Speculative Attacks Research Articles

Can Bitcoin trigger speculative pressures on the US Dollar? A novel ARIMA-EGARCH-Wavelet Neural Networks

In recent years, Bitcoin has garnered attention as a digital currency, prompting increasing debate regarding its effects on traditional financial markets, particularly the US dollar. This study investigates the relationship between Bitcoin and the US dollar, especially in the contexts of speculative attacks, where investors attempt to devalue a currency, and short squeezes, where rapid price rises force short sellers to quickly buy back assets to avoid further losses. The study employs a novel hybrid model combining an autoregressive moving average, Generalized Autoregressive Conditional Heteroskedasticity, and Wavelet Neural Networks techniques with neural networks approaches. The results suggest that significant trading activity in Bitcoin/US dollar, particularly during speculative attacks and short squeezes, can substantially impact the US dollar/EUR market, increasing price volatility as traders adjust their strategies. These adjustments, along with risk management strategies, drive higher trading volumes and further volatility. Our findings demonstrate that our novel hybrid model combined with Quantum Recurrent Neural Networks provides the most accurate predictions, offering valuable insights to inform trading strategies in both Bitcoin/US dollar and US dollar/EUR markets. This study has important implications for policymakers and market participants, emphasising the need to understand the relationship between Bitcoin and the US dollar for financial stability and effective policy formulation. It also highlights the necessity of advanced modeling techniques to accurately predict cryptocurrency market behavior.

Investigating the feasibility of the double-play strategy: evidence from Hong Kong’s speculative attacks

ABSTRACT This paper examines the ‘double-play’ strategy adopted by speculators during currency attacks in Hong Kong. We modify the speculators’ objective function to interpret how they earned potential returns from the automatic mechanism of the linked exchange rate system. Furthermore, we investigate the dynamic conditional correlations among currency, stock and derivatives markets, and identify the key determinants for these relationships during speculative attacks. Our findings offer a comprehensive insight into the behaviour of speculators during currency attacks, and provide compelling empirical support for the impact of central bank interventions on market dynamics.

SecurityCloak: Protection against cache timing and speculative memory access attacks

Microarchitectural innovations such as deep cache hierarchies, out-of-order execution, branch prediction and speculative execution in modern processors have made possible to meet ever-increasing demands for performance. However, these innovations have inadvertently introduced vulnerabilities that are exploited by cache-side channel attacks such as Flush & Reload, Prime & Probe, Evict & Time, and attacks such as Spectre and Meltdown that exploit speculative executions. These attacks can potentially leak information which should be secured.Mitigating the attacks while preserving the performance of out-of-order execution has been a challenge. Previous hardware mitigation techniques against cache timing or side-channel attacks include complex cache indexing mechanisms, encrypting addresses, partitioning cache memories, assigning specific ways of a set for each process, or obfuscating cache accesses by using ghost threads. Previous techniques for preventing or at least mitigating attacks based on speculative executions include hiding speculative data accesses using separate buffers or caches, or undoing the effects of speculation throughout program execution. Most techniques address either attacks that exploit speculation such as Spectre or cache side-channel attacks but not both. In many cases, changes to the microarchitecture with additional hardware are needed to implement the security protection. In some cases the mitigations cause performance penalties. In contrast we present very simple designs aimed at preventing both timing based cache side-channel attacks and Spectre style attacks based on speculative executions. Our approach combines obfuscation of cache timing making it more difficult for side-channel attacks to succeed and delaying speculative data accesses that miss in cache until the speculation is resolved. We will show that these approaches prevent both timing attacks such as Flush & Reload, Prime & Probe, Evict & Time as well as speculative attacks such as Spectre. Our technique requires very minimal changes to hardware.

Political stability and credibility of currency board

Do currency boards offer protection against self-fulling speculative attacks? This paper examines the credibility of currency boards of Argentina, Bulgaria, Estonia, Hong Kong, Latvia, and Lithuania. We employ a Bayesian Markov regime-switching model to analyze the role of economic fundamentals and self-fulfilling expectations in accounting for the credibility of the currency board. We find that the credibility of our sample currency boards is all subject to self-fulfilling runs. We also find that the political stability of adopting economies relates to the credibility of their currency boards and explains a few self-fulfilling runs of their currency boards.

Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience

The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the implementation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes.

