Seasonality in foreign exchange volatility

The paper reports further empirical evidence on seasonality in foreign exchange volatility using high-frequency data. Using a basis of the signal plus noise framework, the approach decomposes tick-by-tick Reuters FXFX quotes into a random walk and a stationary component, termed the efficient price and the pricing error, respectively. The efficient price is not directly observable and is an approximation of the ‘true’ value. The pricing error captures the deviation between the observed indicative quote and the efficient price. Under the proposed model, daily and intraday volatilities of the efficient price are estimated. A pronounced pattern of volatility is uncovered and appears related to the daily activity cycle of major organized stock exchanges. It is argued that seasonality in volatility is a symptom of foreign exchange markets. Results confirm Andersen and Bollerslev's findings that significant seasonal effects are one important determinant of overall volatility at high frequencies.     

Modelling of scale change, periodicity and conditional heteroskedasticity in return volatility

This paper extends the GARCH model to a wide class of nonstationary processes by proposing a semiparametric GARCH model for simultaneous modelling of conditional heteroskedasticity, slow scale change and periodicity in the volatility of high-frequency financial returns. A data-driven algorithm is developed for estimating the model. An approximate significance test of daily periodicity and the use of Monte Carlo confidence bounds for the scale function are proposed. The practical performance of the proposal is investigated in detail using some German stock price returns. It is shown that the various volatility components are all significant. Asymptotic properties of the proposed estimators are investigated.     

The continuous time random walk formalism in financial markets

We adapt continuous time random walk (CTRW) formalism to describe asset price evolution and discuss some of the problems that can be treated using this approach. We basically focus on two aspects: (i) the derivation of the price distribution from high-frequency data, and (ii) the inverse problem, obtaining information on the market microstructure as reflected by high-frequency data knowing only the daily volatility. We apply the formalism to financial data to show that the CTRW offers alternative tools to deal with several complex issues of financial markets.     

A new wavelet-based ultra-high-frequency analysis of triangular currency arbitrage

We develop a new framework to characterize the dynamics of triangular (three-point) arbitrage in electronic foreign exchange markets. To examine the properties of arbitrage, we propose a wavelet-based regression approach that is robust to estimation errors, measurement bias and persistence. Relying on this wavelet-based (denoising) inference, we consider various liquidity and market risk indicators to predict arbitrage in a unique ultra-high-frequency exchange rate data set. We find strong empirical evidence that limit order book, realized volatility and cross-correlations help forecast triangular arbitrage profits. The estimates are statistically significant and relevant for investors such that on average 80−100 arbitrage opportunities exist with a short duration (100−500 ms) on a daily basis. Our analysis also reveals that triangular arbitrage opportunities are counter-cyclical at ultra-high-frequency levels: arbitrage returns tend to increase (decrease) in periods when volatility risk and correlations are relatively low (high). We show that liquidity-driven microstructure measures, however, appear to be more powerful in exploiting arbitrage profits when compared to market-driven factors.     

AGGREGATION,HETEROGENEOUS AUTOREGRESSION AND VOLATILITY OF DAILY INTERNATIONAL TOURIST ARRIVALS AND EXCHANGE RATES*

Tourism is a major source of service receipts for many countries, including Taiwan. The two leading tourism countries for Taiwan are Japan and the USA, which are sources of short‐ and long‐haul tourism, respectively. As a strong domestic currency can have adverse effects on international tourist arrivals through the price effect, daily data from 1 January 1990 to 31 December 2008 are used to model the world price, exchange rates, and tourist arrivals from the world, the USA and Japan to Taiwan, and their associated volatility. Inclusion of the exchange rate and its volatility captures approximate daily and weekly price and price volatility effects on world, US and Japanese tourist arrivals to Taiwan. The heterogeneous autoregressive model is used to approximate the slowly decaying correlations associated with the long‐memory properties in daily and weekly exchange rates and international tourist arrivals, to test whether alternative short‐ and long‐run estimates of conditional volatility are sensitive to the long‐memory in the conditional mean, to examine asymmetry and leverage in volatility, and to examine the effects of temporal and spatial aggregation. The approximate price and price volatility effects tend to be different, with the exchange rate typically having the expected negative impact on tourist arrivals to Taiwan, whereas exchange rate volatility can have positive or negative effects on tourist arrivals to Taiwan. For policy purposes, the empirical results suggest that an arbitrary choice of data frequency or spatial aggregation will not lead to robust findings as they are generally not independent of the level of aggregation used.     

