Profiling high-frequency equity price movements in directional changes

Market prices are traditionally sampled in fixed time intervals to form time series. Directional change (DC) is an alternative approach to record price movements. Instead of sampling at fixed intervals, DC is data driven: price changes dictate when a price is recorded. DC provides us with a complementary way to extract information from data. It allows us to observe features that may not be recognized in time series. The argument is that time series and DC-based analysis complement each other. With data sampled at irregular time intervals in DC, however, some of the time series indicators cannot be used in DC-based analysis. For example, returns must be time adjusted and volatility must be amended accordingly. A major objective of this paper is to introduce indicators for profiling markets under DC. We analyse empirical high-frequency data on major equities traded on the UK stock market, and through DC profiling extract information complementary to features observed through time series profiling.     

Nonlinear prediction of exchange rates with monetary fundamentals

This paper employs a neural network (NN) to study the nonlinear predictability of exchange rates for four currencies at the 1-, 6- and 12-month forecast horizons. We find that our neural network model with market fundamentals cannot beat the random walk (RW) in out-of-sample forecast accuracy, although it occasionally shows a limited market-timing ability. The neural network model without monetary fundamentals forecasts somewhat better for the British pound and the Canadian dollar. The model also exhibits some market-timing ability for the Deutsche mark at the 6- and 12-month horizons, and for the Canadian dollar at the 1-month horizon. In general, the model performs more poorly when it becomes more complex or when the forecast horizon lengthens. Our overall results are more on the negative side and suggest that neither nonlinearity nor market fundamentals appear to be very important in improving exchange rate forecast for the chosen horizons.     

Gold mining companies and the price of gold

This paper studies the exposure of Australian gold mining firms to changes in the gold price. We use a theoretical framework to formulate testable hypotheses regarding the gold exposure of gold mining firms. The empirical analysis based on all gold mining firms in the S&;P/ASX All Ordinaries Gold Index for the period from January 1980 to December 2010 finds that the average gold beta is around one but varies significantly through time. The relatively low average gold beta is attributed to the hedging and diversification of gold mining firms. We further find an asymmetric effect in gold betas, i.e. the gold exposure increases with positive gold price changes and decreases with negative gold price changes consistent with gold mining companies exercising real options on gold.     

Gold price dynamics and the role of uncertainty

This study focuses on the dynamics of the gold price against bonds, stocks and exchange rates based on a disaggregation of the underlying relationships across different frequencies applying a wavelet decomposition. To analyze joint extreme movements (i.e. tail dependence), we adopt a copula approach, which helps us to assess the dependence between the returns of gold and other assets in calm and turmoil market times and therefore the hedge and safe haven functions of gold. We also examine whether gold prices are directly affected by changes in macroeconomic uncertainty, economic policy uncertainty and/or CPI forecasters disagreement. Analyzing data for nine economies for a sample period starting in 1985, we find that the role of gold changes significantly after the collapse of Lehman Brothers in 2008. Gold is unable to serve as a hedge or safe haven in the classical sense while the findings for the period prior to 2008 mostly suggest that gold is able to shield investors. Uncertainty measures display a surprising and time-varying relationship with the path of the gold price. While economic policy uncertainty is positively correlated with gold price changes, macroeconomic uncertainty and inflation uncertainty among forecasters are both negatively related to gold price changes.     

Asset price prediction using seasonal decomposition

We propose a new forecasting procedure for asset prices using seasonal decomposition methods (SD hereafter), e.g., SABL and X-11. Such SD's are based on moving average methods, and they are thus easy to use and are capable of computing the seasonal pattern that changes over time. A SD typically decomposes a series intoT (trend),S (seasonal component), andR (residual or sometimes referred to as the irregular component). We use an ARIMA model onR to obtain its forecast. TheS component is forecasted by an extrapolation taking into account its changing pattern within the sample period. We propose to set up some scenarios on theT component by examining its possibly nonlinear and nonstationary behavior, and in the paper we suggest one possible way for this. Suppose that the forecasting horizon is relatively short compared toT's several cycles just before the end of the sample. Then we may safely extrapolateT linearly into the forecasting period. LinearizingT in such a case, makes sense. As to the slope of the linear line, we suggest the average rate of change of the most recent upward phase of a cycle to be used if we needed an optimistic scenario. Obviously, that of the downward phase may be used for constructing a pessimistic scenario, and that of one entire cycle is suitable for ‘average’ scenario. Once the forecasted values of the three components are obtained, we may put them back to make predictions on the original series based upon various different scenarios. In addition to proposing a new prediction method, we looked into the following issues, among others, in the paper: (1) on what sort of asset prices would our forecasting method work well? (2) Any significant differences if we used X-11 instead of SABL?     

