Time-Varying Currency Betas: Evidence from Developed and Emerging Markets

This paper examines the conditional time-varying currency betas from five developed markets and four emerging markets. A trivariate BEKK-GARCH-in-mean model is used to estimate the timevarying conditional variance and covariance of returns of stock index, the world market portfolio and changes in bilateral exchange rate between the US dollar and the local currency of each country. It is found that currency betas are more volatile than those of the world market betas. Currency betas in emerging markets are more volatile than those in developed markets. Moreover, we find evidence of long-memory in currency betas. The usefulness of time-varying currency betas are illustrated by two applications.time-varying currency betas; multivariate GARCH-M models; international CAPM; fractionally integrated processes; stochastic dominance

Volatility Dynamics in Foreign Exchange Rates: Further Evidence from the Malaysian Ringgit and Singapore Dollar

Singapore dollar are analyzed in this paper. Our approach can simultaneously capture the empirical regularities of persistent and asymmetric effects in volatility and timevarying correlations of financial time series. Consistent with the results of Tse and Tsui (1997), there is only some weak support for asymmetric volatility in the case of the Malaysian ringgit when the two currencies are measured against the US dollar. However, there is strong evidence that depreciation shocks have a greater impact on future volatility levels compared with appreciation shocks of the same magnitude when both currencies measured against the yen. Moreover, evidence of time-varying correlation is highly significant when both currencies are measured against the yen. Regardless of the choice of the numeraire currency and the volatility models, shocks to exchange rate volatility are found to be significantly persistent.Constant correlations; Exchange rate volatility; Fractional integration; Long memory; Bivariate asymmetric GARCH; Varying correlations

Time-varying persistence in US inflation

The persistence property of inflation is an important issue not only for economists, but especially for central banks, given that the degree of inflation persistence determines the extent to which central banks can control inflation. Further, not only is it the level of inflation persistence that is important in economic analyses, but also the question of whether the persistence varies over time, for instance, across business cycle phases, is equally pertinent, since assuming constant persistence across states of the economy is sure to lead to misguided policy decisions. Against this backdrop, we extend the literature on long-memory models of inflation persistence for the US economy over the monthly period of 1920:1\u20132014:5, by developing an autoregressive fractionally integrated moving-average-generalized autoregressive conditional heteroskedastic model with a time-varying memory coefficient which varies across expansions and recessions. In sum, we find that inflation persistence does vary across recessions and expansions, with it being significantly higher in the former than in the latter. As an aside, we also show that persistence of inflation volatility is higher during expansions than in recessions. Understandably, our results have important policy implications

The volatility of realized volatility

Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic-volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing "observable" or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time-series models for realized volatility exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time-varying volatility of realized volatility leads to a substantial improvement of the model's fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting. Klassifikation: C22, C51, C52, C5

Modeling Long Memory in REITs

One stylized feature of financial volatility impacting the modeling process is long memory. This paper examines long memory for alternative risk measures, observed absolute and squared returns for Daily REITs and compares the findings for a market equity index. The paper utilizes a variety of tests for long memory finding evidence that REIT volatility does display persistence. Trading volume is found to be strongly associated with long memory. The results do however suggest differences in the findings with regard to REITs in comparison to the broader equity sector.

Measurement of Financial Risk Persistence

This paper discusses various ways of measuring the persistence or Long Memory (LM) of financial market risk in both its time and frequency domains. For the measurement of the risk, irregularity or 'randomness' of these series, we can compute a set of critical Lipschitz - Hölder exponents, in particular, the Hurst Exponent and the Lévy Stability Alpha, and relate them to the Mandelbrot-Hoskings' fractional difference operators, as occur in the Fractional Brownian Motion model (which is our benchmark). The main contribution of this paper is to provide a compaison table of the various critical exponents available in various scientific disciplines to measure the LM persistence of time seies. It also discusses why Markov- and (G)ARCH models cannot capture this LM, long term dependence or risk persistence, because these models have finite lag lengths, while the empirically observed long memory risk phenomenon is an infinite lag length phenomenon. Currently, there are three techniques of nonstationary time series analysis to measure time - varying financial risk: Range/Scale analysis, windowed Fourier analysis, and wavelet MRA. This paper relates these powerful analytic techniques to classical Box-Jenkins-type time series analysis and to Pearson's spectral frequency analysis, which both rely on the uncorroboated assumption of stationarity and ergodicity.Persistence, long memory, dependence, time series, frequency, critical exponents, fractional Brownian motion, (G)ARCH, risk measurement

