents and estimates an innovative term structure model where inflation expectations and inflation risk premia are strictly interconnected with both the timevarying volatility of interest rates and investors’ expectations of future GDP growth. The estimation of the model is based on U.S. data over the 1999 to 2012 sample period. Distinct from previous studies, the empirical work explicitly considers data on both the implied volatility of Treasury bonds and survey forecasts of GDP growth, as well as data on nominal Treasury yields, TIPS yields and survey forecasts of CPI inflation. The estimated inflation risk premia, which are relatively low and less volatile with respect to earlier empirical evidence, are negatively related to the volatility of interest rates and have a strongly positive link with the stochastic conditional mean of GDP growth.

Article aims to demonstrate the significant impact of dynamics of the relationship between financial intermediaries on the level of market volatility. Particularly important are the growing share of the links between hedge funds and other financial institutions. In order to demonstrate the dynamic test was presented Granger causality, which allows the statistical analysis of cause and effect relationships in the risk spread in the financial system. Using multiple regression analysis study was calculated the impact of the hedge fund market development measured in assets, leverage, the price volatility in various financial markets). Due to data availability study has been limited to 10-year period of analysis (2001-2011). The results show a significant correlation between the volatility in the stock market, bonds and CDS, and the activities of hedge funds on financial markets.

A very general stochastic volatility (SV) model specification with leverage, heavy tails, skew and switching regimes is proposed, using realized volatility (RV) as an auxiliary time series to improve inference on latent volatility. The information content of the range and of implied volatility using the VIX index is also analyzed. Database is the S&P 500 index. Asymmetry in the observation error is modeled by the generalized hyperbolic skew Student-t distribution, whose heavy and light tail enable substantial skewness. Resulting number of regimes and dynamics differ dependent on the auxiliary volatility proxy and are investigated in-sample for the financial crash period 2008/09 in more detail. An out-of-sample study comparing predictive ability of various model variants for a calm and a volatile period yields insights about the gains on forecasting performance from different volatility proxies. Results indicate that including RV or the VIX pays off mostly in more volatile market conditions, whereas in calmer environments SV specifications using no auxiliary series outperform. The range as volatility proxy provides a superior in-sample fit, but its predictive performance is found to be weak.

The condensed research article presents some innovative research results on the venture capital optimal investment portfolio strategies selection in the diffusion-type financial systems in the imperfect highly volatile global capital markets with the incomplete information, which are characterized by the asymmetric information flows and impacted by the various types of the nonlinearities. We discuss the venture capital firms with the different organizational structures: the corporation funded venture capital firm, investment bank funded venture capital firm, private equity funded venture capital firm, state funded venture capital firm. We consider the complicated issues on the venture capital optimal investment portfolio strategies selection, evaluation of the possible returns on the investments, and implementation of exit strategies for the venture capital investment schemes. We propose that the information signals can be mixed and self-modulated during the asymmetric information flows in the information transmission channels between the market agents, resulting in the origination of the various types of the nonlinearities such as the high order harmonics, which may have a considerable impact on the venture capital investments in the diffusion-type financial system. These nonlinearities have to be taken to an account during the venture capital optimal investment portfolio strategies selection process, which is all about making the right investment choices with the application of the inductive, deductive and abductive logics. In our opinion, the State of Queensland in Australia is a very attractive place to make the venture capital investments in the hi-tech startups, comparing to other regions in the World. We conclude with the notion that the venture capital can greatly improve the macroeconomic indicators of national economies, creating the new hi-tech industries, generating the abundant wealth, and increasing the Gross Domestic Product.