CURRENCY CRISES AND CONTAGION CHANNELS IN ASIAN ECONOMIES

This study examines multiple transmission mechanisms that propagate and amplify shocks across Asian nations owing to financial turbulence with emphasis on global shock transmission between economies that prioritise ‘trade’ and ‘financial’ connections in four countries: Indonesia, Korea, Malaysia, and the Philippines. Based on the logit estimation outcomes, a higher degree of trade openness amplifies the implications of shocks on the economy. Relevant implications are drawn for optimal regional monitoring and the coordination of integration as the economic fundamentals associated with the currency crises complements the first-generation models of speculative attacks.

DEEP NEURAL NETWORKS METHODS FOR ESTIMATING MARKET MICROSTRUCTURE AND SPECULATIVE ATTACKS MODELS: THE CASE OF GOVERNMENT BOND MARKET

A sovereign bond market offers a wide range of opportunities for public and private sector financing and has drawn the interest of both scholars and professionals as they are the main instrument of most fixed-income asset markets. Numerous works have studied the behavior of sovereign bonds at the microeconomic level, given that a domestic securities market can enhance overall financial stability and improve financial market intermediation. Nevertheless, they do not deepen methods that identify liquidity risks in bond markets. This study introduces a new model for predicting unexpected situations of speculative attacks in the government bond market, applying methods of deep learning neural networks, which proactively identify and quantify financial market risks. Our approach has a strong impact in anticipating possible speculative actions against the sovereign bond market and liquidity risks, so the aspect of the potential effect on the systemic risk is of high importance.

Volatility spill over effect of cryptocurrency prices and foreign exchange in Nigeria

The study analyzes the volatility spillover effects of cryptocurrencies and foreign exchange market in Nigeria, covering a two-year period from September 19th, 2019, to September 19th, 2021. It captures a period where the domestic and foreign economy experienced a series of challenges, reflecting on its financial markets and cryptocurrency. The study adopts the Vector Autoregressive - Multivariate Generalized Conditional Heteroskedastic methodological framework, with the Baba, Engle, Kraft, and Kroner transformation (VAR-MGARCH-BEKK), to determine the volatility spillover effect between Nigeria’s Foreign exchange returns and the price returns of four of the largest cryptocurrencies traded in Nigeria. Findings indicate foreign exchange have positive effect on the mean spillovers on cryptocurrencies, and an overall market influence over cryptocurrencies, due to a high GARCH and low ARCH estimate. However, the ARCH parameters show that past errors of foreign exchange market are observed to be vulnerable to external volatilities. Therefore, the study is able to conclude that cryptocurrencies serve as a viable hedging, safe haven and an effective diversification instrument against financial uncertainties, and therefore, recommends optimal diversification strategies and low leverage contracts to avoid the high risks cryptocurrencies present, as they are highly volatile, hence, susceptible to speculative attacks.

STOCK MARKET VOLATILITY AND THE SPREAD OF SPECULATIVE ATTACKS: EVIDENCE FROM WAVELET ANALYSIS

This paper investigates the effects of speculative-led attacks on volatility spillover through selected 20 stock markets over the period of January 03, 2013 and March 14, 2021, implementing a recently developed wavelet spectrum analysis. To assess the degree of speculation, the time scale of the study is divided into two parts as the pre-COVID-19 era and the COVID-19 pandemic. In addition, the potential effects of speculative-led attacks on stock markets are analyzed both through the income-based and region-based classifications. In that vein, the empirical results are built upon three parts. First, the volatility spillover is more common for the high-income economies than the rest of the other economies and it is much visible at the COVID-19 pandemic. Second, the regional differences of stock markets have also a crucial impact on the behaviors of financial investors in which the speculative-led attacks are intensified in the regions where the financial transactions are relatively much higher. Finally, the core reason behind the increase of speculative attacks in those stock markets is an ongoing stagnation in productive activities during the COVID-19 pandemic.

