142 research outputs found
Portfolio Selection under Systemic Risk
No full textThis paper proposes a modified Sharpe ratio to construct optimal portfolios under systemic events. The portfolio allocation problem is solved analytically under the absence of short-selling restrictions and numerically when short-selling restrictions are imposed. This approach is made operational by embedding it in a multivariate dynamic setting using dynamic conditional correlation and copula models. We evaluate the out-of-sample performance of our portfolio empirically over the period 2007 to 2020 using ex post final wealth paths and systemic risk metrics against mean–variance, equally weighted, and global minimum variance portfolios. Our portfolio outperforms all competitors under market distress and remains competitive in noncrisis periods
Measuring causality between volatility and returns with high-frequency data
No full textWe use high-frequency data to study the dynamic relationship between volatility and equity
returns. We provide evidence on two alternative mechanisms of interaction between returns and
volatilities: the leverage effect and the volatility feedback effect. The leverage hypothesis asserts
that return shocks lead to changes in conditional volatility, while the volatility feedback effect
theory assumes that return shocks can be caused by changes in conditional volatility through a
time-varying risk premium. On observing that a central difference between these alternative
explanations lies in the direction of causality, we consider vector autoregressive models of
returns and realized volatility and we measure these effects along with the time lags involved
through short-run and long-run causality measures proposed in Dufour and Taamouti (2008), as
opposed to simple correlations. We analyze 5-minute observations on S&P 500 Index futures
contracts, the associated realized volatilities (before and after filtering jumps through the
bispectrum) and implied volatilities. Using only returns and realized volatility, we find a weak
dynamic leverage effect for the first four hours at the hourly frequency and a strong dynamic
leverage effect for the first three days at the daily frequency. The volatility feedback effect
appears to be negligible at all horizons. By contrast, when implied volatility is considered, a
volatility feedback becomes apparent, whereas the leverage effect is almost the same. We
interpret these results as evidence that implied volatility contains important information on
future volatility, through its nonlinear relation with option prices which are themselves forwardlooking.
In addition, we study the dynamic impact of news on returns and volatility, again
through causality measures. First, to detect possible dynamic asymmetry, we separate good
from bad return news and find a much stronger impact of bad return news (as opposed to good
return news) on volatility. Second, we introduce a concept of news based on the difference
between implied and realized volatilities (the variance risk premium) and we find that a positive
variance risk premium (an anticipated increase in variance) has more impact on returns than a
negative variance risk premium
Specification and casualty of distribution models
No full textMany important economic and finance hypotheses are investigated through testing
the specification of restrictions on the conditional distribution of a time series, such
as conditional goodness-of- t (Box and Pierce (1970)), conditional quantiles (Koenker
and Machado (1999)), and distributional Granger non-causality (Taamouti, Bouezmarni,
and El Ghouch, 2014). This PhD Thesis contributes to the study of specification and
causality tests that provide a more flexible and detailed approach to evaluate economic
relationships, which are useful in many relevant empirical applications.
In the first chapter, we propose a practical and consistent specification test of conditional
distribution models for dependent data in a very general setting. Our approach
covers conditional distribution models possibly indexed by function-valued parameters,
which allows for a wide range of important empirical applications, such as the linear
quantile auto-regressive, the CAViaR, and the distributional regression models. Our test
statistic is based on a comparison between the estimated parametric and the empirical
distribution functions. The new specification test (i) is valid for general linear and
nonlinear dynamic models under parameter estimation error, (ii) allows for dynamic misspecification, (iii) is consistent against fixed alternatives, and (iv) has nontrivial power
against √T -local alternatives, with T the sample size. As the test statistic is non-pivotal,
we propose and theoretically justify a block bootstrap approach to obtain valid inference.
Monte Carlo simulations illustrate that the proposed test has good finite sample
properties for different data generating processes and sample sizes. Finally, an empirical
application to models of Value-at-Risk (VaR) highlights the benefits of our approach.
The second chapter proposes a consistent parametric test of Granger-causality in
quantiles. Although the concept of Granger-causality is defined in terms of the conditional
distribution, the majority of papers have tested Granger-causality using conditional mean
regression models in which the causal relations are linear. Rather than focusing on a
single part of the conditional distribution, we develop a test that evaluates nonlinear
causalities and possible causal relations in all conditional quantiles. The proposed test
statistic has correct asymptotic size, is consistent against fixed alternatives and has power
against Pitman deviations from the null hypothesis. The proposed approach allows us
to evaluate nonlinear causalities, causal relations in conditional quantiles, and provides a suficient condition for Granger-causality when all quantiles are considered. As the
proposed test statistic is asymptotically non-pivotal, we tabulate critical values via a
subsampling approach. We present Monte Carlo evidence and an application considering
the causal relation between the gold price, the USD/GBP exchange rate, and the oil
price.
