8,254 research outputs found
Common price and volatility jumps in noisy high-frequency data
We introduce a statistical test for simultaneous jumps in the price of a
financial asset and its volatility process. The proposed test is based on
high-frequency data and is robust to market microstructure frictions. For the
test, local estimators of volatility jumps at price jump arrival times are
designed using a nonparametric spectral estimator of the spot volatility
process. A simulation study and an empirical example with NASDAQ order book
data demonstrate the practicability of the proposed methods and highlight the
important role played by price volatility co-jumps
Estimating the COP Exchange Rate Volatility Smile and the Market Effect of Central Bank Interventions: A CHARN Approach
In this paper we estimated a volatility model for COP/US under two different samples, one containing the information before the “discretional interventions” started, and the other using the whole sample. We use a nonparametric approach to estimate the mean and “volatility smile” return functions using daily data. For the pre-interventions sample, we found a nonlinear expected return function and, surprisingly, a nonsymmetric “volatility smile”. These lack of linearity and symmetry are related to absolute returns above 1,5% and 1,0%, respectively. We also found that the “discretional interventions” did not shift the mean response function, but moved the expected returns along the line towards the required levels. In contrast, the “volatility smile” tends to increase in a non-symmetric way after accounting for “discretional interventions”. The Sep/29/2004 announcement does not seem to have had any effect on the expected conditional mean or variance functions, but the Dec/17/2004 announcement seems to be related to non-symmetric effects on the volatility smile. We concluded that the announcement of discretional intervention by the monetary authority was more efficient when time and amount were unannounced.Volatility Smile,
Factores Macroecon�micos en Retornos Accionarios Chilenos
We evaluate the growth effects of real exchange rate (RER) misalignments and their volatility. We calculate RER misaligments as deviations of actual RERs from their equilibrium for 60 countries over 1965-2003 using panel and time series cointegration methods. Using dynamic panel data techniques we find that RER misalignments hinder growth but the effect is non-linear: growth declines are larger, the larger the size of the overvaluation. Although large undervaluations hurt growth, small to moderate undervaluations enhance growth. However, we find that it is difficult to follow a pro-growth RER policy. Finally, growth is hampered by highly volatile RER misalignments.
Cross-correlations between volume change and price change
In finance, one usually deals not with prices but with growth rates ,
defined as the difference in logarithm between two consecutive prices. Here we
consider not the trading volume, but rather the volume growth rate ,
the difference in logarithm between two consecutive values of trading volume.
To this end, we use several methods to analyze the properties of volume changes
, and their relationship to price changes . We analyze
daily recordings of the S\&P 500 index over the 59-year period
1950--2009, and find power-law {\it cross-correlations\/} between and
using detrended cross-correlation analysis (DCCA). We introduce a
joint stochastic process that models these cross-correlations. Motivated by the
relationship between and , we estimate the tail exponent
of the probability density function for both the S\&P 500 index as well as the
collection of 1819 constituents of the New York Stock Exchange Composite index
on 17 July 2009. As a new method to estimate , we calculate the
time intervals between events where . We demonstrate that
, the average of , obeys . We find . Furthermore, by
aggregating all values of 28 global financial indices, we also observe
an approximate inverse cubic law.Comment: 7 pages, 5 figure
Long term memories of developed and emerging markets: using the scaling analysis to characterize their stage of development
The scaling properties encompass in a simple analysis many of the volatility
characteristics of financial markets. That is why we use them to probe the
different degree of markets development. We empirically study the scaling
properties of daily Foreign Exchange rates, Stock Market indices and fixed
income instruments by using the generalized Hurst approach. We show that the
scaling exponents are associated with characteristics of the specific markets
and can be used to differentiate markets in their stage of development. The
robustness of the results is tested by both Monte-Carlo studies and a
computation of the scaling in the frequency-domain.Comment: 46 pages, 7 figures, accepted for publication in Journal of Banking &
Financ
Shortcomings of a parametric VaR approach and nonparametric improvements based on a non-stationary return series model
A non-stationary regression model for financial returns is examined theoretically in this paper. Volatility dynamics are modelled both exogenously and deterministic, captured by a nonparametric curve estimation on equidistant centered returns. We prove consistency and asymptotic normality of a symmetric variance estimator and of a one-sided variance estimator analytically, and derive remarks on the bandwidth decision. Further attention is paid to asymmetry and heavy tails of the return distribution, implemented by an asymmetric version of the Pearson type VII distribution for random innovations. By providing a method of moments for its parameter estimation and a connection to the Student-t distribution we offer the framework for a factor-based VaR approach. The approximation quality of the non-stationary model is supported by simulation studies. --heteroscedastic asset returns,non-stationarity,nonparametric regression,volatility,innovation modelling,asymmetric heavy-tails,distributional forecast,Value at Risk (VaR)
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model
This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the dependency structure and distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new estimation method and suggest that it works reasonably well in finite samples. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.Autoregression, nonlinear/non-Gaussian time series, realized volatility, semiparametric model, volatility forecast
Nonparametric Option Pricing under Shape Restrictions
Frequently, economic theory places shape restrictions on functional relationships between economic variables. This paper develops a method to constrain the values of the first and second derivatives of nonparametric locally polynomial estimators. We apply this technique to estimate the state price density (SPD), or risk-neutral density, implicit in the market prices of options. The option pricing function must be monotonic and convex. Simulations demonstrate that nonparametric estimates can be quite feasible in the small samples relevant for day-to-day option pricing, once appropriate theory-motivated shape restrictions are imposed. Using S&P500 option prices, we show that unconstrained nonparametric estimators violate the constraints during more than half the trading days in 1999, unlike the constrained estimator we propose.
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