The peso problem and dollar hegemony under inflation targeting

This article examines the influence of US monetary policy on Mexico’s exchange rate (peso/dollar) and monetary policy. It shows that the recent reduced volatility of Mexico’s exchange rate is a consequence of defensive policies undertaken by Mexico’s central bank to avoid sudden capital reversals and speculative attacks, usually associated with destabilizing speculative behavior. To test that hypothesis, the paper examines the effect of the accumulation of international reserves and exchange-rate variations on the Mexico–US interest-rate gap. The authors’ findings confirm that international reserves permit the central bank to maneuver the exchange rate and its inflation target. Furthermore, the paper provides an estimated Taylor rule for Mexico, including the US interest rate. The estimation reveals that Mexico’s monetary policy is not independent of US monetary policy. Mexico faces a liquidity trap at a higher interest rate than the United States. Whereas the United States faces a trap at the zero lower bound, Mexico encounters monetary policy ineffectiveness at an interest rate of 3.5 percent.

A novel cache based on dynamic mapping against speculative execution attacks

The Spectre attacks exploit the speculative execution vulnerabilities to exfiltrate private information by building a leakage channel. Creation of a leakage channel is the basic element for spectre attacks, among which the cache-tag side channel is considered to be the most serious one. To block the leakage channels, a novel cache applies Dynamic Mapping technology, named DmCache, is presented in this paper. DmCache applies a dynamic mapping mechanism to temporarily store all the cache lines polluted by speculative execution and keep invisible when accessing. Then it monitors the head of the reorder buffer to determine which polluted cache line can become visible. In this paper, we demonstrated that Spectre attacks exerted no impact on a processor system equipped with DmCache based on the analysis of the processor’s circuit behaviour, which equipped with the DmCache and under the Spectre attack.

Speculative attacks and investor attention

AbstractThis paper presents the first evidence that the investor attention can be used to forecast currency prices in a speculative attack. Investigating the attacks that affected several emerging economies in the second semester of 2018, I document a strong influence of investor attention on the price and risk of the currency under attack, and this influence monotonically rises with the increase of the attacks’ severity. Moreover, this association is absent outside of the attacks or for those emerging currencies that did not experience an attack in the sample period. These findings are robust to several alternative explanations and provide further support for the importance of retail investors to asset pricing.

Discussion of “Currency Stability Using Blockchain Technology”

The volatility of crypto currencies hinders their ability to be media of exchange or stores of value, leading to the implementation of exchange-rate pegs in an attempt to stabilize these currencies. This strategy has been used by crypto currencies such as US Dollar Tether, Steem Backed Dollar and TrueUSD; and was previously adopted in countries such as Brazil, Mexico and Argentina. However, an exchange-rate peg is vulnerable to speculative attacks if it is not 100% backed by reserves, as discussed in Obstfeld (1996). Using insights from the bank-run literature, Routledge and Zetlin-Jones (2018) build on Green and Lin (2003) and propose a model of speculative attacks. They show that adjustments to the exchange rate can prevent speculative attacks in equilibrium. They also show how to implement such contracts using blockchain technology. In this discussion paper, I provide a cautionary tale. I show also in a version of Green and Lin (2003) that the information content in the blockchain prevents agents from attaining all the gains from risk sharing—highlighting the downsides of too much public information.

Currency stability using blockchain technology

To date, cryptocurrency prices are volatile and many cryptocurrency developers have adopted ad hoc approaches to stabilize their cryptocurrency price. When these currencies are not 100% backed by other valued assets, part of their price volatility may arise from self-fulfilling expectations of a speculative attack (as in Obstfeld (1996)). We show that an exchange rate policy, which is less than 100% backed and dynamically adjusts in response to traders’ conversion demand eliminates speculative attacks while, under some conditions, preserving much of the desired exchange rate stability. This dynamic exchange rate policy admits a great deal of discretion to and requires commitment by the party implementing the policy. We demonstrate how to implement this policy using the Ethereum network—a smart contract blockchain environment—and how this implementation yields commitment to the policy.

Foreign Exchange Intervention Rules for Central Banks

This paper presents a rule for foreign exchange interventions (FXI), designed to preserve financial stability in floating exchange rate arrangements. The FXI rule addresses a market failure: the absence of hedging solution for tail exchange rate risk in the market (i.e. high volatility). Market impairment or overshoot of exchange rate between two equilibria could generate high volatility and threaten financial stability due to unhedged exposure to exchange rate risk in the economy. The rule uses the concept of Value at Risk (VaR) to define FXI triggers. While it provides to the market a hedge against tail risk, the rule allows the exchange rate to smoothly adjust to new equilibria. In addition, the rule is budget neutral over the medium term, encourages a prudent risk management in the market, and is more resilient to speculative attacks than other rules, such as fixed-volatility rules. The empirical methodology is backtested on Banco Mexico’s FXIs data between 2008 and 2016.