Modeling the Taiwan Stock Market and International Linkages

This paper analyzes the Taiwan stock market and examines its price and volatility linkages with those of the United States. In particular, it tests the hypothesis that the short-term volatility and price changes spill over from the developed markets, mainly the United States, to the emerging Taiwan stock market. The model and the test are built upon Engle's ARCH (autoregressive conditional heteroskedasticity) and Engle and Kroner's M-GARCH (multivariate generalized ARCH) models. The paper differs from previous studies on the Taiwan stock market in three respects. First, instead of using daily closing prices, it uses close-to-open and open-to-close returns to avoid the problem of overlapping samples. It carefully models the day-of-the-week effect in daily data to avoid misspecification of the model. Second, to circumvent the generated regressor problem arising from the two-step estimation procedure, it also employs the M-GARCH model where all parameters are estimated simultaneously. Third, the misspecification test is carried out on various kinds of asymmetric ARCH factors. A substantial volatility spillover effect is found from the US stock market to the Taiwan stock market, especially for the model using close-to-open returns. There is also evidence supporting a spillover effect in price changes. The findings can be explained by the recent gradual opening of the Taiwan stock market to foreign investors.     

Ad-Hoc Black–Scholes vis-à-vis TSRV-based Black–Scholes: Evidence from Indian Options Market

This research paper examines one-day-ahead out-of-sample performance of the volatility smirk-based options pricing models, namely, Ad-Hoc-Black–Scholes (AHBS) models on the CNX Nifty index options of India. Further, we compare the performance of these models with that of a TSRV-based Black–Scholes (BS) model. For the purpose, the study uses tick-by-tick data. The results on the AHBS models are highly satisfactory and robust across all the subgroups considered in the study. Notably, a daily constant implied volatility based ad-hoc approach outperforms the TSRV-based BS model substantially. The performance of the ad-hoc approaches improves further when the smile/smirk effect is considered. For the estimation of the implied volatility smile, we apply three weighting schemes based on the Vega and liquidity of the options. All the schemes offer equally competing results. The major contribution of the study to the existing literature on options pricing is in terms of the ex-ante examination of the ad-hoc approaches to price the options by calibrating volatility smile/smirk on a daily basis.

     

S&P500 volatility analysis using high-frequency multipower variation volatility proxies

The availability of ultra-high-frequency data has sparked enormous parametric and nonparametric volatility estimators in financial time series analysis. However, some high-frequency volatility estimators are suffering from biasness issues due to the abrupt jumps and microstructure effect that often observed in nowadays global financial markets. Hence, we motivate our studies with two long-memory time series models using various high-frequency multipower variation volatility proxies. The forecast evaluations are illustrated using the S&P500 data over the period from year 2008 to 2013. Our empirical studies found that higher-power variation volatility proxies provide better in-sample and out-of-sample performances as compared to the widely used realized volatility and fractionally integrated ARCH models. Finally, these empirical findings are used to estimate the one-day-ahead value-at-risk of S&P500.     

Foreign equity option pricing under stochastic volatility model with double jumps

This paper considers the challenging problem advocated by Huang and Hung (2005), that is to incorporate the stochastic volatility into the foreign equity option pricing. Foreign equity options (quanto options) are contingent claims where the payoff is determined by an equity in one currency but the actual payoff is done in another currency. Huang and Hung (2005) priced foreign equity options under the Lévy processes. In Huang and Hung's paper, they considered jumps in the foreign asset prices and exchange rates and assumed the volatility as constant. However, many studies showed that constant volatility and jumps in returns are incapable of fully capturing the empirical features of equity returns or option prices. In this paper, the stochastic volatility with simultaneous jumps in prices and volatility is proposed to model foreign asset prices and exchange rates. The foreign equity option pricing formula is given by using the Fourier inverse transformation. The numerical results show that the use of stochastic volatility with simultaneous jumps in prices and volatility proposed to model foreign asset prices and exchange rates is necessary and this approach can help us to capture more accurately the foreign equity option prices.     

