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This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. This yields the rotated BEKK (RBEKK) model. The extension to DCC-type parameterizations is given, introducing the rotated DCC (RDCC) model. Inference for these models is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on the DJIA stocks.

Global frailty is an unobserved macroeconomic variable. In event data contexts, this unobserved variable is assumed to impact the hazard rate of event arrivals. Attempts to identify and estimate the path of frailty are complicated when observed macroeconomic variables also impact hazard rates. It is possible that the impact of the observed macro variables and global frailty can be confused and identification can fail. In this paper I show that, under appropriate assumptions, the path of global frailty and the impact of observed macro variables can both be recovered. This approach differs from previous work in that I do not assume frailty follows a specific stochastic process form. Previous studies identify global frailty by assuming a stochastic form and using a filtering approach. However, chosen stochastic forms are arbitrary and can potentially lead to poor results. The method in this paper shows how to recover frailty without these assumptions. This can serve as a model check to filtering approaches. The methods are applied to simulations and an application to corporate default.

This paper assesses the extent of the transmission of financial shocks between South Africa and other members of the BRICS grouping in order to infer the degree of contagion during the period 1996–2012. The paper makes use of a multivariate VAR-DCC-GARCH model to this end. The paper finds evidence of cross-transmission and dependence between South Africa and Brazil. However, the empirical results show that South Africa is more affected by crises originating from China, India and Russia while these countries are least affected by crises originating from South Africa. The findings of this paper should be of interest to policy makers in the BRICS grouping should they be considering the possibility of full capital market liberalization and to the international investor who is looking at diversifying portfolios in the BRICS grouping.

We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Building on Bansal and Yaron (2004), our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The estimation is based on annual consumption data from 1929 to 1959, monthly consumption data after 1959, and monthly asset return data throughout. We maximize the span of the sample to recover the predictable component and use high-frequency data, whenever available, to efficiently identify the volatility processes. Our Bayesian estimation provides strong evidence for a small predictable component in consumption growth (even if asset return data are omitted from the estimation). Three independent volatility processes capture different frequency dynamics; our measurement error specification implies that consumption is measured much more precisely at an annual than monthly frequency; and the estimated model is able to capture key asset-pricing facts of the data.

The equity premium — the expected return on the aggregate stock market less the government bill rate – is of central importance to the portfolio allocation of individuals, to the investment decisions of firms, and to model calibration and testing. This quantity is usually estimated from the sample average excess return. We propose an alternative estimator, based on maximum likelihood, that takes into account information contained in dividends and prices. Applied to the postwar sample, our method leads to an economically significant reduction from 6.4% to 5.1%. Simulation results show that our method produces more reliable estimates under a wide range of specifications.

We consider inference and testing in extended constant conditional correlation GARCH models in the case where the true parameter vector is a boundary point of the parameter space. This is of particular importance when testing for volatility spillovers in the model. The large-sample properties of the QMLE are derived together with the limiting distributions of the related LR, Wald, and LM statistics. Due to the boundary problem, these large-sample properties become nonstandard. The size and power properties of the tests are investigated in a simulation study. As an empirical illustration we test for (no) volatility spillovers between foreign exchange rates.

This paper features an analysis of volatility spillover effects from Australia's major trading partners, namely, China, Japan, Korea and the United States, for a period running from 1st January 2004 to 30th June 2014. This captures the impact of the Global Financial Crisis (GFC). These markets are represented by the following major indices: The Shanghai composite and the Hangseng. (in the case of China, as both China and Hong Kong appear in Australian trade statistics), the S&P500 index, the Nikkei225 and the Kospi index. We apply the Diebold and Yilmaz (2009) Spillover Index, constructed in a VAR framework, to assess spillovers across these markets in returns and in volatilities. The analysis confirms that the US and Hong Kong markets have the greatest influence on the Australian one. We then move to a GARCH framework to apply further analysis and apply a tri-variate Cholesky-GARCH model to explore the effects from the US and Chinese market, as represented by the Hang Seng Index. We further explore three sample sub-periods, Pre-GFC 2004/1/1/ until 2007/07/08/, GFC 2007/08/09 until 2010/05/07 and Post-GFC 2010/05/10 until 2014/06/20 and analyse the behaviour of time-varying conditional correlations in these sub-periods. The GARCH analysis re-affirms the strong influence of the Hang Seng Index and the S&P500 Index. Refereed/Peer-reviewed

A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to combine efficient parameter estimation, variable selection, and valid inference for time series processes. In an application to several constituents of the S&P 500 index it is shown that (i) the optimal significant lag structure is time-varying and subject to drastic regime shifts that seem to happen across assets simultaneously; (ii) in many cases the relevant information for prediction is included in the first 22 lags, corroborating previous results concerning the accuracy and the difficulty of outperforming out-of-sample the heterogeneous autoregressive (HAR) model; and (iii) some common features of the optimal lag structure can be identified across assets belonging to the same market segment or showing a similar beta with respect to the market index.

