อาคาร 2 ชั้น 9 ธปท.
Foreign Exchange Order Flows and the Thai Exchange Rate Dynamics
Applying the microstructure approach to exchange rates, this paper aims to shed light on the price formation process in the Thai foreign exchange market using a unique supervisory dataset of daily foreign exchange transactions from all licensed dealers in Thailand. We examine the main drivers of different types of order flows and the effect of resident and non-resident customer order flows on the Thai exchange rate. The results suggest that non-resident order flows have an important influence on movements in the Thai baht, while resident order flows do not. Regarding investors’ trading behavior, we find that non-resident order flows are driven by both fundamentals and movements of the Thai baht. Specifically, non-resident players appear to be ‘trend-followers’ with regard to exchange rate returns, exerting buying pressure when the baht recently appreciated. In contrast, domestic players tend to behave as ‘contrarians’, by buying the Thai baht after it depreciates.
Extrapolative Beliefs and Exchange Rate Markets
Following Engel (2016) and Valchev (2015), this paper documents the relationship between interest rate differentials and differential returns on domestic and foreign bonds over time horizon using a broader data sample. I find that countries with higher contemporaneous interest rates earn excess positive bond returns initially in accordance with previous UIP literature. However, the sign of excess returns reverses in the medium run. Higher contemporaneous interest rates predict negative excess returns. Eventually, interest differentials have no excess return predictability. I argue that behavioral bubbles are natural and successful candidates in generating exchange rate dynamics observed in the data. In particular, I propose that investors rely not only on fundamentals (interest differentials) but also extrapolate past exchange rates when forming expectations. The proposed extrapolative model is consistent with both excess return patterns and survey evidence in the data.
A Microscopic View of Thailand’s Foreign Exchange Market: Players, Activities, and Networks
This paper explores Thailand’s foreign exchange (FX) market landscape by utilizing the Bank of Thailand’s supervisory Financial Market Statistics (FMST) data which covers the universe of onshore foreign exchange transactions in Thailand. Historical developments regarding different groups of market players and the use of foreign exchange instruments, as well as the overall market structure are documented. Through the lens of network analysis, we also provide topological descriptions of Thailand’s FX market landscape, with applications on interbank network stability. We observe low degree of concentration among the dealer banks in terms of market turnover share. In contrast, from a customer’s perspective, market share is highly concentrated within a handful of large FX customers. The network connectivity among different groups of players suggests that the Thai FX market is one that is rather segmented and clustered among similar players. A substantial degree of specialization is evident across banks in terms of FX instruments and market segments, both in the interbank network and in the retail market. Probing into the interbank network stability, we find a small subset of banks to be truly central to the FX market network, though the system appears to hold up well in stress times supported by fluidity among interbank players.
FX Hedging Behavior among Thai Exporters: A Micro-level Evidence
Over the past 20 years, Thailand’s FX hedging market has evolved to accommodate demands from rising trade and investment activities. Notwithstanding the growth in the use of FX derivative instruments for investment risk management by outward investment funds and non-residents, FX hedging demand from merchandise trade remains a significant part of the market. This paper utilizes a transactional database that disaggregates exporters according to their firm-level characteristics in order to explain their hedging behavior over periods of exchange rate fluctuation. FX hedging by exporters is found to be sensitive to the movement in exchange rate and past hedging experience. These sensitivities give rise to periods of panic or complacency. The effects also vary across exporters with different sizes.
Currency Wars: Who Gains from the Battle?
We study the growth effects of currency undervaluation when countries employ active exchange rate management policies or impose capital controls, using a panel dataset of 185 countries. Applying two-stage regressions, we find that changes in undervaluation driven by exchange rate management and capital control policies have no significant impact on economic growth. Undervaluation that leads to higher growth mainly stems from policies that lower government consumption, reduce inflation and increase domestic savings. However, these policies are good for growth by themselves, with only limited additional growth effects through increased currency undervaluation. In sum, we find no evidence that battling in the currency depreciation war significantly increases a country’s growth rate.
Stability of Thai Baht: Tales from the Tails
We demonstrate how the EVT-based signalling approach for currency crises can be applied to an individual country with a small sample size. Using Thai historical data, first, we study the tail characteristics of the distributions of two Thai baht instability measures and 21 economic fundamentals. Then, we test asymptotic dependence between the currency instability measures and lagged economic fundamentals. Empirically, we find that the distributions of both currency instability measures and economic variables are heavy tailed. Assuming a normal distribution for the variables tends to underestimate the probability of extreme events. Furthermore, most of the economic variables which are usually used as signalling indicators for currency crises are asymptotically independent of the currency instability measures. Signals issued by these variables are thus not reliable. Nevertheless, the non-parametric EVT approach facilitates the selection of economic indicators with credible signals and high crisis prediction success.