A neural network enhanced volatility component model

Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio.     

Nonparametric prediction for the time-dependent volatility of the security price

In this paper we consider a continuous time model for the security price with the time-dependent volatility. It is shown that the non-normality and non-linear dependency of the short-term return, the major characteristics observed on many financial assets, can be incorporated into our model. In order to evaluate the option price formula on the model we propose a nonparametric predictor for the volatility function without reference to a specific functional form. We examine the so-called continuous record asymptotics and show that the proposed predictor is asymptotically minimax for a wide class of the volatility functions. One of the most important results is that the application of the Black-Scholes method can be justified by plugging the proposed predictor in the standard Black-Scholes formula even if the volatility changes over time.     

The intrinsic value of gold: An exchange rate-free price index

In this paper, we propose a gold price index that enables market participants to separate the change in the ‘intrinsic’ value of gold from changes in global exchange rates. The index is a geometrically weighted average of the price of gold denominated in different currencies, with weights that are proportional to the market power of each country in the global gold market. Market power is defined as the impact that a change in a country’s exchange rate has on the price of gold expressed in other currencies. We use principal components analysis to reduce the set of global exchange rates to four currency ‘blocs’ representing the U.S. dollar, the euro, the commodity currencies and the Asian currencies, respectively. We estimate the weight of each currency bloc in the index in an error correction framework using a broad set of variables to control for the unobserved intrinsic value. We show that the resulting index is less volatile than the USD price of gold and, in contrast with the USD price of gold, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset.     

Asymmetric volatility in the foreign exchange markets

We examine the presence or absence of asymmetric volatility in the exchange rates of Australian dollar (AUD), Euro (EUR), British pound (GBP) and Japanese yen (JPY), all against US dollar. Our investigation is based on a variant of the heterogeneous autoregressive realized volatility model, using daily realized variance and return series from 1996 to 2004. We find that a depreciation against USD leads to significantly greater volatility than an appreciation for AUD and GBP, whereas the opposite is true for JPY. Relative to volatility on days following a positive one-standard-deviation return, volatility on days following a negative one-standard-deviation return is higher by 6.6% for AUD, 6.1% for GBP, and 21.2% for JPY. The realized volatility of EUR appears to be symmetric. These results are robust to the removal of jump component from realized volatility and the sub-samplings defined by structural-changes. The asymmetry in AUD, GBP and JPY appears to be embedded in the continuous component of realized volatility rather than the jump component.     

How do the Renminbi and other East Asian currencies co-move?

This paper investigates the degree and the nature of exchange rate co-movements between the Renminbi and a set of seven East Asian currencies by estimating Markov switching models with regime-dependent correlations and time-varying transition probabilities. These models have several advantages. First, exchange rate co-movements can vary across different depreciation and appreciation regimes. Second, the Renminbi can act as a transition variable that provides information regarding how the exchange rates evolve over time. After controlling for global effects and exchange market pressures, the results yield robust evidence of the Renminbi’s rising role in East Asia as a significant factor in currency fluctuations. A key result is that regional currencies tend to overreact when the Renminbi depreciates and underreact when it appreciates, suggesting that East Asian economies are not willing to allow their currency to substantially appreciate against the Chinese currency.     

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.     

Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey

Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.     

Economic evaluation of asymmetric and price range information in gold and general financial markets

Gold is widely perceived as a good diversification or safe haven tool for general financial markets, especially in market turmoil. To fully understand the potential, this study constructs an asymmetric multivariate range-based volatility model to investigate the dependence and volatility structures of gold, stock, and bond markets and further to compare the difference between the financial crisis and post-financial crisis periods. We find a striking explanatory ability to volatility structures provided by the price range information and significant evidence of asymmetric dependence across gold, stock, and bond markets. We implement an asset-allocation strategy incorporating asymmetric dependence and price range information to explore their economic importance. The out-of-sample results show that between 35 and 517 basis points and between 90 and 1111 basis points are earned annually when acknowledging asymmetric dependence and price range information, respectively. These economic benefits are inversely related to the level of investors’ risk aversion and are particularly significant in the period of the global financial crisis.     