As an investor, we are interested in the relationship between economic and financial indicators. For this, for the investor, it is of utmost importance to identify the correct model for the long run and short run relationship, as this will determine the timing of entering and exiting the stock market. In this paper we investigate the correlation between the real stock price and the real industrial production index. The estimation of correlation coefficient would involve the panel data of nine (9) developing countries, including the four (4) BRIC countries, using data for the period 2008 to 2010. We employed the panel unit root test and panel cointegration tests using Eviews. We then proceed with the estimation of Fixed Effect (FE), Random Effect (RE), Pool Mean Group (PMG) and the Mean Group (MG) using Stata II command. The application of the heterogeneous panel model of Pool Mean Group (PMG) and the Mean Group (MG) – Im,Pesaran,Smith (IPS,1999) will allow for the heterogeneity effect among the different economies. Our findings proved that RE is superior to FE due to the inconsistency problem, which is the existence of correlation between missing cross sectional variables with the explanatory/regressor variables. The Hausman test performed supported this finding. We observed that the slope coefficients indicate a negative relationship between real industrial production and real stock price. Again, although both PMG and MG are consistent, Hausman test proved that MG is inefficient, and thus PMG is chosen for the final estimation. Finally, while we found out that in the short run the coefficient of industrial production varies with each country, they were the same in the long run.

Financial time series often exhibit properties that depart from the usual assumptions of serial independence and normality. These include volatility clustering, heavy-tailedness and serial dependence. A voluminous literature on different approaches for modeling these empirical regularities has emerged in the last decade. In this paper we review the estimation of a variety of highly flexible stochastic volatility models, and introduce some efficient algorithms based on recent advances in state space simulation techniques. These estimation methods are illustrated via empirical examples involving precious metal and foreign exchange returns. The corresponding Matlab code is also provided.

In this paper we investigate short-run co-movements before and after the Lehman Brothers’ collapse among the volatility series of US and a number of European countries. The series under investigation (implied and realized volatility) exhibit long-memory and, in order to avoid miss-specification errors related to the parameterization of a long memory multivariate model, we rely on wavelet analysis. More specifically, we retrieve the time series of wavelet coefficients for each volatility series for high frequency scales, using the Maximal Overlapping Discrete Wavelet transform and we apply Maximum Likelihood for a factor decomposition of the short-run covariance matrix. The empirical evidence shows an increased interdependence in the post-break period and points at an increasing (decreasing) role of the common shock underlying the dynamics of the implied (realized) volatility series, once we move from the 2-4 days investment time horizon to the 8-16 days. Moreover, there is evidence of contagion from the US to Europe immediately after the Lehman Brothers’ collapse, only for realized volatilities over an investment time horizon between 8 and 16 days.

Considering the financial theory based on cost-of-carry model, a futures contract price is always influenced by the spot price of its underlying asset, as long as the futures price is determined as the sum of the underlying asset's spot price and its cost of carrying or storing. The aim of this paper is to verify if there are dynamic connections between spot and futures prices as statued by the cost-of-carry model, and to identify the direction of causality.;The empirical analysis is conducted on three different commodity markets, namely crude oil, natural gas and gold. We estimate a battery of recursive bivariate VAR models over a sample of daily spot and futures prices ranging from January 1997 to September 2013. Using the recursive Grange-rcausality analysis, we show that some interactions between spot and futures prices clearly exist and they mainly depend on market type and futures contract's maturity.

The hedge fund represents a unique investment opportunity for the institutional and private investors in the diffusion-type financial systems. The main objective of this condensed article is to research the hedge fund’s optimal investment portfolio strategies selection in the global capital markets with the nonlinearities. We provide a definition for the hedge fund, describe the hedge fund’s organization structures and characteristics, discuss the hedge fund’s optimal investment portfolio strategies and review the appropriate hedge fund’s risk assessment models for investing in the global capital markets in time of high volatilities. We analyze the advanced techniques for the hedge fund’s optimal investment portfolio strategies replication, based on both the Stratonovich – Kalman - Bucy filtering algorithm and the particle filtering algorithm. We developed the software program with the embedded Stratonovich – Kalman - Bucy filtering algorithm and the particle filtering algorithm, aiming to track and replicate the hedge funds optimal investment portfolio strategies in the practical cases of the non-Gaussian non-linear chaotic distributions.

Estimation of log-GARCH models via the ARMA representation is attractive because it enables a vast amount of already established results in the ARMA literature. We propose an exponential Chi-squared QMLE for log-GARCH models via the ARMA representation. The advantage of the estimator is that it corresponds to the theoretically and empirically important case where the conditional error of the log-GARCH model is normal. We prove the consistency and asymptotic normality of the estimator, and show that, asymptotically, it is as efficient as the standard QMLE in the log-GARCH(1,1) case. We also verify and study our results in finite samples by Monte Carlo simulations. An empirical application illustrates the versatility and usefulness of the estimator.