The greater financial integration particularly over the past decade has led to more synchronized movements of financial markets across the globe. As the domestic debt capital market, particularly the Malaysian Government Securities (MGS) and Government Investment Issue (GII or Government Sukuk), shariah compliant Malaysian sovereign papers deepen, the movements of the domestic yield curve are expected to be increasingly influenced by movements in foreign bond yields as both domestic and foreign investors respond to global developments and sentiments. Based on standard time series techniques, our findings tend to indicate that GII as well as MGS are weakly endogenous subject to changes in US Treasury which are most probably transmitted through changing their investment preference and expectation of liquidity and risk premium that they are willing to pay by holding local currency bonds and sukuk. This may create shifting in yield curve and significant capital outflows or inflows that may destabilize financial condition that requires policy changes. Finally, the findings also reaffirmed that the sukuk is still priced based on the conventional way of pricing bonds.

This paper deals with three aspects of spectacular oil price episodes such as the one witnessed in 2008. First, the concept of temporary explosiveness is proposed as an empirical method for capturing this type of behavior. The application of a recently proposed recursive unit root test shows that phases of explosive behavior occurred in 1990/1991, 2005/2006, and 2007/2008. Second, the underlying causes of the observed behavior are discussed. The prevailing opinion in the literature is that fundamental factors are the main explanation for the 2007/2008 oil price hike, but that in 1990/1991, speculative demand shocks also played a role. Third, it is shown that temporary oil price hikes influence economic decisions that are based on oil price information. For this purpose, a real options model on the oil field development decision is reconsidered. The mechanism behind this is an increase in the profitability of the oil field development project. In sum, the key contributions of this paper are to highlight a new empirical feature in oil prices and to show that economic effects of speculative demand shocks can emerge that to date appear to have been overlooked.

This paper proposes a new way to measure and deal with risk within the portfolio selection problem using a skewness/semivariance biobjective optimization framework. The solutions of this biobjective optimization problem allow the investor to analyse the efficient trade-off between skewness and semivariance. Due to the endogeneity of the cosemivariance matrix, the biobjective problem is solved using a derivative-free algorithm based on direct multisearch. For four datasets, collected from the Fama/French benchmark collection, the direct multisearch was able to determine the in-sample Pareto frontier. The out-of-sample performance of the skewness/semivariance model was assessed by choosing three portfolios (the portfolio that maximizes a skewness per semivariance ratio, the portfolio that maximizes the Sharpe ratio and the portfolio that maximizes the Sortino ratio) at each in-sample Pareto frontier and measuring their performance in terms of skewness per semivariance ratio, Sharpe ratio, Sortino ratio and turnover. The results show that the efficient skewness/semivariance portfolios are consistently competitive, and often superior, comparatively to the benchmark portfolios considered. Both in-sample and the out-of-sample performance analysis were conducted using three different benchmark returns for the semivariance computations.

The time-series dynamics of short-term interest rates are important as they are a key input into pricing models of the term structure of interest rates. In this paper we extend popular discrete time short-rate models to include Markov switching of infinite dimension. This is a Bayesian nonparametric model that allows for changes in the unknown conditional distribution over time. Applied to weekly U.S. data we find significant parameter change over time and strong evidence of non-Gaussian conditional distributions. Our new model with a hierarchical prior provides significant improvements in density forecasts as well as point forecasts. We find evidence of recurring regimes as well as structural breaks in the empirical application.

We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995{2014. and compare these to the tail indexes produced by simulating GARCH models. Our results suggest that actual and simulated values differ greatly for GARCH models with normal conditional distributions, which underestimate the tail risk. By contrast, the GARCH models with Student's t conditional distributions capture the tail shape more accurately, with GARCH and GJR-GARCH being the top performers.

