1,721,029 research outputs found
Smile from the past: A general option pricing framework with multiple volatility and leverage components
No full textA Simple Approximate Long-Memory Model of Realized Volatility
No full textThe paper proposes an additive cascade model of volatility components defined over different time periods. This volatility cascade leads to a simple AR-type model in the realized volatility with the feature of considering different volatility components realized over different time horizons and thus termed Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). In spite of the simplicity of its structure and the absence of true long-memory properties, simulation results show that the HAR-RV model successfully achieves the purpose of reproducing the main empirical features of financial returns (long memory, fat tails, and self-similarity) in a very tractable and parsimonious way. Moreover, empirical results show remarkably good forecasting performance. Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected], Oxford University Press.
When Micro Prudence Increases Macro Risk: The Destabilizing Effects of Financial Innovation, Leverage, and Diversification
Full text linkBy exploiting basic common practice accounting and risk-management rules, we propose a simple analytical dynamical model to investigate the effects of microprudential changes on macroprudential outcomes. Specifically, we study the consequence of the introduction of a financial innovation that allows reducing the cost of portfolio diversification in a financial system populated by financial institutions having capital requirements in the form of Value at Risk (VaR) constraint and following standard mark-to-market and risk-management rules. We provide a full analytical quantification of the multivariate feedback effects between investment prices and bank behavior induced by portfolio rebalancing in presence of asset illiquidity and show how changes in the constraints of the bank portfolio optimization endogenously drive the dynamics of the balance sheet aggregate of financial institutions and, thereby, the availability of bank liquidity to the economic system and systemic risk. The model shows that when financial innovation reduces the cost of diversification below a given threshold, the strength (because of higher leverage) and coordination (because of similarity of bank portfolios) of feedback effects increase, triggering a transition from a stationary dynamics of price returns to a nonstationary one characterized by steep growths (bubbles) and plunges (bursts) of market prices
Follow the money: The monetary roots of bubbles and crashes
No full textA reduced form model for the join dynamics of liquidity and asset prices is proposed. The self-reinforcing feedback between credit creation and the market value of the financial assets employed as collateral in the bank loans (the so called financial accelerator) is modeled by a coupled non-linear stochastic process. We show that such non-linear interaction produces explosive dynamics in the financial variables announcing a regime change in finite time in the form of a market crash which can also be modeled by the same coupled non-linear stochastic process with inverted signs. Casting the financial accelerator dynamics into a highly stylized macroeconomic model, we study its macro-dynamics implications for real economy and for monetary policy interventions. Finally, by exploiting the implications of the proposed model on the dynamics of financial asset returns, we introduce an extension of the GARCH process, that can provide an early warning identification of bubbles. © 2014 Elsevier Inc
Realized covariance tick-by-tick in presence of rounded time stamps and general microstructure effects
No full textThis paper presents two classes of tick-by-tick covariance estimators adapted to the case of rounding in the price time stamps to a frequency lower than the typical arrival rate of tick prices. Through Monte Carlo simulations, we investigate the behavior of such estimators under realistic market microstructure conditions analogous to those of the financial data examined in this paper's empirical section, that is, nonsynchronous trading, general ARMA structure for microstructure noise, and true lead–lag cross-covariance. Simulation results show the robustness of the proposed tick-by-tick covariance estimators to time stamp rounding, and their overall performance is superior to competing covariance estimators under empirically realistic microstructure conditions. These results are confirmed in the empirical application where the economic benefits of the proposed estimators are evaluated with volatility timing strategies applied to a bivariate portfolio of S&P 500 futures and 30-year U.S. treasury bond futures
Modeling tick-by-tick realized correlations
No full textA tree-structured heterogeneous autoregressive (tree-HAR) process is proposed as a simple and parsimonious model for the estimation and prediction of tick-by-tick realized correlations. The model can account for different time and other relevant predictors’ dependent regime shifts in the conditional mean dynamics of the realized correlation series. Testing the model on S&P 500 Futures and 30-year Treasury Bond Futures realized correlations, empirical evidence that the tree-HAR model reaches a good compromise between simplicity and flexibility is provided. The model yields accurate single- and multi-step out-of-sample forecasts. Such forecasts are also better than those obtained from other standard approaches, in particular when the final goal is multi-period forecasting
Discrete sine transform for multi-scale realized volatility measures
Full text linkIn this study we present a new realized volatility estimator based on a combination of the multi-scale regression and discrete sine transform (DST) approaches. Multi-scale estimators similar to that recently proposed by Zhang (2006) can, in fact, be constructed within a simple regression-based approach by exploiting the linear relation existing between the market microstructure bias and the realized volatilities computed at different frequencies. We show how such a powerful multi-scale regression approach can also be applied in the context of the Zhou [Nonlinear Modelling of High Frequency Financial Time Series, pp. 109–123, 1998] or DST orthogonalization of the observed tick-by-tick returns. Providing a natural orthonormal basis decomposition of observed returns, the DST permits the optimal disentanglement of the volatility signal of the underlying price process from the market microstructure noise. The robustness of the DST approach with respect to the more general dependent structure of the microstructure noise is also shown analytically. The combination of the multi-scale regression approach with DST gives a multi-scale DST realized volatility estimator similar in efficiency to the optimal Cramer–Rao bounds and robust against a wide class of noise contamination and model misspecification. Monte Carlo simulations based on realistic models for price dynamics and market microstructure effects show the superiority of DST estimators over alternative volatility proxies for a wide range of noise-to-signal ratios and different types of noise contamination. Empirical analysis based on six years of tick-by-tick data for the S&P 500 index future, FIB 30, and 30 year U.S. Treasury Bond future confirms the accuracy and robustness of DST estimators for different types of real data
Robust Recursive Filtering and Smoothing
Full text linkUsing a perturbation technique, we derive a new approximate filtering and
smoothing methodology generalizing along different directions several existing
approaches to robust filtering based on the score and the Hessian matrix of the
observation density. The main advantages of the methodology can be summarized
as follows: (i) it relaxes the critical assumption of a Gaussian prior
distribution for the latent states underlying such approaches; (ii) can be
applied to a general class of state-space models including univariate and
multivariate location, scale and count data models; (iii) has a very simple
structure based on forward-backward recursions similar to the Kalman filter and
smoother; (iv) allows a straightforward computation of confidence bands around
the state estimates reflecting the combination of parameter and filtering
uncertainty. We show through an extensive Monte Carlo study that the mean
square loss with respect to exact simulation-based methods is small in a wide
range of scenarios. We finally illustrate empirically the application of the
methodology to the estimation of stochastic volatility and correlations in
financial time-series.Comment: 44 pages, 5 figures, 6 table
HARK the SHARK: Realized Volatility Modeling with Measurement Errors and Nonlinear Dependencies
Full text linkDespite their effectiveness, linear models for realized variance neglect measurement errors on integrated variance and exhibit several forms of misspecification due to the inherent nonlinear dynamics of volatility. We propose new extensions of the popular approximate long-memory heterogeneous autoregressive (HAR) model apt to disentangle these effects and quantify their separate impact on volatility forecasts. By combining the asymptotic theory of the realized variance estimator with the Kalman filter and by introducing time-varying HAR parameters, we build new models that account for: (i) measurement errors (HARK), (ii) nonlinear dependencies (SHAR) and (iii) both measurement errors and nonlinearities (SHARK). The proposed models are simply estimated through standard maximum likelihood methods and are shown, both on simulated and real data, to provide better out-of-sample forecasts compared to standard HAR specifications and other competing approaches
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