This paper considers the location-scale quantile autoregression in which the location and scale parameters are subject to regime shifts. The regime changes are determined by the outcome of a latent, discrete-state Markov process. The new method provides direct inference and estimate for different parts of a nonstationary time series distribution. Bayesian inference for switching regimes within a quantile,via a three-parameter asymmetric-Laplace distribution, is adapted and designed for parameter estimation. The simulation study shows reasonable accuracy and precision in model estimation. From a distribution point of view, rather than from a mean point of view, the potential of this new approach is illustrated in the empirical applications to reveal the countercyclical risk pattern of stock markets and the asymmetric persistence of real GDP growth rates and real trade-weighted exchange rates.

A two-step approach for conditional Value at Risk (VaR) estimation is considered. In the first step, a generalized-quasi-maximum likelihood estimator (gQMLE) is employed to estimate the volatility parameter, and in the second step the empirical quantile of the residuals serves to estimate the theoretical quantile of the innovations. When the instrumental density $h$ of the gQMLE is not the Gaussian density utilized in the standard QMLE, or is not the true distribution of the innovations, both the estimations of the volatility and of the quantile are asymptotically biased. The two errors however counterbalance each other, and we finally obtain a consistent estimator of the conditional VaR. For a wide class of GARCH models, we derive the asymptotic distribution of the VaR estimation based on gQMLE. We show that the optimal instrumental density $h$ depends neither on the GARCH parameter nor on the risk level, but only on the distribution of the innovations. A simple adaptive method based on empirical moments of the residuals makes it possible to infer an optimal element within a class of potential instrumental densities. Important asymptotic efficiency gains are achieved by using gQMLE instead of the usual Gaussian QML when the innovations are heavy-tailed. We extended our approach to Distortion Risk Measure parameter estimation, where consistency of the gQMLE-based method is also proved. Numerical illustrations are provided, through simulation experiments and an application to financial stock indexes.

This paper investigates the main drivers of the agricultural commodity price volatility using the GARCH-MIDAS model of Engel et al. (2013), a new class of component models that allows for isolating the low-frequency component of volatility and taking into consideration macroeconomic factors via mixed data sampling. We show that modelling the agricultural price volatility as the product of high and low frequency components is more efficient than filtering it through a standard GARCH(1,1) model. After combing wheat, corn and soybean daily prices with monthly market-specific and common macroeconomic drivers over the period 1986-2012, it appears that supply-demand indicators and conventional speculation proxies are crucial in explaining the low-frequency component of volatility while monetary factors and energy markets play significant but less important role. Nevertheless, when we consider only the period following the recent price spikes (2006-2012), the monetary factors –especially interest rate – become essential to describe agricultural price fluctuations, suggesting also that the heterogeneity in the effects of the drivers on different crops is decreasing.

The central banks introduce and implement the monetary and financial stabilities policies, going from the accurate estimations of national macro-financial indicators such as the Gross Domestic Product (GDP). Analyzing the dependence of the GDP on the time, the central banks accurately estimate the missing observations in the financial time series with the application of different interpolation models, based on the various filtering algorithms. The Stratonovich – Kalman – Bucy filtering algorithm in the state space interpolation model is used with the purpose to interpolate the real GDP by the US Federal Reserve and other central banks. We overviewed the Stratonovich – Kalman – Bucy filtering algorithm theory and its numerous applications. We describe the technique of the accurate characterization of the economic and financial time series with application of state space methods with the Stratonovich – Kalman - Bucy filtering algorithm, focusing on the estimation of Gross Domestic Product by the Swiss National Bank. Applying the integrative thinking principles, we developed the software program and performed the computer modeling, using the Stratonovich – Kalman – Bucy filtering algorithm for the accurate characterization of the Australian GDP, German GDP and the USA GDP in the frames of the state-space model in Matlab. We also used the Hodrick-Prescott filter to estimate the corresponding output gaps in Australia, Germany and the USA. We found that the Australia, Germany on one side and the USA on other side have the different business cycles. We believe that the central banks can use our special software program with the aim to greatly improve the national macroeconomic indicators forecast by making the accurate characterization of the financial time-series with the application of the state-space models, based on the Stratonovich – Kalman – Bucy filtering algorithm.