The aim of this paper is to investigate the return and volatility linkages among the Moroccan stock market and that of the USA and three European countries (France, Germany and UK) before and during the financial crisis. More specifically, we use stock returns in MASI, CAC, DAX, FTSE and NASDAQ as representatives of Moroccan, French, German, British and US markets, respectively. The data sample frequency is daily and spans from January 2002 to December 2012 excluding holidays. Using the estimation results of a bivariate VAR-BEKK GARCH model, we analyze the return and volatility spillover effects between the Moroccan market and the other considered markets. Moreover, the identification of break point due to the subprime crisis is made by Lee and Strazicich (Rev Econ Stat 85(4):1082–1089, 2003, Econ Bull 33(4):2483–2492, 2013), Papell and Prodan (J Money Credit Bank 38:1329–1349, 2006) and Prodan (J Bus Econ Stat 26(1):50–65, 2008) structural break tests. The empirical results indicate varying degrees of interdependence and spillover effects between the four considered major stock markets and the Moroccan emerging stock market before and after the global financial crisis.

This paper provides an assessment of the comparative effectiveness of four econometric methods in estimating the optimal hedge ratio in an emerging equity market, particularly the South African equity and futures markets. The paper bases the effectiveness of hedging on volatility reduction and minimisation of the coefficient of variation of hedged returns as well as risk-aversion based utility maximisation. The empirical analysis shows that the single equation method estimated by ordinary least squares is the most effective over daily hedging periods. However, the vector error-correction method and multivariate GARCH methods are most effective over weekly and monthly hedging periods.

International audience; The Paris Agreement establishes a mechanism which allows a Party to benefit from greenhouse gas emissions reductions conducted in a host Party to fulfil its nationally determined contribution. In this context, the objective of this paper is to improve the understanding of carbon offset price dynamics in comparison with regular carbon market allowances. We combine a cointegration approach with risk premium considerations to compare the price dynamics of European Union Allowances (EUA) and Certified Emission Reductions (CER) in the second phase of the European carbon market. By taking account of breaks identified in the series, we find that, while the EUA and CER returns present comparable dynamics mainly driven by fuel switching, the long-term relationships between the price of these two types of permits and their drivers differ significantly. Whereas the price of EUA is well explained by a demand effect, the impact of energy prices on the CER price suggests the existence of a supply-side effect for credits. We find that the price elasticity of allowances with regard to the coal and gas prices is negative in time periods of low economic activity and positive during the remaining time. We explain the former with the fact that the market is not tight and the latter with the effect of the economic activity on the price of commodities and energy.

We study the impact of oil price shocks on U.S. stock market volatility. We derive three different structural oil shock variables (i.e. aggregate demand, oil-supply, and oil-demand shocks) and relate them to stock market volatility, using bivariate structural VAR models, one for each oil price shock. Identification is achieved by assuming that the price of crude oil reacts to stock market volatility only with delay. This implies that innovations to the price of crude oil are not strictly exogenous, but predetermined with respect to the stock market. We show that volatility responds significantly to oil price shocks caused by sudden changes in aggregate and oil-specific demand, while the impact of supply-side shocks is negligible.

Based on unique data we show that macro variables, the default rate and loss given default of bank loans share common cyclical components. The innovation in our model is the distinction between loans with either severe or mild losses. The variation in the proportion of these two types drives the cyclic behavior of the loss given default and constitutes the links with the default rate and macro variables. These links vary according to loan and borrower characteristics. During downturns, the proportion of defaults with severe losses increases, but the distribution of losses conditional on their being mild or severe does not change. although loans are monitored more closely than bonds and are more senior, the cyclical variation in their losses resembles those for bonds, albeit around a lower average level. This variation leads to an increase in the capital reserves required for loan portfolios.

This article reviews recent econometric developments in the literature on price discovery and predictability. For both areas, we discuss traditional approaches to econometric modeling, limitations to these approaches, and recent developments designed to overcome them. We also discuss the state of the art and suggest future research. Three main conclusions are drawn. First, while many recent empirical applications in price discovery and price predictability are on the frontier of econometric methods, further developments are needed to increase relaxation of relevant assumptions and push the boundaries of applications. Second, future research in econometric modeling needs to combine/synthesize recent developments across multiple econometric issues, rather than proceeding in a piecemeal manner, for instance, by integrating developments in the time series literature into panel-based frameworks. Third, recent econometric literature is generating findings that challenge long-held beliefs about apparent empirical regularities in price discovery and price predictability, thus presenting opportunities to develop relevant theory.