The last chapter of the thesis studies the co-integration relationship between industry
stock returns and excess stock market returns, and it is co-authored with Prof José
Penalva and Prof Abderrahim Taamouti. We find that the equilibrium error term from
this co-integrating relationship has strong predictive power for excess stock returns, which
is increased if combined with the previous month's excess stock returns. Our results
suggest that short-term return reversals and liquidity measures are primary reasons for
the negative relation between the equilibrium error and expected excess stock returns.
We provide new evidence on the out-of-sample stock return predictability, in contrast
to Welch and Goyal (2008), among others, who found negligible out-of-sample predictive
power using standard variables. We also show that the out-of-sample explanatory power is
economically meaningful for investors. Simple trading strategies implied by the proposed
predictability provide portfolios with higher mean returns and Sharpe ratios than a buyand-
hold or a benchmark strategy does.The research presented in this thesis was supported by a scholarship from the Ministerio de Economía y Competitivad (ECO2013-46395).Programa Oficial de Doctorado en EconomíaPresidente: Miguel Ángel Delgado González; Secretario: Juan Carlos Escanciano Reyero; Vocal: José Olmo Bádena
Measuring High-Frequency Causality Between Returns, Realized Volatility and Implied Volatility
No full textIn this paper, we provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and implied volatility, and find that implied volatilities are essential for assessing the volatility feedback effect. The leverage hypothesis asserts that return shocks lead to changes in conditional volatility, while the volatility feedback effect theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On observing that a central difference between these alternative explanations lies in the direction of causality, we consider vector autoregressive models of returns and realized volatility and we measure these effects along with the time lags involved through short-run and long-run causality measures proposed in Dufour and Taamouti (2010), as opposed to simple correlations. We analyze 5-minute observations on S&P 500 Index futures contracts, the associated realized volatilities (before and after filtering jumps through the bispectrum) and implied volatilities. Using only returns and realized volatility, we find a strong dynamic leverage effect over the first three days. The volatility feedback effect appears to be negligible at all horizons. By contrast, when implied volatility is considered, a volatility feedback becomes apparent, whereas the leverage effect is almost the same. These results can be explained by the fact that volatility feedback effect works through implied volatility which contains important information on future volatility, through its nonlinear relation with option prices which are themselves forward-looking. In addition, we study the dynamic impact of news on returns and volatility. First, to detect possible dynamic asymmetry, we separate good from bad return news and find a much stronger impact of bad return news (as opposed to good return news) on volatility. Second, we introduce a concept of news based on the difference between implied and realized volatilities (the variance risk premium) and we find that a positive variance risk premium (an anticipated increase in variance) has more impact on returns than a negative variance risk premium.Volatility asymmetry, leverage effect, volatility feedback effect, risk premium, variance risk premium, multi-horizon causality, causality measure, high-frequency data, realized volatility, bipower variation, implied volatility.,
Asymptotic properties of the Bernstein density copula for dependent data
No full textCopulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based. This paper proposes nonparametric estimation of the density copula for α-mixing data using Bernstein polynomials. We study the asymptotic properties of the Bernstein density copula, i.e., we provide the exact asymptotic bias and variance, we establish the uniform strong consistency and the asymptotic normality.nonparametric estimation, copula, Bernstein polynomial, α-mixing, asymptotic properties, boundary bias
Finite-Sample Sign-Based Inference in Linear and Nonlinear Regression Models with Applications in Finance
No full textWe review several exact sign-based tests that have been recently proposed for testing orthogonality between random variables in the context of linear and nonlinear regression models. The sign tests are very useful when the data at the hands contain few observations, are robust against heteroskedasticity of unknown form, and can be used in the presence of non-Gaussian errors. These tests are also flexible since they do not require the existence of moments for the dependent variable and there is no need to specify the nature of the feedback between the dependent variable and the current and future values of the independent variable. Finally, we discuss several applications where the sign-based tests can be used to test for multi-horizon predictability of stock returns and for the market efficiency
The Reaction of Stock Market Returns to Unemployment