Neural Networks for Estimating Speculative Attacks Models.

Currency crises have been analyzed and modeled over the last few decades. These currency crises develop mainly due to a balance of payments crisis, and in many cases, these crises lead to speculative attacks against the price of the currency. Despite the popularity of these models, they are currently shown as models with low estimation precision. In the present study, estimates are made with first- and second-generation speculative attack models using neural network methods. The results conclude that the Quantum-Inspired Neural Network and Deep Neural Decision Trees methodologies are shown to be the most accurate, with results around 90% accuracy. These results exceed the estimates made with Ordinary Least Squares, the usual estimation method for speculative attack models. In addition, the time required for the estimation is less for neural network methods than for Ordinary Least Squares. These results can be of great importance for public and financial institutions when anticipating speculative pressures on currencies that are in price crisis in the markets.

Foreign Exchange Intervention Rules for Central Banks: A Risk-Based Framework

This paper presents a rule for foreign exchange interventions (FXI), designed to preserve financial stability in floating exchange rate arrangements. The FXI rule addresses a market failure: the absence of hedging solution for tail exchange rate risk in the market (i.e. high volatility). Market impairment or overshoot of exchange rate between two equilibria could generate high volatility and threaten financial stability due to unhedged exposure to exchange rate risk in the economy. The rule uses the concept of Value at Risk (VaR) to define FXI triggers. While it provides to the market a hedge against tail risk, the rule allows the exchange rate to smoothly adjust to new equilibria. In addition, the rule is budget neutral over the medium term, encourages a prudent risk management in the market, and is more resilient to speculative attacks than other rules, such as fixed-volatility rules. The empirical methodology is backtested on Banco Mexico's FXIs data between 2008 and 2016.

Twin Crises: A Comparative Analysis of the Global Financial Crisis of 2007-09 and the Asian Financial Crisis of 1997-99

This paper presents a comparative analysis of the key causes and effects of The Asian Financial Crisis (AFC) of 1997-99 and the Global Financial Crisis (GFC) of 2007-09. Drawing on existing financial crisis scholarship, the author finds that weak institutions, speculative attacks and political corruption were accelerants of the AFC and GFC, but not root causes. The author finds evidence that, as with the Global Financial Crisis, the most important factors were (a) the rapid accumulation of credit and an asset price boom in the lead-up to a banking crisis, (b) a consequent sharp deterioration of asset values through a leverage downswing, (c) the impact of a leverage downswing on securities markets and financial institutions at systemically important junctures within under- and unregulated frontiers, and (d) credit and liquidity crunches that froze capital allocation. These patterns offer lessons about common themes across financial crises, and recommend several areas that may be the subject of continued research.

Government size and speculative attacks on public debt

In this paper I set up a primary surplus target zone model—differently from some previous papers that used instead an interest rate target zone model— both to interpret the public debt euro area crisis and, more importantly, to draw some more general conclusions as to the role of the primary surplus in preventing speculative attacks against public debt. This primary surplus target zone model also allows considering the effects resulting from either a small or a big government. If a small government determines a higher GDP growth than a big one, then a higher steady state public debt-to-GDP level can be obtained in the first case. However, this paper shows that in the case of a credible fiscal upper target it also turns out that such a difference does not affect the size of the expectation effect determining the ‘honeymoon’. Moreover, when the primary surplus reaches its upper boundary—something that will occur inevitably, irrespective of the size of the government determining the primary surplus— monetary policy and the presence of a lender of last resort remain essential to stabilize the public debt and avoid speculative attacks.

The evolution of European economic institutions during the COVID-19 crisis.

This article argues that the incomplete economic and institutional integration of the euro area has exposed the monetary union to increasing economic divergence, which could be deepened by the COVID‐19 crisis. We discuss how monetary and fiscal measures implemented at the onset of the pandemic have contributed to mitigate the economic consequences of lockdowns, but provided limited insurance to narrow economic gaps across member countries. However, EU countries agreed on July 21, 2020 to develop, for the first time, countercyclical fiscal transfers financed by common debt issuance. We discuss the potential of this instrument to contribute to improve the resilience of the eurozone.