Errors-in-Variables Estimation with No Instruments

This paper develops a wavelet (spectral) approach to estimate the parameters of a linear regression model where the regressand and the regressors are persistent processes and contain a measurement error. We propose a wavelet filtering approach which does not require instruments and yields unbiased and consistent estimates for the intercept and the slope parameters. Our Monte Carlo results also show that the wavelet approach is particularly effective when measurement errors for the regressand and the regressor are serially correlated. With this paper, we hope to bring a fresh perspective and stimulate further theoretical research in this areaCointegration, discrete wavelet transformation, maximum overlap wavelet transformation, energy decomposition, errors-in-variables, persistence

Crash of ’87 - Was it Expected? Aggregate Market Fears and Long Range Dependence

We develop a dynamic framework to identify aggregate market fears ahead of a major market crash through the skewness premium of European options. Our methodology is based on measuring the distribution of a skewness premium through a q-Gaussian density and a maximum entropy principle. Our findings indicate that the October 19th, 1987 crash was predictable from the study of the skewness premium of deepest out-of-the-money options about two months prior to the crashNon-additive Entropy, Shannon Entropy, Tsallis Entropy, q-Gaussian Distribution, Skewness Premium

The dynamic interaction of order flows and the CAD/USD exchange rate

We explore the relationship between disaggregated order flow, the Canada/U.S. dollar (CAD/USD) market and U.S. macroeconomic announcements. Three types of CAD order flow and the CAD/USD are cointegrated. Financial order flow appears to contemporaneously drive the CAD/USD while commercial order flow seems to contemporaneously respond to exchange rate movements. Past order flow and lagged exchange rates strongly explain most types of order flow. Despite this predictability and the contemporaneous correlation of order flow with exchange rate returns, exchange rate returns are not predictable by either statistical or economic criteria (trading rule). This negative finding contrasts with that of Rime et al (2007), who use a different data set. There is strong evidence of structural breaks in the order-flow-exchange rate systems in 1994, 1996-1997 and 1999-2000.Foreign exchange rates

Profitability in an Electronic Foreign Exchange Market: Informed Trading or Differences in Valuation?

Fundamental spot exchange rate models preclude the existence of asymmetric information in foreign exchange markets. This article critically investigates the possibility that private information arises in the spot foreign exchange market. Using a rich dataset, we first empirically detect transaction behavior consistent with the informed trading hypothesis. We then work within the theoretical framework of a high-frequency version of a structural microstructure trade model, which directly measures the market maker’s beliefs. We find that the time-varying pattern of the probability of informed trading is rooted in the strategic arrival of informed traders on a particular hour-of-day, day-of-week, or geographic location (market)Foreign Exchange Markets; Volume; Informed Trading; Noise Trading

The Profitability Of Technical Trading Rules: A Combined Signal Approach

The focus of this paper is to determine the profitability of technical trading rules by evaluating their ability to outperform the naïve buy-and-hold trading strategy. Moving average cross-over rules, filter rules, Bollinger Bands, and trading range break-out rules are tested on the S&P/TSX 300 Index, the Dow Jones Industrial Average Index, NASDAQ Composite Index, and the Canada/U.S. spot exchange rate. After accounting for transaction costs, excess returns are generated by the moving average cross-over rules and trading range break-out rules for the S&P/TSX 300 Index, NASDAQ Composite Index and the Canada/U.S. spot exchange rate. Filter rules also earn excess returns when applied on the Canada/U.S. spot exchange rate. The bootstrap methodology is used to determine the statistical significance of the results. The profitability of the technical trading rules is further enhanced with a combined signal approach

Asymmetry of Information Flow Between Volatilities Across Time Scales

Conventional time series analysis, focusing exclusively on a time series at a given scale, lacks the ability to explain the nature of the data generating process. A process equation that successfully explains daily price changes, for example, is unable to characterize the nature of hourly price changes. On the other hand, statistical properties of monthly price changes are often not fully covered by a model based on daily price changes. In this paper, we simultaneously model regimes of volatilities at multiple time scales through wavelet-domain hidden Markov models. We establish an important stylized property of volatility across different time scales. We call this property asymmetric vertical dependence. It is asymmetric in the sense that a low volatility state (regime) at a long time horizon is most likely followed by low volatility states at shorter time horizons. On the other hand, a high volatility state at long time horizons does not necessarily imply a high volatility state at shorter time horizons. Our analysis provides evidence that volatility is a mixture of high and low volatility regimes, resulting in a distribution that is non-Gaussian. This result has important implications regarding the scaling behavior of volatility, and consequently, the calculation of risk at different time scalesDiscrete wavelet transform, wavelet-domain hidden Markov trees, foreign exchange markets, stock markets, multiresolution analysis, scaling

Crash of '87 - Was it expected?: Aggregate market fears and long-range dependence

We develop a dynamic framework to identify aggregate market fears ahead of a major market crash through the skewness premium of European options. Our methodology is based on measuring the distribution of a skewness premium through a q-Gaussian density and a maximum entropy principle. Our findings indicate that the October 19th, 1987 crash was predictable from the study of the skewness premium of deepest out-of-the-money options about two months prior to the crash

Errors-in-Variables Estimation with Wavelets

This paper proposes a wavelet (spectral) approach to estimate the parameters of a linear regression model where the regressand and the regressors are persistent processes and contain a measurement error. We propose a wavelet filtering approach which does not require instruments and yields unbiased estimates for the intercept and the slope parameters. Our Monte Carlo results also show that the wavelet approach is particularly effective when measurement errors for the regressand and the regressor are serially correlated. With this paper, we hope to bring a fresh perspective and stimulate further theoretical research in this area

Overnight interest rates and aggregate market expectations

This paper introduces an entropy approach to measuring market expectations with respect to overnight interest rates in an inter-bank money market. The findings for the Turkish 2000-2001 borrowing crisis suggest that a dynamic, non-extensive entropy framework provides a valuable insight into the degree of aggregate market concerns during the crisis

Clustering and Classification in Option Pricing

This paper reviews the recent option pricing literature and investigates how clustering and classification can assist option pricing models. Specifically, we consider non-parametric modular neural network (MNN) models to price the S&P-500 European call options. The focus is on decomposing and classifying options data into a number of sub-models across moneyness and maturity ranges that are processed individually. The fuzzy learning vector quantization (FLVQ) algorithm we propose generates decision regions (i.e., option classes) divided by ‘intelligent’ classification boundaries. Such an approach improves generalization properties of the MNN model and thereby increases its pricing accuracy