Financial risk management is difficult at the best of times, but especially so in the presence of economic uncertainty and financial crises. The purpose of this special issue on “Advances in Financial Risk Management and Economic Policy Uncertainty” is to highlight some areas of research in which novel econometric, financial econometric and empirical finance methods have contributed significantly to the analysis of financial risk management when there is economic uncertainty, especiallythe power of print: uncertainty shocks, markets, and the economy, determinants of the banking spread in the Brazilian economy: the role of micro and macroeconomic factors, forecasting value-at-risk using block structure multivariate stochastic volatility models, the time-varying causality between spot and futures crude oil prices: a regime switching approach, a regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates, a practical approach to constructing price-based funding liquidity factors, realized range volatility forecasting: dynamic features and predictive variables, modelling a latent daily tourism financial conditions index, bank ownership, financial segments and the measurement of systemic risk: an application of CoVaR, model-free volatility indexes in the financial literature: a review, robust hedging performance and volatility risk in option markets: application to Standard and Poor’s 500 and Taiwan index options, price cointegration between sovereign CDS and currency option markets in the global financial crisis, whether zombie lending should always be prevented, preferences of risk-averse and risk-seeking investors for oil spot and futures before, during and after the global financial crisis, managing financial risk in Chinese stock markets: option pricing and modeling under a multivariate threshold autoregression, managing systemic risk in The Netherlands, mean-variance portfolio methods for energy policy risk management, on robust properties of the SIML estimation of volatility under micro-market noise and random sampling, asymmetric large-scale (I)GARCH with hetero-tails, the economic fundamentals and economic policy uncertainty of Mainland China and their impacts on Taiwan and Hong Kong, prediction and simulation using simple models characterized by nonstationarity and seasonality, and volatility forecast of stock indexes by model averaging using high frequency data.

We propose regression-based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. We allow for state variables that are cross-sectional pricing factors, forecasting variables for the price of risk, and factors that are both. The estimators explicitly allow for time-varying prices of risk, time-varying betas, and serially dependent pricing factors. Our approach nests the Fama-MacBeth two-pass estimator as a special case. We provide asymptotic multistage standard errors necessary to conduct inference for asset pricing tests. We illustrate our new estimators in an application to the joint pricing of stocks and bonds. The application features strongly time-varying, highly significant prices of risk that are found to be quantitatively more important than time-varying betas in reducing pricing errors.

In our paper, we examined the relationship between non-performing loans, as a measure of stability, and concentration, as a measure of competition, in the Libyan banking sector. We used aggregate quarterly data for the 15 commercial banks in the country during the period 2002-2013. A broad set of tests were conducted to measure the relationship between the two variables, and alternative robustness tests were conducted to verify our core finding that less competition in the banking sector leads to a more resilient banking sector. Thus, our results offer empirical support against the “competition–stability” theory and conform the “competition–fragility” literature. We conclude by recommending the need to inspect in more detail (on a bank by bank level) the relationship between competition and fragility in developing countries in general and in Libya in particular.

The empirically most relevant stylized facts when it comes to modeling time varying financial volatility are the asymmetric response to return shocks and the long memory property. Up till now, these have largely been modeled in isolation though. To more flexibly capture asymmetry also with respect to the memory structure we introduce a new model and apply it to stock market index data. We find that, although the effect on volatility of negative return shocks is higher than for positive ones, the latter are more persistent and relatively quickly dominate negative ones.

One of the fastest growing areas in empirical finance, and also one of the least rigorously analyzed, especially from a financial econometrics perspective, is the econometric analysis of financial derivatives, which are typically complicated and difficult to analyze. The purpose of this special issue of the journal on “Econometric Analysis of Financial Derivatives” is to highlight several areas of research by leading academics in which novel econometric, financial econometric, mathematical finance and empirical finance methods have contributed significantly to the econometric analysis of financial derivatives, including market-based estimation of stochastic volatility models, the fine structure of equity-index option dynamics, leverage and feedback effects in multifactor Wishart stochastic volatility for option pricing, option pricing with non-Gaussian scaling and infinite-state switching volatility, stock return and cash flow predictability: the role of volatility risk, the long and the short of the risk-return trade-off, What’s beneath the surface? option pricing with multifrequency latent states, bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets, a stochastic dominance approach to financial risk management strategies, empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction, non-linear dynamic model of the variance risk premium, pricing with finite dimensional dependence, quanto option pricing in the presence of fat tails and asymmetric dependence, smile from the past: a general option pricing framework with multiple volatility and leverage components, COMFORT: A common market factor non-Gaussian returns model, divided governments and futures prices, and model-based pricing for financial derivatives keywords: Hedge Fund Diversi_cation, Spillover Index, Markowitz Analaysis, Downside Risk, CVaR, Draw-Down.

We investigate several promising algorithms, proposed in literature, devised to detect sudden changes (structural breaks) in the volatility of financial time series. Comparative study of three techniques: ICSS, NPCPM and Cheng’s algorithm is carried out via numerical simulation in the case of simulated T-GARCH models and two real series, namely German and US stock indices. Simulations show that the NPCPM algorithm is superior to ICSS because is not over-sensitive either to heavy tails of market returns or to their serial dependence. Some signals generated by ICSS are falsely classified as structural breaks in volatility, while Cheng’s technique works well only when a single break occurs.

