Is Arch Useful in High Frequency Foreign Exchange Applications?

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One of the many challenges posed by the study of high frequency financial market data is to develop models capable of explaining asset price behaviour at a range of frequencies. At the same time as presenting researchers with new opportunities, it also calls into question whether standard time series models are useful in high frequency applications. This paper addresses this issue from two perspectives. First, a Monte Carlo procedure is used to investigate whether the unconditional distribution of high frequency foreign exchange returns can be approximated by the unconditional distribution of returns simulated by a range of popular stochastic processes. Second, high frequency data is used to generate and appraise forecasts of daily variance. Forecasts are evaluated using statistical criteria as well as a profitability measure based on a trading game in a pseudo options market.

The simulation exercise demonstrates that the autoregressive conditional heteroskedasticity (ARCH) family of models is unable to reproduce the unconditional distribution of foreign exchange returns at frequencies higher than 24 hours. This is largely a legacy of the heavy-tailed feature of intraday returns. However, results from the forecasting analysis extend those in Andersen and Bollerslev (1998) by showing that a range of standard volatility models can in fact produce accurate forecasts of realized daily variance. In other words, it is possible for ARCH models to predict variability in the conditional second moment of daily foreign exchange returns. This is attributed to the use of frequently sampled data in the construction of estimates of realized variance (against which forecasts are measured). In addition, the inclusion of the sum of squared intraday returns in the Generalized ARCH(1,1) model yields improvements in the modelling, and most notably forecasting, of realized daily variance. This appears to be an artifact of the noise inherent in using the daily squared return as an estimator of realized daily variance.

This paper demonstrates that whilst standard econometric models do not capture the intraday foreign exchange return generating process, this should not immediately preclude these models from high frequency applications. Instead, the forecasting exercise demonstrates practical benefits are easily attainable from using high frequency data to develop and evaluate existing asset pricing models.


Forecasting Short Term Interest Rates Using ARMA, ARMA-GARCH and ARMA-EGARCH Models

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Forecasting interest rates is of great concern for financial researchers, economists and players in the fixed income markets. The purpose of this study is to develop an appropriate model for forecasting the short-term interest rates i.e., commercial paper rate, implicit yield on 91 day treasury bill, overnight MIBOR rate and call money rate. The short-term interest rates are forecasted using univariate models, Random Walk, ARIMA, ARMA-GARCH and ARMA-EGARCH and the appropriate model for forecasting is determined considering six-year period from 1999. The results show that interest rates time series have volatility clustering effect and hence GARCH based models are more appropriate to forecast than the other models. It is found that for commercial paper rate ARIMA-EGARCH model is most appropriate model, while for implicit yield 91 day Treasury bill, overnight MIBOR rate and call money rate, ARIMA-GARCH model is the most appropriate model for forecasting.

Predicting Break-Points in Trading Strategies with Twitter

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The importance of being able to identify changepoints in financial time series has been stressed by many authors, both for econometric forecasting and for enhancing the performance of trading strategies. Strategies which work well in one type of context may lose money under different circumstances so it is crucial for traders to be able to identify break-points as they occur.

Our basic hypothesis is that these breakpoints are usually linked to factors outside the markets such as breaking news or the changes in the socio-political context. The buzz on the internet especially on social networks like Twitter provides an advance indicator of these changes.

In this paper we propose a novel approach for identifying micro-breakpoints based on Twitter. In order to measure the impact on trading strategies, of knowing when changes occur, we used a simple genetic algorithm using Forex data as the reference benchmark. It makes a decision every two minutes whether to hold US dollars or euro. We compare its performance to a hybrid algorithm which stops trading and goes through a relearning phase after each Twitter alert. In tests over a 5 month period, the hybrid algorithm performed significantly better than the benchmark algorithm. One unexpected result was the discovery of a wave-like relationship between the time to react to each alert and the performance of the algorithmic trader. We call this the Twitter wave. As our results have only been validated over a 5 month period, longer tests are clearly required but the preliminary results are very promising. If confirmed they open up new perspectives for identifying micro-breakpoints in real-time, for high-frequency trading.