No full textWe empirically investigate the short-run impact of anticipated and unanticipated unemployment rates on stock prices. We particularly examine the nonlinearity in the stock market's reaction to the unemployment rate and study the effect at each individual point (quantile) of the stock return distribution. Using nonparametric Granger causality and quantile regression-based tests, we find that only anticipated unemployment rate has a strong impact on stock prices. Quantile regression analysis shows that the causal effects of anticipated unemployment rate on stock returns are usually heterogeneous across quantiles. For the quantile range (0.35, 0.80), an increase in the anticipated unemployment rate leads to an increase in stock market prices. For other quantiles, the impact is generally statistically insignificant. Thus, an increase in the anticipated unemployment rate is, in general, good news for stock prices. Finally, we offer a reasonable explanation for the reason, and manner in which, the unemployment rate affects stock market prices. Using the Fisher and Phillips curve equations, we show that a high unemployment rate is followed by monetary policy action of the Federal Reserve (Fed). When the unemployment rate is high, the Fed decreases the interest rate, which in turn increases the stock market prices.We thank the Editor-in-Chief Prof. Bruce Mizrach and two anonymous referees for their very useful comments. The authors
also thank Jean-Marie Dufour, Barbara Rossi, and Enrique Santana for their several useful comments. Earlier versions of this
paper were presented at the 65th European Meeting of the Econometric Society in Oslo (2011), the NBER - NSF Time Series
Conference in Michigan (2011), the XXVI Simposio of the Spanish Economic Association (SAEe) in Malaga (2011), and the 4th
International IFABSB conference on Rethinking Banking and Finance: Money, Markets and Models in Valencia (2012).
Financial support from the Spanish MINECO (grant ECO2013-46395) and Maria de Maeztu (grant MDM 2014-0431), Bank of
Spain (ER grant program), and MadEco-CM (grant S2015/HUM-3444) is gratefully acknowledged. Some results of this paper
were obtained when A. Taamouti was at Universidad Carlos III de Madrid
Analytical Value-at-Risk and Expected Shortfall under Regime Switching
No full textIt is well known that the use of Gaussian models to assess financial risk leads to an underestimation of risk. The reason is because these models are unable to capture some important facts such as heavy tails and volatility clustering which indicate the presence of large fluctuations in returns. An alternative way is to use regime-switching models, the latter are able to capture the previous facts. Using regime-switching model, we propose an analytical approximation for multi-horizon conditional Value-at-Risk and a closed-form solution for conditional Expected Shortfall. By comparing the Value-at-Risks and Expected Shortfalls calculated analytically and using simulations, we find that the both approaches lead to almost the same result. Further, the analytical approach is less time and computer intensive compared to simulations, which are typically used in risk management
Exact optimal and adaptive inference in regression models under heteroskedasticity and non-normality of unknown forms
No full textIn this paper, we derive simple point-optimal sign-based tests in the context of linear and
nonlinear regression models with fixed regressors. These tests are exact, distribution-free, robust
against heteroskedasticity of unknown form, and they may be inverted to obtain confidence
regions for the vector of unknown parameters. Since the point-optimal sign tests depend on the
alternative hypothesis, we propose an adaptive approach based on split-sample techniques in
order to choose an alternative such that the power of point-optimal sign tests is close to the
power envelope. The simulation results show that when using approximately 10% of sample to
estimate the alternative and the rest to calculate the test statistic, the power of point-optimal sign
test is typically close to the power envelope. We present a Monte Carlo study to assess the
performance of the proposed “quasi”-point-optimal sign test by comparing its size and power to
those of some common tests which are supposed to be robust against heteroskedasticity. The
results show that our procedures are superior
Covid‐19 Control and the Economy: Test, Test, Test*
No full textHard lockdowns have left policymakers to face the ethical dilemma of choosing between saving lives and saving the economy. However, massive testing could have helped to respond more effectively to Covid-19 crisis. In this paper, we study the trade-o§ between infection control, lockdown and testing. The aim is to understand how these policies can be effectively combined to contain Covid-19 without damaging the economy. An extended SIR epidemic model is developed to identify the set of testing and lockdown levels that lead to a reproduction number below one, thus to infection control and saving lives. Depending on whether the testing policy is static or dynamic, the model suggests that testing 4% to 7% of the population is the way to safely reopen the economy and the society
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