Using estimators of the variation of positive and negative returns (realized semivariances) and high-frequency data for the S&P 500 Index and 105 individual stocks, this paper sheds new light on the predictability of equity price volatility.We showthat future volatility is more strongly related to the volatility of past negative returns than to that of positive returns and that the impact of a price jump on volatility depends on the sign of the jump, with negative (positive) jumps leading to higher (lower) future volatility. We show that models exploiting these findings lead to significantly better out-of-sample forecast performance.

In this paper, we investigate the dynamic response of stock market volatility to changes in monetary policy. Using a vector autoregressive model, our findings reveal a significant and asymmetric response of stock returns and volatility to monetary policy shocks. Although the increase in the volatility risk premium, futures-trading volume, and leverage appear to contribute to a short-term increase in volatility, the longer-term dynamics of volatility are dominated by monetary policy's effect on fundamentals. The estimation results from a bivariate VAR-GARCH model suggest that the Fed does not respond to the stock market at a high frequency, but they also suggest that market participants' uncertainty regarding the monetary stance affects stock market volatility.

A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties of the proposed estimators.

International audience; This paper proposes an indirect inference (Gourieroux, Monfort and Renault, 1993; Smith, 1993) estimation method for a large class of dynamic equilibrium models. Our approach is based on the observation that the econometric structure of these systems naturally generates auxiliary equilibria that can serve as building blocks for estimation. We use this insight to develop an accurate estimator for the long-run risk model of Bansal and Yaron (2004). We demonstrate the accuracy of our method by Monte Carlo simulation and estimate the long-run risk model on U.S. data. We also illustrate the good performance of the methodology on an asset pricing model with investor learning.

In this study we examined the effect of structural break points in conditional volatility on variance persistency of asymmetric GARCH models. We used Bai and Perron methodology to detect structural break points in conditional variance of daily stock returns of 7 emerging markets (4-European and 3-Asian) from 1997 to 2014. We implied Exponential GARCH or EGARCH and Threshold GARCH or T-GARCH models with and without sudden structural breaks and tried to evaluate persistency in variance and leverage effect while estimating conditional volatility. We concluded that persistence in variance reduces while considering regime shifts in conditional volatility of these models. The half-lives of shock to volatility significantly decline when we consider these sudden break points. Moreover by comparing these two models we concluded that T-GARCH model reduces persistency more gladly than EGARCH model when we account these sudden changes.

International audience; The paper applies an event study methodologyaims to investigate the macroeconomic announcements effects on Standard&Poor’s500 and oil prices. Our results provide evidence for a significant impact of the US macroeconomic news on oil prices. This impact is split into two components, namely the direct effect (common response) and indirect effect (volatility transmission). Altogether our results show that the volatility transmission is bidirectional. Not only a significant volatility transmission from the oil market to the US stock market is revealed, but also a high volatility transmission is recorded from the oil market to the stock market especially after the release of consumption indicators.

Unit-linked contracts are wrapped with some death, maturity and accumulation guarantees such as the guaranteed minimum maturity benefit, the guaranteed minimum death benefit, the guaranteed minimum accumulation benefit, the guaranteed minimum income benefit, and/or the guaranteed minimum surrender benefit. According to Romanian legislation which regulates the unit-linked life insurance market, unit-linked life insurance contracts pass most of the investment risk to the policyholder and involve no investment risk for the insurer. Although the Romanian legislation authorizes the Romanian insurers to offer unit-linked contracts without investment guarantees, this research provides a proposal of a theoretical and empirical basis for modelling liabilities for unit-linked insurance contracts with incorporated investment guarantees. The aim of this study is to offer an optimal theoretical approach for simulating liabilities for unit-linked life insurance contracts with incorporated death benefit and maturity benefit.

We suggest a joint analysis of ex-post intra-day variability in an option and its associated underlying asset market as a novel means of validating an option pricing model. For this purpose, we introduce the notion of option realized variance, by which we mean the cumulative variance realized by the sample path of successive option price observations. In concurrently observing the realized path of the underlying asset, we contrast option realized variance with the realized variance that would be implied from the underlying asset price path under certain model assumptions. In the empirical analysis, we focus on the implied volatility compensated Black-Scholes model and the Heston model. We find that neither model reconciles second-order moments in the option and the underlying asset market. The differences point to the existence of additional relevant pricing factors that affect option second-order moments. We thus corroborate findings made in option data of lower frequency.