This paper proposes a Near Explosive Random-Coefficient autoregressive model for asset pricing which accommodates both the fundamental asset value and the recurrent presence of autonomous deviations or bubbles. Such a process can be stationary with or without fat tails, unit-root nonstationary or exhibit temporary exponential growth. We develop the asymptotic theory to analyze ordinary least-squares (OLS) estimation. One important theoretical observation is that the estimator distribution in the random coefficient model is qualitatively different from its distribution in the equivalent fixed coefficient model. We conduct recursive and full-sample inference by inverting the asymptotic distribution of the OLS test statistic, a common procedure in the presence of localizing parameters. This methodology allows to detect the presence of bubbles and establish probability statements on their apparition and devolution. We apply our methods to the study of the dynamics of the Case-Shiller index of U.S. house prices. Focusing in particular on the change in the price level, we provide an early detection device for turning points of booms and bust of the housing market.

Many postulated relations in finance imply that expected asset returns strictly increase in an underlying characteristic. To examine the validity of such a claim, one needs to take the entire range of the characteristic into account, as is done in the recent proposal of Patton and Timmermann (2010). But their test is only a test for the direction of monotonicity, since it requires the relation to be monotonic from the outset: either weakly decreasing under the null or strictly increasing under the alternative. When the relation is non-monotonic or weakly increasing, the test can break down and falsely ‘establish’ a strictly increasing relation with high probability. We offer some alternative tests that do not share this problem. The behavior of the various tests is illustrated via Monte Carlo studies. We also present empirical applications to real data.

A critique that has been directed towards the log-GARCH model is that its log-volatility specification does not exist in the presence of zero returns. A common ``remedy" is to replace the zeros with a small (in the absolute sense) non-zero value. However, this renders Quasi Maximum Likelihood (QML) estimation asymptotically biased. Here, we propose a solution to the case where actual returns are equal to zero with probability zero, but zeros nevertheless are observed because of measurement error (due to missing values, discreteness approximisation error, etc.). The solution treats zeros as missing values and handles these by combining QML estimation via the ARMA representation with the Expectation-maximisation (EM) algorithm. Monte Carlo simulations confirm that the solution corrects the bias, and several empirical applications illustrate that the bias-correcting estimator can make a substantial difference.

Understanding the empirical relationship between the exchange rates, interest rates and stock prices are important and useful to the policy makers, professional investors and academics. Although the scholars and practitioners have studied the subject extensively, few empirical studies are available in the context of the Islamic banking stock prices. In this paper, we make an humble attempt to fill in this gap in the empirical literature of Islamic banking, in particular. We use panel cointegration and panel vector error-correction (VECM) model to examine the existence and direction of the causal relationship between exchange rate, interest rate and Islamic banking sector stock prices using monthly data over the last five years. The VECM is employed to discern the short-run and long-run Granger causality by applying the dynamic Generalized Method of Moments (dynamic GMM). For 40 Islamic banks, the empirical results tend to indicate that the Islamic bank stock prices have negative significant relationship with the exchange rates but no significant relationship with the interest rates. In addition, we found that there exists a bidirectional Granger-causal relationship between the Islamic bank stock prices and exchange rates. This finding tends to suggest that this significant relationship between the exchange rates and Islamic bank stock prices should be borne in mind by the policy makers while formulating their policies.

The paper is the first attempt to evaluate the role of gold as a hedge (negative or low correlation with equities in normal market conditions) and safe haven (negative or low correlation in times of market turbulence) by using the daily data for gold and Shariah-compliant equities ranging from January 1996 to April 2013, and comparing between developed and emerging markets in time-frequency domain. Wavelet Coherence technique is applied to identify the best time-frequency for gold as a hedge, and MGARCH-DCC to find out the reaction of gold to unfavorable market conditions as a safe haven. The results tend to indicate that gold maintains its capacity as hedging instrument at higher time-scales, while during the financial crisis it demonstrated a weak form of safe haven by showing almost zero correlation with the Shariah-compliant equities.