人民币外汇市场间不对称汇率变动的实证研究

现有关于人民币汇率各市场间关系的研究一般是基于多元GARCH模型,探讨各市场间的线性相关关系,未能考虑各市场间汇率变动可能存在的"不对称效应":面临正(反)向的较大冲击时,各市场汇率变动表现出同步性;而面临反(正)向的较大冲击时,各市场汇率变动不同步。本文运用SJC-Copula-MGARCH模型对人民币汇率境内SPOT市场、境内DF市场和境外NDF市场之间的相依关系进行实证分析,发现境内汇率市场(SPOT市场和DF市场)和境外NDF市场间的联系仍较弱;SPOT-DF市场在面临大的正冲击和负冲击时均表现出较强的联动性,而SPOT-NDF市场和DF-NDF市场在面临大的冲击时汇率变动表现出"不对称效应":在面临大的正冲击(人民币相对贬值)时,境内汇率市场和境外NDF市场汇率变动不同步,当面临大的负冲击(人民币相对升值)时,境内汇率市场和境外NDF市场汇率变动表现出较强的同步性。本文进一步分析了上述"不对称效应"的经济机理,探讨了其经济学含义。     

Price reversals versus price continuations: the transitory price effects of futures trading extension on the underlying stock market

This paper examines the transitory price effects of index futures trading extension on the underlying stock market. Based on the model formulation of George and Hwang (1995) and Amihud and Mendelson (1987) and using the Hong Kong data, we find that the extension of futures trading hour helps to reduce the opening pricing errors and change the correlations between daytime and overnight stock returns. Our finding adds to the literature that the trading behavior of derivatives has a significant influence on the transitory price changes of the underlying cash products.

Forecasting stock-market tail risk and connectedness in advanced economies over a century: The role of gold-to-silver and gold-to-platinum price ratios

We examine the predictive value of risk perceptions as measured in terms of the gold-to-silver and gold-to-platinum price ratios for stock-market tail risks and their connectedness in eight major industrialized economies using monthly data for the period 1916:02–2020:10 and 1968:01–2020:10, where we use four variants of the popular Conditional Autoregressive Value at Risk (CAViaR) framework to estimate the tail risks for both 1% and 5% VaRs. Our findings for the short sample period show that the gold-to-silver price ratio resembles the gold-to-platinum price ratios in that it is a useful proxy for global risk. Our findings for the long sample period show, despite some heterogeneity across economies, that the gold-to-silver price ratio often helps to out-of-sample forecast for both 1% and 5% stock market tail risks, particularly when a forecaster suffers a higher loss from underestimation of tail risks than from a corresponding overestimation of the same absolute size. We also find that using the gold-to-silver price ratio for forecasting the total connectedness of stock markets is beneficial for an investor who suffers a higher loss from an underestimation of total connectedness (i.e., an investor who otherwise would overestimate the benefits from portfolio diversification) than from a comparable overestimation.     

The dual contributions of information instruments in return models: magnitude and direction predictability

By partitioning asset return prediction errors, we show explicitly the dual role of magnitude and sign prediction of return instruments. We demonstrate analytically that sign prediction directly affects heteroskedasticity in asset returns; increases in precision attenuate the heteroskedasticity. Our findings with monthly asset returns are consistent with earlier evidence and indicate that our proposed analytical model captures the sign predictive component of returns. Our results are supportive of a nonlinear return generating model that can be thought of as the product of a model, perhaps linear, for forecasting return signs and a model for forecasting return magnitudes.     

Properties of foreign exchange risk premiums

We study the properties of foreign exchange risk premiums that can explain the forward bias puzzle, defined as the tendency of high-interest rate currencies to appreciate rather than depreciate. These risk premiums arise endogenously from the no-arbitrage condition relating the term structure of interest rates and exchange rates. Estimating affine (multi-currency) term structure models reveals a noticeable tradeoff between matching depreciation rates and accuracy in pricing bonds. Risk premiums implied by our global affine model generate unbiased predictions for currency excess returns and are closely related to global risk aversion, the business cycle, and traditional exchange rate fundamentals.     

The scapegoat theory of exchange rates: the first tests

The scapegoat theory of exchange rates (Bacchetta and van Wincoop, 2004, Bacchetta and van Wincoop, 2013) suggests that market participants may attach excessive weight to individual economic fundamentals, which are picked as “scapegoats” to rationalize observed currency fluctuations at times when exchange rates are driven by unobservable shocks. Using novel survey data that directly measure foreign exchange scapegoats for 12 exchange rates, we find empirical evidence that supports the scapegoat theory. The resulting models explain a large fraction of the variation and directional changes in exchange rates in sample, although their out-of-sample forecasting performance is mixed.     

The immediate price impact of trades on the Australian Stock Exchange

We study the immediate price impact of a single trade executed in the Australian Stock Exchange (ASX). By ordering the top 300 stocks on the ASX in order of their free float market capitalization, a clear pattern emerges, with higher cap stocks experiencing lower price impact than lower cap stocks for the same traded volume. We investigate this relationship in detail, and show that the price impact and liquidity scale as a power of the market capitalization. This relationship is used to obtain a single market impact curve which shows average price shift as a function of volume traded. We obtain similar results for every year from 2001 to 2004.