Technical analysis aims on visually identifying geometrical patterns and indicators in price charts in order to anticipate price “trends”. This paper investigates the relations between return and technical analysis indicators of Turkish commercial banking sector in BIST-BANKING index for the period 1-1-2000- 31-12-2013. Technical analysis indicators are generated by taking into account the daily stock prices. The general steps go through the identification of the relations for the securities returns and technical analysis indicators. We illustrate the proposed with a panel data methodology including several securities of banking sector. Our investigation shows that there is a statistically significant relation between technical analysis indicators and stock returns as panel.

This paper aims at providing empirical support to claims made by officials in oil-producing countries that investors in the New York Stock Exchange (NYSE) market are involved in the disruption of oil production in some Organization of the Petroleum Exporting Countries countries. The claims state that some investors in the NYSE are financing militias in those countries to close down oilfields and ports, and buy oil before this incident occurs. By doing so, they have access to information that no one else in the market has, and make profits from this information. Using a vector autoregression (VAR) model approach to detect this phenomenon, and being inspired by the asymmetric information theory, we fail to support those claims. We tried to put this theory under investigation by running test on three oil-disruption incidents that occurred in 2013, and all of the results turned out to be insignificant. Nevertheless, this approach was able to detect a period which might involve asymmetric information in the NYSE. In addition, using a VAR model enabled us to measure the duration and magnitude of the effect of a shock in volumes of trade on oil prices in that market.

We investigate the linkages between international financial markets and Poland, including stocks, bonds and foreign exchange. We work in a static copula framework, allow for asymmetry of tail behavior and use tail dependence as a measure of contagion. Even though we find the overall dependence to be strong, Polish assets are to a certain extent immune to contagion from global and emerging markets. Equities are prone to only mild contagion, foreign exchange and long-term bonds are even less affected, and short-term bonds appear insulated.

In this article we study the relationship between microeconomic factors (financial ratios) and SMEs’ insolvencies in Romania using logistic regression model to predict the likelihood of SMEs bankruptcy in the aftermath of the financial and economic crisis. We conducted a logit econometric model for each size category of SMEs (micro, small and medium). The main finding is that the return on assets (ROA) and total debt ratio (TDR) across the three size categories of SMEs are the statistically significant key indicators for the SMEs insolvencies. The best logistic model is the one for small firms because the number of fair estimations represents 80.6%.

The purpose of this study is to investigate the impacts of crude oil price variations on the Turkish stock market returns. We have employed vector autoregression (VAR) model using daily observations of Brent crude oil prices and Istanbul Stock Exchange National Index (ISE-100) returns for the period between January 2, 1990 and November 1, 2011. We have also tested the relationship between oil prices and stock market returns under global liquidity conditions by incorporating a liquidity proxy variable, Chicago Board of Exchange’s (CBOE) S&P 500 market volatility index (VIX), into the model. Variance decomposition test results suggest little empirical evidence that crude oil price shocks have been rationally evaluated in the Turkish stock market. Rather, it was global liquidity conditions that were found to account for the greatest amount of variation in stock market returns.

En este trabajo se generan estrategias especulativas en volatilidad con opciones europeas sobre veintiún componentes del Índice de Precios y Cotizaciones (IPC) y sobre este mismo índice, bajo el supuesto de que la volatilidad del activo subyacente es conducida por un proceso GARCH-M (1,1) calibrado con datos históricos, el precio de la opción se obtiene por simulación Monte Carlo. Con las estrategias de volatilidad construidas con los precios de las opciones simuladas se determinó que la estrategia cono corto es adecuada al realizar operaciones en el mercado mexicano (IPC) para plazos de 45 y 60 días, pero no para aquellas a 90 días. Asimismo se observó una relación directa entre el plazo y la estrategia cono corto en cuanto a ganancias al emplear ésta en el componente KIMBERA en el corto plazo, es decir, al aumentar el plazo, se obtuvo una mayor ganancia generada por dicha estrategia / In this paper speculative volatility strategies are generated with European options on twenty one components of the Mexican Stock Exchange (IPC) and on this same index, under the assumption that the volatility of the underlying asset is driven by a GARCH-M (1,1) process calibrated with historical data, the option price is obtained by Monte Carlo simulation. With volatility strategies built with simulated option prices, it was determined that the short cone strategy is adequate to trade in the Mexican market (IPC) for periods of 45 to 60 days, but not for 90 days periods. Moreover, a direct relationship between the term and the short cone strategy in terms of profits was observed when using this strategy in the KIMBERA component in the short term, i.e. by increasing the length a higher profit generated by such a strategy was obtained

News might trigger jump arrivals in financial time series. The "bad" news and "good" news seem to have distinct impact. In the research, a double exponential jump distribution is applied to model downward and upward jumps. Bayesian double exponential jump-diffusion model is proposed. Theorems stated in the paper enable estimation of the model’s parameters, detection of jumps and analysis of jump frequency. The methodology, founded upon the idea of latent variables, is illustrated with simulated data.