A fractionally integrated ECOGARCH process

In this paper we introduce a fractionally integrated exponential continuous time GARCH(p,d,q) process. It is defined in such a way that it is a continuous time extension of the discrete time FIEGARCH(p,d,q) process. We investigate stationarity and moment properties of the new model. It is also shown that the long memory effect introduced in the log-volatility propagates to the volatility process

Links between the Indian, U.S. and Chinese Stock Markets

This study examines the bilateral relations between three pairs of stock markets, namely India-U.S., India-China and China-U.S. We use a Fractionally Integrated Vector Error Correction Model (FIVECM) to examine the cointegration mechanism between markets. By augmenting the FIVECM with a multivariate GARCH formulation, we study the first and second moment spillover effects simultaneously. Our empirical results show that all three pairs of stock markets are fractionally cointegrated. The U.S. stock market plays a dominant role in the relations with the other two markets, whereas there is an interactive relationship between the Indian and Chinese stock markets. In particular, the Indian stock market dominates the first moment feedback with the Chinese market, while the latter dominates the second moment feedback with the former.Stock market, Cointegration, Fractionally Integrated Vector Error Correction Model, Multivariate GARCH

Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models

Recently, Donaldson and Kamstra (1997) proposed a class of NN-GARCH models which are extended to a class of NN-GARCH family by Bildirici and Ersin (2009). The study aims to analyze the nonlinear behavior and leptokurtic distribution in petrol prices by utilizing a newly developed family of econometric models that deal with these concepts by benefiting from both LSTAR type and ANN based nonlinearity. With this purpose, the study proposed several LSTAR-GARCH-NN family models. It is noted that the multilayer perceptron (MLP) neural network and LSTAR models have significant architectural similarities. Accordingly, linear GARCH, fractionally integrated FI-GARCH, asymmetric power APGARCH and fractionally integrated asymmetric power APGARCH models are augmented with a family of Neural Network models. The study has following contributions: i. STAR-GARCH and LSTAR-GARCH are extended to their fractionally integrated asymmetric power versions and STAR-ST-FIGARCH and STAR-ST-APGARCH, STAR-ST-FIAPGARCH models are developed and evaluated. ii. By extending these models with neural networks, LSTAR-LST-GARCH-MLP family models are developed and investigated. These models benefit from LSTAR type nonlinearity and NN based nonlinear NN-GARCH models to capture time varying volatility and nonlinearity in petrol prices. ANN augmented versions of LSTAR-LST-GARCH models are as follows: LSTAR-LST-GARCH-MLP, LSTAR-LST-FIGARCH-MLP, LSTAR-LST-APGARCH-MLP and LSTAR-LST-FIAPGARCH-MLP. Empirical findings are collected as follows. i. To model petrol prices, fractionally integrated and asymmetric power versions provided improvements among the GARCH family models in terms of forecasting. ii. LSTAR-LST-GARCH model family is promising and show significant gains in out-of-sample forecasting. iii. MLP-GARCH family provided similar results with the LSTAR-LST-GARCH family models, except for the MLP-FIGARCH and MLP-FIAPGARCH models. iv. Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are captured most efficiently with the LSTAR-LST-GARCH-MLP models benefiting from forecasting capabilities of neural network techniques, whereas, among the newly developed models, LSTAR-LST-APGARCH-MLP model provided the best performance overall