中国股票市场波动率的高频估计与特性分析

本文旨在应用高频数据估计中国股市的已实现波动率。我们发现股票指数与个股的高频交易数据中的微观摩擦影响正好相反 ,使用极高频的数据会大大增加个股的波动率估计值 ,相反却会大大降低指数的波动率估计值。在计算各种频率的已实现波动率的基础上 ,本文构造了一种较为精确的估计波动率的方法 ,可以更好地平衡测量误差与微观结构误差。基于已实现波动率 ,本文研究了中国股市波动率不对称性和长期记忆特性     

Microstructure Noise, Realized Variance, and Optimal Sampling

A recent and extensive literature has pioneered the summing of squared observed intra-daily returns, "realized variance", to estimate the daily integrated variance of financial asset prices, a traditional object of economic interest. We show that, in the presence of market microstructure noise, realized variance does not identify the daily integrated variance of the frictionless equilibrium price. However, we demonstrate that the noise-induced bias at very high sampling frequencies can be appropriately traded off with the variance reduction obtained by high-frequency sampling and derive a mean-squared-error (MSE) optimal sampling theory for the purpose of integrated variance estimation. We show how our theory naturally leads to an identification procedure, which allows us to recover the moments of the unobserved noise; this procedure may be useful in other applications. Finally, using the profits obtained by option traders on the basis of alternative variance forecasts as our economic metric, we find that explicit optimization of realized variance's finite sample MSE properties results in accurate forecasts and considerable economic gains.     

The information content of the term structure of interest rates

The profitability of chartist trading rules on foreign exchange markets is still under debate. Since simple technical trading rules may not adequately capture the complex phenomenon of chartist trading, this study focuses on the prominent head-and-shoulder pattern as a representative trading rule which incorporates various ‘technical’ ideas such as smoothed trends, trend reversal, resistance levels, and volatility clustering. For various combinations of the building blocks of head-and-shoulder definitions the result is generally negative: Returns to head-and-shoulder trading rules are not significantly positive - and if there is any evidence for non-zero returns at all, then it is evidence for negative returns.     

Modeling the volatility of futures return in rubber and oil—A Copula-based GARCH model approach

This paper attempts to make use of a Copula-based GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) Model to find out the relationships between the volatility of rubber futures returns in the Agricultural Futures Exchange of Thailand (AFET) and other four main markets, namely, the volatility of rubber futures returns in the Singapore Commodity Exchange (SICOM), the volatility of rubber futures returns, crude oil returns, and gas oil returns in the Tokyo Commodity Exchange (TOCOM). The results illustrate that the Student-t dependence only shows better explanatory power than the Gaussian dependence structure and the persistence pertaining to the dependence structure between rubber futures returns in AFET and oil futures returns, namely, crude oil futures returns and gas oil futures returns in TOCOM. Whereas, the Gaussian dependence shows better explanatory ability between rubber futures returns in AFET and other rubber futures returns, namely, the volatility of rubber futures in SICOM and TOCOM. For the multivariate Copula model, all the parameters between AFET and other variables are significant. Based on these results, with the liberalization of agricultural trade and the withdrawal of government support to agricultural producers, there is in many countries a new need for price discovery and even physical trading mechanisms, a need that can often be met by commodity futures exchanges. Hence, this paper recommends that the government supports the hedge mutual funds that can be invested in every commodities futures exchange in the world. It can also put the funds together that will contribute farmers to invest in each commodities futures market.     

Oil Price Shocks and Volatility in Australian Stock Returns

This paper examines the effect of oil shocks on return and volatility in the sectors of Australian stock market and finds significant effects for most sectors. For the overall market index, an increase in oil price return significantly reduces return, and an increase in oil price return volatility significantly reduces volatility. An advantage of looking at sector returns rather than a general index of stock returns is that sectors may well differ markedly in how they respond to oil price shocks. The energy and material sectors (as expected) and the financial sector (surprisingly) are out of step (in different ways) with results for the other sectors and for the overall index. A rise in oil price increases returns in the energy and material sectors and an increase in oil price return volatility increases stock return volatility in the financial sector. Explanation for the negative (positive) association between oil return (oil return volatility) and returns (volatility of returns) in the financial sector must be based on the association via lending to and/or holdings of corporate bonds issued by firms with significant exposure to oil price fluctuations and their speculative positions in oil‐related instruments.     

Financial contagion and volatility spillover: An exploration into Indian commodity derivative market

This study measures the extent of financial contagion in the Indian asset markets. In specific it shows the contagion in Indian commodity derivative market vis-à-vis bond, foreign exchange, gold, and stock markets. Subsequently, directional volatility spillover among these asset markets, have been examined. Applying DCC-MGARCH method on daily return of commodity future price index and other asset markets for the period 2006–16, time varying correlation between commodity and other assets are estimated. The degree of financial contagion in commodity derivative market is found to be the largest with stock market and least with the gold market. A generalized VAR based volatility spillover estimation shows that commodity and stock markets are net transmitters of volatility while bond, foreign exchange and gold markets are the net receivers of volatility. Volatility is transmitted to commodity market only from the stock market. Such volatility spillover is found to have time varying nature, showing higher volatility spillover during the Global Financial Crisis and during the period of large rupee depreciation in 2013–14. These results have significant implication for optimal portfolio choice.     