The purpose of this study is to test the weak form efficiency within framework of the random walk model by using price movements of BIST-100 Index and to evaluate forecasting performance of investors in Turkish stock market. Whether investors can earn excess returns or not has been decided with determination of whether the stock price movements and expectations of investors follow a random walk or not at the period of 1990:01-2014:06 in Turkey. Firstly for this purpose, the randomness of price movements of BIST-100 Index has been tested by using Ljung-Box and LM analysis. The second assumption of random walk model, randomness of expectations of investor and each of information entering market has been analysed with BDS test. Empirical evidences show that investors in Turkish capital market can forecast by using historical stock price movements, investors can have all information which is likely to affect the stock price in advance, and investors can earn excess returns by taking decisions based on this information.

This paper empirically examines the relationship between trading volume and conditional volatility of returns in the Tunisian stock market within the framework of the mixture of distribution hypothesis (MDH) and the sequential information arrival hypothesis (SIAH). Through this study, we especially aim to test the volatility persistence degree without volume, with contemporaneous volume, and with lagged volume. Our empirical analysis is based on daily data related to the 43 most active and dynamic securities traded from January 2, 2008 to June 29, 2012. Our daily analysis reveals several results. Firstly, we confirm the strong positive relationship between trading volume and returns conditional volatility issued from generalized autoregressive conditional heteroskedasticity (GARCH (1,1)) model. Secondly, according to the theoretical predictions of the MDH, we show that including contemporaneous trading volume in the conditional variance equation significantly reduces volatility persistence. Thirdly, through the addition of the lagged volume in the conditional variance equation, we show that volatility persistence remains in the whole at a high level and close to that obtained from the GARCH (1,1) model without trading volume, and also at a higher level than that resulting from the addition of the contemporaneous volume. Our results thus do not support the implications of the SIAH.

Unit-linked insurance is a life insurance policy with investment component. An important component of the activity carried out by the insurance companies is the investment of the premiums paid by policyholders in various types of assets, in order to obtain higher yields than those guaranteed by the insurance contracts, while providing the necessary liquidity for the payment of insurance claims in case of occurrence of the assumed risks. This research contributes to the existing literature regarding the study of investment alternatives, with an exclusive focus on the investment in unit-linked life insurance. A special place in this study is the presentation of investments in unit-linked insurance versus other types of financial investments: deposits, treasury bills, shares (BET), currency (EURO) and gold.

International audience; We analyze the persistence effects in the empirical relationship between announcement releases and return volatilities of four major companies of the French stock market using high frequency data over the period 1995–1999. Besides its institutional stability, this sample period avoids the econometric difficulties inherent to simultaneous news arrivals. Our approach contributes to the relevant literature in that we focus on individual stock volatilities rather than indices, we distinguish firm-specific and macroeconomic announcements, and we endogenize both the durations of announcement effects and the response patterns of equity prices. We find that our individual volatilities are affected by a systematic market effect, calendar effects, announcements related to the firms’ macroeconomic environment and announcements related to the firms’ and their competitors’ strategic dealings and commercial outcomes. We find evidence that all volatility responses are gradual with persistence horizons ranging from one to three hours, revealing a significant degree of inefficiency of the French stock market over the period. This inefficiency can be viewed as a breeding ground for the implementation of more performant informational and trading systems that allowed markets to move towards more efficiency.

We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced. We prove consistency and a point-wise stable central limit theorem for the proposed spot covariance estimator in a very general setup with stochastic volatility, leverage effects and general noise distributions. Moreover, we extend the LMM estimator to be robust against autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. Based on simulations we provide empirical guidance on the effective implementation of the estimator and apply it to high-frequency data of a cross-section of Nasdaq blue chip stocks. Employing the estimator to estimate spot covariances, correlations and volatilities in normal but also unusual periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, and (iii) can increase strongly and nearly instantaneously if new information arrives.