Methods for Estimating the Hurst Exponent of Stock Returns

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This note is a further commentary on a previous paper on the chaos theory of stock returns that derives from the alleged detection of persistence in time series data indicated by values of the Hurst exponent H that differs from the neutral value of H=0.5 implied by the efficient market hypothesis (EMH) (Munshi, 2014). A comparison of four different methods for estimating H is presented. Linear regression of log transformed values (OLS) is compared against a numerical approach using the generalized reduced gradient (GRG) method. These methods are applied to two different empirical models for the estimation of H. We find that the major source of error in the empirical estimation of H is the insertion of the extraneous constant C into the empirical model.

Automated Trading with Genetic-Algorithm Neural-Network Risk Cybernetics: An Application on FX Markets

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Recent years have witnessed the advancement of automated algorithmic trading systems as institutional solutions in the form of autobots, black box or expert advisors. However, little research has been done in this area with sufficient evidence to show the efficiency of these systems. This paper builds an automated trading system which implements an optimized genetic-algorithm neural-network (GANN) model with cybernetic concepts and evaluates the success using a modified value-at-risk (MVaR) framework. The cybernetic engine includes a circular causal feedback control feature and a developed golden-ratio estimator, which can be applied to any form of market data in the development of risk-pricing models. The paper applies the Euro and Yen forex rates as data inputs. It is shown that the technique is useful as a trading and volatility control system for institutions including central bank monetary policy as a risk-minimizing strategy. Furthermore, the results are achieved within a 30-second timeframe for an intra-week trading strategy, offering relatively low latency performance. The results show that risk exposures are reduced by four to five times with a maximum possible success rate of 96%, providing evidence for further research and development in this area.


Adaptive Expectations, Time-Series Models, and Analyst Forecast Revision

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The process by which analysts revise quarterly earnings forecasts is analyzed and compared to the way in which several time-series models of quarterly earnings revise forecasts. A significant portion of the analyst's forecast revision is explained by the most recent one-quarter-ahead forecast error. Analyst revisions are adaptive in the same manner that single-period ahead model forecasts are adaptive. At longer horizons, the evidence is that analysts revise forecasts in the same way that autoregressive models of quarterly earnings revise and not as moving average models do.

Price Lead-Lags in Indian Stock and Futures Market - A Wavelet Based Study

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This paper examines the relationship between the stock and futures markets in terms of cointegration (Johnson Cointegration) and lead lag relationship (Wavelet Approach). We applied the Maximum Overlap Discrete Wavelet Transform (MODWT) method to stock and futures prices of 12 near month contracts during the period April 2011 and March 2012. The study included 13 Scripts across sectors which are included both in BSE Sensex and Nifty 50. Empirical results show that stock and futures are cointegrated in the long run and there is either feedback relationship or futures lead across time scales and also we have seen in some scripts there is no lead lag neither contemporaneously nor in different time scales.

Comovement of Exchange Rates: A Wavelet Analysis

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In this paper we employ the wavelet multiple correlation and the wavelet multiple cross-correlation to investigate the behaviour of exchange rates in the Central and Eastern Europe (CEE). This novel approach takes care of several limitations which are encountered when conventional pair wise wavelet correlation and cross correlation are used to assess the comovement in the set of exchange rates. Our results show that CEE exchange rates are nearly perfectly integrated in the short and medium run, since the returns obtained in any of the CEE foreign exchange market can almost be explained by the overall performance in the other CEE markets. The discrepancies between CEE exchange rates are small, but increase within three to six months and that means in the long run the integration of foreign exchange markets is weak.

Wavelet Decomposition of the Financial Cycle: An Early Warning System for Financial Tsunamis

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We propose a wavelet-based approach for construction of a financial cycle proxy. Specifically, we decompose three key macro-financial variables – private credit, house prices, and stock prices – on a frequency-scale basis using wavelet multiresolution analysis. The resulting “wavelet-based” sub-series are aggregated into a composite index representing our cycle proxy. Selection of the sub-series deemed most relevant is done by emphasizing early warning properties. The wavelet-based financial cycle proxy is shown to perform well in detecting banking crises in out-of-sample exercises, outperforming the credit-to-GDP gap and a financial cycle proxy derived using the approach of Schüler et al. (2015).


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