THE DYNAMIC RELATION BETWEEN RETURNS,TRADING VOLUME,AND VOLATILITY: LESSONS FROM SPILLOVERS BETWEEN ASIA AND THE UNITED STATES

This paper investigates the dynamic relationship between index returns, return volatility, and trading volume for eight Asian markets and the US. We find cross‐border spillovers in returns to be non‐existent, spillovers in absolute returns between Asia and the US to be strong in both directions, and spillovers in volatility to run from Asia to the US. Trading volume, especially on the Asian markets, depends on shocks in domestic and foreign returns as well as on volatility, especially those shocks originating in the US. However, only weak evidence is found for trading volume influencing other variables. In the light of the theoretical models, these results suggest sequential information arrivals, with investors being overconfident and applying positive feedback strategy. Furthermore, new information causes price volatility to rise due to differences in its interpretation among traders, but the subsequent market reaction takes the form of adjustment in price level, not volatility. Lastly, the intensity of cross‐border spillovers seems to have increased following the 1997 crisis, which we interpret as evidence of increased noisiness in prices and diversity in opinions about news originating abroad. Our findings might also help to understand the nature of financial crises, to predict their further developments and consequences.     

A FURTHER EXAMINATION OF THE PRICE AND VOLATILITY IMPACT OF STOCK DIVIDENDS AT EX‐DATES*

We examine the price and volatility reaction around stock dividend ex‐dates for an Australian sample, over the period January 1992 to December 2000. We find that price reaction around stock dividend ex‐dates provides positive abnormal returns both prior, and subsequent, to the abolishment of par value of shares in July 1998. When we partitioned the sample into financial, industrial non‐financial and mining firms, the price reaction is found to be positive and significant only for industrial non‐financial companies. Volatility of daily returns for periods subsequent to ex‐dates is significantly greater than corresponding periods prior to announcement dates, while cumulative raw returns subsequent to ex‐dates are significantly lower than periods prior to announcement dates for industrial non‐financial companies. The magnitude of the price reaction is statistically significantly related to an increase in the volatility of daily returns and to a reduction in cumulative raw returns subsequent to the ex‐dates, for industrial non‐financial companies. These findings support buying pressure hypothesis suggested by Dhatt et al. (1994, 1996 ).     

CAPITAL FLOWS AND JAPANESE ASSET VOLATILITY

Characterizing asset price volatility is an important goal for financial economists. The literature has shown that variables that proxy for the information arrival process can help explain and/or forecast volatility. Unfortunately, however, obtaining good measures of volume and/or order flow is expensive or difficult in decentralized markets such as foreign exchange. We investigate the extent that Japanese capital flows, which are released weekly, reflect information arrival that improves foreign exchange and equity volatility forecasts. We find that capital flows can help explain transitory shocks to generalized autoregressive conditionally heteroskedastic volatility.     

A Reappraisal of the Border Effect on Relative Price Volatility

Engel & Rogers (1996) find that crossing the US–Canada border can considerably raise relative price volatility and that exchange rate fluctuations explain about one-third of the volatility increase. Using a decomposition method, this study re-evaluates the border effect. It is shown that cross-country heterogeneity in price volatility can induce a bias in measuring the border effect unless proper adjustment is made to correct it. We further examine the implication of symmetric sampling for the border effect estimation under the decomposition approach. Two conditions governing the strength of the border effect are identified. In particular, the more dissimilar the price shocks are across countries, the greater the border effect will be. Decomposition estimates also suggest that exchange rate fluctuations actually account for a large majority of the border effect.     

Return patterns of South Korean stocks following large price shocks

This study tests the market efficiency of the South Korean stock market by examining returns on stocks of the constituents of the KOSPI 50 from 2000 to 2014 following large 1-month price decreases and increases. An exponential GARCH (EGARCH) event study framework is used to analyse the stock returns. The results show that large price shocks, positive and negative, are likely to be followed by positive market returns. Moreover, the results show an increase in the beta of stocks in the years following a large price shock. The overall results therefore support the Uncertain Information Hypothesis. However, beginning in 2008, return patterns more closely reflect those hypothesised by the Efficient Market Hypothesis, possibly due to increased participation by international investors. The observed returns following large price increases and decreases can be partially explained by changes in the Korean won to US dollar exchange rate and the trading behaviour of foreign investors.