In this paper we extend the study of the cardinality impact from the standard mean-variance scenario to higher moments, considering a utility maximization framework. For each scenario, we propose a bi-objective model that allows the investor to directly analyse the efficient trade-off between expected utility and cardinality. We study not only the effect of cardinality in each scenario but also the real gain of considering higher moments in portfolio management. This analysis is performed assuming that the investor has constant relative risk aversion (CRRA) preferences. For the data collected on the PSI20 index, the empirical results showed that there are no performance gains, in-sample, from the efficient mean-variance expected utility/cardinality portfolios to the efficient expected utility/cardinality portfolios when higher moments are considered. However, the out-of-sample performance of the efficient mean-variance-skewness expected utility/cardinality portfolios and of the efficient mean-variance-skewness-kurtosis expected utility/cardinality portfolios suggest the existence of real gains, especially when transaction costs are considered.

In this paper, we investigate the causal effects of public and private debts on U.S. output dynamics. We estimate a battery of Cointegrated Structural Vector Autoregressive models, and we identify structural shocks by employing Independent Component Analysis, a data-driven technique which avoids ad-hoc identification choices. The econometric results suggest that the impact of debt on economic activity is Janus-faced. Public debt shocks have positive and persistent influence on economic activity. In contrast, rising private debt has a milder positive impact on GDP, but it fades out over time. The analysis of the possible transmission mechanisms reveals that public debt crowds in private consumption and investment. In contrast, mortgage debt fuels consumption and output in the short-run, but shrinks them in the medium-run.

The capital structure theory advocates a mix between debt and equity to optimize a firm‘s value (Modigliani & Miller, 1958). However, Islamic finance recognizes more conservative levels of debt for firms, based not on capital structure, but on debt to total assets or debt to market capitalization. This study is the first attempt to investigate the role of leverage in affecting the returns and the firm‘s share price volatility in relation to an Islamic finance perspective (IFP). The paper is based on a sample of 320 European companies distributed into different portfolios with high and low debt, high and low assets and focused on ten different countries. Comparative portfolio analysis was used to obtain a number of testable hypotheses, which specify the factors at the level of the firm and at the level of the market, in order to determine optimal financial risk. Specifically, we use the mean variance efficient frontier (MVEF) to confirm, in accordance with the theory, that a portfolio with a higher capital structure has higher volatility and lower returns compared to a portfolio with a lower capital structure. Then, the Shari‘ah screening method of improving the firm‘s stability in the market has been analyzed based on econometric analysis. Dynamic GMM is used in order to correlate the firm‘s leverage to total assets with its return and volatility in portfolios with high and low capital structure. To ensure the robustness of results, the business cycle effects have been considered after adding the firm and the country characteristics with a view to taking into account the heterogeneity across different firms and different markets. The preliminary results tend to indicate that there are significant correlations between capital structure and both returns and volatility, but not necessarily with high debt to assets given different sizes and growth of firms. The paper suggests that are three main factors which need to be considered by the firms in order to improve their stability in the market: firstly, the level of capital structure but not the debt to total assets as suggested by some scholars using the IFP; secondly, the capitalization or the firm size and finally, the level of the sovereign debt and country dynamics. Although the latter may be beyond the firm‘s control, it is up to the firm to consider its own market with implications on its leverage policy in relation to the frequency-dependent strategy.

Markowitz (1952) portfolio selection requires estimates of (i) the vector of expected returns and (ii) the covariance matrix of returns. Many successful proposals to address the first estimation problem exist by now. This paper addresses the second estimation problem. We promote a nonlinear shrinkage estimator of the covariance matrix that is more flexible than previous linear shrinkage estimators and has 'just the right number' of free parameters to estimate (that is, the Goldilocks principle). It turns out that this number is the same as the number of assets in the investment universe. Under certain high-level assumptions, we show that our nonlinear shrinkage estimator is asymptotically optimal for portfolio selection in the setting where the number of assets is of the same magnitude as the sample size. For example, this is the relevant setting for mutual fund managers who invest in a large universe of stocks. In addition to theoretical analysis, we study the real-life performance of our new estimator using backtest exercises on historical stock return data. We find that it performs better than previous proposals for portfolio selection from the literature and, in particular, that it dominates linear shrinkage.

We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete stock price changes. The likelihood function for our model is analytically intractable and requires Monte Carlo integration methods for its numerical evaluation. The proposed methodology is applied to tick-by-tick data of four stocks traded on the New York Stock Exchange. We require fast simulation methods for likelihood evaluation since the number of observations per series per day varies from 1000 to 10,000. Complexities in the intraday dynamics of volatility and in the frequency of trades without price impact require further non-trivial adjustments to the dynamic Skellam model. In-sample residual diagnostics and goodness-of-fit statistics show that the final model provides a good fit to the data. An extensive forecasting study of intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling approaches.